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Object Detection from SAR Images based on Curvelet Despeckling
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ScienceDirect Materials Today: Proceedings 11 (2019) 1102–1116
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I2CN_2018
Object Detection from SAR Images based on Curvelet Despeckling Mrs. Devi Devapal1, Mrs. Hashna N.2, Ms. Aparna V. P.3, Ms. Bhavyasree C.4, Ms. Jeena Mathai5, Ms. Sangeetha Soman K.6 Dept of Computer Science and Engineering, College of Engineering, Pathanapuram, Elikkattoor P O, Kollam, Kerala, Pin: 689696, India
Abstract SAR images are increasingly gaining importance in the field of remote sensing applications. The receiving SAR data are affected by multiplicative speckle noise which form granular patterns. Hence despeckling is done prior to object detection. Here a comparison of several transform domain techniques are carried out for despeckling SAR images. Performance parameters such as ENL, PSNR, SSIM etc. are used to evaluate the result. From the result, Curvelet is chosen as the optimum technique for despeckling. In this paper Curvelet based despeckling of SAR images is performed and further object detection is done using CA-CFAR. © 2018 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of International Multi- Conference on Computing, Communication, Electrical & Nanotechnology: Materials Science. Keywords: SAR; Speckle; CA-CFAR; ENL; PSNR; SSIM; Curvelet;
1. Introduction SAR (Synthetic Aperture Radar) is an active satellite imaging technique which produces a high resolution image of earth’s surface. SAR antenna is mounted on a moving platform such as an aircraft or spacecraft. To create a SAR image, successive pulses of radio waves are transmitted to illuminate a target scene, and the echo of each pulse is received and recorded. Airborne radar synthesizes a longer antenna. The distance the aircraft flies in synthesizing the antenna is known as the synthetic aperture.The SAR image can be captured in all weather * Corresponding author. Devi Devapal Tel.: +91-9446503136; fax: 2225959. E-mail address: [email protected] 2214-7853© 2018 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of International Multi- Conference on Computing, Communication, Electrical & Nanotechnology: Materials Science.
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Η I K Α T
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Variance Mean Noise True Image Noisy Image Scaling Factor Detection Threshold
conditions. These images are affected by a large amount of random multiplicative noise called speckle. The main goal in image despeckling is eliminating speckle noise and also preserving the details of the edges [1]. Speckle is a special phenomenon in lasers, SAR or ultrasound images. Despeckling is carried out as a preprocessing step to object detection, segmentation and classification. They are the coherent summation of the return scattered signals and the random interference of electromagnetic signals causes speckle. Multi-pass single-look and single-pass multilook techniques are also adopted for speckle reduction [2, 3]. The fundamental theory behind SAR is that it needs transmission and reception of linearly frequency modulated chirp signals, and Doppler processing of the encoded returned echo [4]. Coherent signal processing is done to attain high spatial resolution. However due to this coherent nature of signal, a grainy speckle noise occur in the image, which degrades its radiometric quality. SAR image is used in variety of application in the areas of agriculture, land use type discrimination, forestry, biomass estimation, geology, flood mapping, disaster zone mapping, oil spill detection etc. With speckle noise, the accuracies of image analysis involving classification, segmentation, texture analysis, target detection etc. are severely affected. Speckle results in dilation of fine details of image, reduce the contrast, deteriorating the shape or size of objects and blurs the edges of the image and makes interpretation of image difficult. So there is a need for an efficient despeckling method which adapts to discontinuities in the image while preserving the important geometrical features like edges and contours. Reducing noise always comes at a cost of image degradation, blurring, spatial resolution degradation and edge smearing. Apart from that, the type of noise present in the image is a critical factor in formulating the despeckling scheme. Images produced by coherent processing such as those of SAR are affected by speckle noise, which is difficult to remove. This paper discuss briefly about SAR image and the characteristics of speckle noise occurring in it. The transform domain based image despeckling techniques are compared here, with special emphasis on SAR images. An appropriate method is chosen based on performance measures such as ENL, PSNR, SSIM etc. Then the Object Detection in SAR images is done on the curvelet despeckled image which is obtained as the better despeckling scheme. In the section 2, the survey about despeckling and Object detection is done. The section 3 describes the proposed methodology. Section 4 describes the different multi-resolution schemes for despeckling SAR images. Section 5 specifies about the object detection in the despeckled SAR images and section 6 gives the result analysis. The results obtained from various despeckling scheme and the object detection from this despeckled images are discussed. Finally section 7 describes overall conclusion and future scope.
2. Literature Survey This section surveys different papers related to Despeckling and Object Detection of SAR images. Speckle noise follows a multiplicative noise model where the noise distribution is not Gaussian, with Rayleigh and Gamma being commonly used densities [5]. Despeckling can be carried out either during the image formation time or later. Despeckling techniques can be broadly classified into spatial domain techniques and transform domain techniques. The transform domain techniques make use of the transform coefficients for filtering purpose. The transform domain based filtering techniques can be classified into threshold based methods and the statistical model based methods [6]. They make use of various transforms such as wavelet, ridgelet, curvelet, contourlet, shearlet, bandlet etc for despeckling images. The spatial techniques carries out filtering using mask and estimate the local noise variance .This mask is moved pixel by pixel over the whole image and the central pixel was substituted with a mathematically processed value. These techniques are computationally less complex. They are suitable for reducing speckle in homogeneous areas but over smooth heterogeneous areas. The spatial speckle reduction techniques make use of standard filters like Lee, Kuan, Frost, Kalman etc. for speckle reduction. These methods are referred to as
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local methods since they exploit the spatial redundancy in the local neighborhood. In non-local approach [7, 8] the intensity of each pixel is determined from the whole image rather than the local neighborhood and hence called nonlocal means approach. BM3D is denoising technique algorithm that follows non-local means modeling and exploits the redundancy occurring in natural images. In object detection, single object recognition is done using set of local feature templates which uses corner detector and filters as explained in [9]. Here the authors have verified planar object recognition using SIFT features which can provide match for affine geometric alignment. Feature based object recognition technique uses bag of key points [10]. Here features are quantized into words and used for text categorization.
3. Proposed Methodology In this paper, the object detection is carried out in the despeckled SAR image in order to ensure greater accuracy. The first process is despeckling which is done after comparing the different multi-resolution techniques such as Wavelet, Contourlet and Curvelet. Based on the comparative study, Curvelet is found as the better one and hence curvelet transform is chosen to despeckle SAR images. The curvelet transform is applied to the input SAR image where they are converted to transformed coefficients. Then threshold is applied in order to remove the noise. All those noise value greater than threshold are eliminated and the inverse transformation is applied to obtain the original noise free image.
Fig 1: Flowchart of the Automatic Target Detection System
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The algorithm for the proposed methodology is Step 1: Curvelet Decomposition Perform Curvelet transformation of the input SAR image. Step 2: Threshold Computation Except for low frequency sub-band compute the GCV threshold for all sub-bands. Step 3: Image reconstruction Image is reconstructed from noise-free coefficients using inverse curvelet transform. Step 4: Object detection Define and set up a CA-CFAR detector. Compute the object detection threshold using the despeckled image. After despeckling the process of object detection is done. Then set up a cell averaged constant false alarm detection. A threshold is calculated to identify object. And the target object is detected using a hard threshold. Fig. 1 represent the flowchart of the curvelet based automatic target object detection. The GCV mentioned in the above algorithm is the Generalized Cross Validation, which is used to find the threshold is calculated using the Equation 1.
( )=
−
,
(1)
The optimal threshold is obtained here, that is the minimum value of GCV function. In the equation 1, Nd is the number of Curvelet transform coefficient in the subband and the noisy coefficient are represented as Yd .The number of coefficient below threshold which were set at zero is Nd,0.
4. Despeckling In SAR Image Noise is a random variation of brightness or color information in images. It is also known as unwanted signals, which can be multiplicative or additive. The speckle noise is random noise which is multiplicative in nature. This speckle noise is formed as a result of the random interference between the coherent returns from active imaging sensors such as LASER, SAR etc. Radar pulses are transmitted coherently and depending on the exact distance travelled, the returning wave may be in phase or out of phase. When the returning waves are in phase, the intensity of the resulting signal will be amplified resulting in constructive interference (bright spots). When the returning waves are out of phase, they tend to cancel each other reducing the intensity of the signal resulting in destructive interference (dark spots). This constructive and destructive interference of signal produces speckle noise. It appears as granular patterns in the image and make the interpretation of image difficult. Speckle occurs when object illuminated by coherent radiation have rough surface. If ‘K’ denotes the output scattering coefficient of a target, ‘I’ is its true value, and ‘η’ is the speckle noise affecting the input signal then the response is represented as in Equation 2. = ∗η
(2)
Multiplicative noise is the characteristics formed to a fully developed speckle noise as shown by [11]. Speckle model is explained by using the signal geometry for a SAR sample in Fig.2. A particular resolution cell contains the scattering from a large number of scatters with a wavelength which is comparable to the roughness of the terrain or object being imaged. Hence the response from one cell is a coherently summed signal from many scatters. Speckle appears as random placement of dark and bright spots on the image. The basic aim of denoising technique is to remove noise, irrespective of the spectral content of the noisy signal. The filter should also preserve the spatial
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Fig 2: SAR Speckle Model
variability ie, the textural information for areas with texture. The aim is to clean the image without a change in the geometric resolution. General noise removal techniques are effective for additive noise present in optical images. Since speckle is a coherent type of noise which is multiplicative, general techniques do not work effectively on such images. Multi-look technique is one of the method applied for speckle noise reduction in the SAR signal. It is done in the frequency domain during the SAR data processing in the azimuth direction [12]. The signal bandwidth is split up into smaller segments and processed in the frequency domain. The outputs are incoherently summed up to get the output having better radiometric resolution, i.e. less noise. However in the process, the spatial resolution is degraded. For ‘N’ look processing the effective spatial resolution becomes N times the original spatial resolution, while the radiometric resolution is improved by a factor of √N. In order to overcome this problem, several filtering techniques have evolved, over the last decade. Among the different multi-resolution techniques, wavelet is the first one. A wavelet is a wave like oscillation that begins at amplitude zero, increases, and then decreases back to zero. Wavelets are brief oscillations and can be combined with known portions of a damaged signal to extract information from the unknown portions. It is done using a reverse, shift, multiply and integrate technique called convolution. Wavelet can be used as a mathematical tool for information extraction from different kind of data, audio and images. Thus, sets of complementary wavelets which are reversible are useful in wavelet based compression or decompression algorithms where it is used to recover the original image information with minimal or less loss. Another multi-resolution technique is contourlets, which is a multi-resolution directional tight frame designed and it is used to efficiently approximate images made of smooth regions separated by smooth boundaries. The contourlet fast implementation is based on a Laplacian Pyramid (LP) decomposition followed by Directional Filter-Banks (DFB) applied on band-pass sub-band. This transform uses a double filter bank structure to get the smooth contours of images. The double filter bank, the LP is first used to capture the point discontinuities. Then DFB is used to form those point discontinuities into linear structures. Only one band-pass image is produced by the decomposition of LP in the multi-dimensional signal processing, which can avoid the frequency scrambling. Since the DFB will leak the low frequency signals in its directional sub-bands, it is only fit for high frequency signals. Therefore the DFB and LP are combined which is multi-scale decomposition and then it remove the low frequency. Hence, the image signals pass through the LP sub-bands to get band-pass signals and pass those signals through DFB to capture the directional information of image. This double filter banks also called as Pyramid Directional Filter Bank (PDFB) which is the structure of combination of LP and DFB, and this transform approximates the original image by using basic contour. It is also called discrete contourlet transform.
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Another image processing multi-resolution technique is based on Curvelet which is a multi-scale directional transform which allows an optimal non-adaptive representation of edges designed to represent images at different scales and different angles [13]. Here the curved singularities are approximated with some coefficients. Curvelets are non-adaptive and remain coherent waveforms under the action of the wave equation in a smooth medium. The Discrete Curvelet Transform of a continuum function ( , ) makes use of a dynamic sequence of scales, and a bank of filters ( , ∆ , ∆ , … . . )with the property that the pass-band filter ∆ is concentrated near ]. The wavelet theory describes decomposition into dyadic sub-bands[2 , 2 ]. In the frequencies [2 , 2 contrast, the sub-bands used in the Discrete Curvelet Transform is represented as in the Equation 3. ( , )=
( , )
∑
( , )
(3)
Curvelet decomposition is the sequencing process consisting of subband decomposition as shown in Equation 4. ,∆ ,∆ ,…..)
(
(4)
Equation 5 shows smooth partitioning where each subband is smoothly windowed into squares of appropriate scale. ∆f
(
∆ )
(5)
In renormalization, each resulting square is renormalized to unit scale as represented in Equation 6. (
) (
∆ )
(6)
In the ridgelet analysis each square is analyzed via the discrete ridgelet transform where the two dyadic sub-bands are merged before applying the ridgelet transform.
Fig.3: Curvelet Transform Flow Diagram. Fig. 3 illustrates the decomposition of the original image into subbands followed by the spatial partitioning of each sub-band. The ridgelet transform is then applied to each block. It introduces inter-scale orthogonality by means of sub-band filtering. Different levels of the multi-scale ridgelet pyramid are used to represent different sub-
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bands of a filter bank output and also this sub-band decomposition imposes a relationship between the width and length of the important frame elements so that they are anisotropic and obey the relation ℎ= ℎ . Therefore anisotropic features that occur in real images are better represented by Curvelets.
5. Object Detection in SAR Images The incoming radar waves are reflected by most of the man-made objects, from background which clutters the detection of these objects. Due to the bright spots induced by the interference of reflected coherent waves, the SAR image interpretation is difficult [14]. By thresholding the output of the receiver, a target is detected from the radar signals. If the output is greater than a fixed threshold, it is considered as a target presence otherwise a target absence. If the threshold is fixed too high, it is a missed detection where strong target echoes would be detected and weak echoes might be lost. The false alarm occurs if the fixed threshold is too low, even the noise alone might be able to exceed the threshold [15]. If a proper threshold is fixed along with the noise, the output would not exceed the fixed threshold. Thus the selection of a perfect fixed threshold is a compromise between false alarm and missed detection. Thus to keep a constant false alarm rate, threshold has to be varied adaptively and is achieved by using constant false alarm rate detectors. In order to achieve the targeted objective [16] more efficiently and accurately an improvement in object detection is still required [17]. In the input image the process of object detection analysis is to determine the number, location, size and position of the objects. The process of object detection is the basic concept for tracking and recognition of objects. It affects the efficiency and accuracy of object recognition. The color-based approach is the common object detection method. It detects the objects based on their color values [18]. This method is used because of its strong adaptability and robustness, hence, the detection speed needs to be improved, because it requires testing all possible windows by exhaustive search and has high computational complexity. The challenging application in the image processing is the object detection from a complex background. The goal of this project is to identify objects placed over a surface from a complex background image using various techniques. The Intersection-over-Union (IOU) means the standard performance measure that is commonly used for the object category segmentation problem [19]. The similarities between the predicted region and the ground-truth region for an object present in the given image can be found with the help of IOU measures and can be defined as the size of the intersection divided by the union of the different regions. For example, if any particular algorithm predicts each and every pixel of an image to be its background, the IOU measure is given as the intersection between the predicted and ground-truth regions which would be zero. OpenCV (Open Source Computer Vision) library implemented in python2.7 along with the help of Numpy is used for object detection. A virtual ANN (Artificial Neural Network) is created using Sci-kit tool. The Edge matching technique involves the uses of edge detection techniques to find the edges. Effect of changes in lighting and color and it also count the number of overlapping edges. The Divide and Conquer search is the next process where all positions are to be considered as a set and the lower bound is determined at best position in the cell. Next is the gray scale matching where the pixel distance is computed as a function of both pixel intensity and position. Last one is the gradient matching. In gradient matching, comparing image gradients can also be helpful in making it robust to illumination change and matching is performed like matching gray scale images. The steps include calculating the Euclidean distance between center of the circle and the connected points. First convert the image to a gray scale image, detect edges, move along edges, and draw normal which will intersect at center. And then repeat this process for entire circle or find connected edges and then calculate Euclidean distance between center of the circle and the connected points. The manual interpretation of SAR images for object detection is difficult and error prone. Automatic algorithms for the purpose of object detection [20] mostly uses Constant False Alarm Rate (CFAR) algorithm. In such detectors the clutter distribution is always locally estimated according to a given probability of false alarm. Cell-averaging CFAR (CA-CFAR) detector is the most common among the CFAR detectors. These CFAR detectors works well in situation where the speckle noise in the SAR images are relatively low. The role of the constant false alarm rate is to determine the power threshold above which any return can be considered to probably originate from a target. If this threshold is too low, then more targets will be detected with expense of increased numbers of false alarms [21]. If the threshold is too high, then fewer targets will be detected, but the number of false alarms will also be low. In most radar detectors, the threshold is set in order to achieve a required probability of false alarm.
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If the background of the targets is constant with time and space, then a fixed threshold level can be chosen that provides a specified probability of false alarm, governed by Gaussian probability density function. But in scenario like SAR imaging where the noise level changes both spatially and temporally [22], a changing threshold is needed. Here the threshold level is raised and lowered to maintain a constant probability of false alarm. The key task on detection is coming up with an appropriate threshold. In general, the threshold is a function of both the probability of detection and the probability of false alarm. In many phased array systems [23], it is desirable to have a detection threshold that maximizes the probability of detection and keeps the probability of false alarm below a preset level. In CFAR, when the detection is needed for a given cell, often termed as the Cell Under Test (CUT), the noise power is estimated from neighbouring cells. Then the detection threshold ‘ T ’ is given by the Equation 7. =
(7)
where ′ ′ is the noise power estimate and ‘ ‘ is a scaling factor called the threshold factor. From the equation 7, it is clear that the threshold adapts to the data. With the appropriate threshold factor ‘ , the resulting probability of false alarm can be kept at a constant, hence the name CFAR. The cell averaging CFAR detector is probably the most widely used CFAR detector. It is also used as a baseline comparison for other CFAR techniques. In a cell averaging CFAR detector, noise samples are extracted from both leading and training cells around the CUT. The noise estimate can be computed using Equation 8. = ∑
(8)
where ‘N’ is the number of training cells , ‘ ′ is the sample in each training cell and ‘ ‘ represents the estimated noise power. Guard cells are placed adjacent to the CUT, in both leading and lagging cells to avoid signal components from leaking into the training cell, which could adversely affect the noise estimate.
6. Results and Discussion The effectiveness of the proposed despeckling and object detection algorithm using Curvelet on real SAR images is presented in this section. Different SAR images of resolution 200x200 pixels with speckle noise are given as input to the Curvelet based despeckling algorithm for evaluating the performance. OpenCV is an open source computer vision and machine learning software library [24]. It supports real-time vision applications and these algorithms are categorized under classic algorithms, computer vision algorithms and machine learning algorithms. These algorithms are easily implemented in Java, MATLAB, Python, C, C++ etc. and are well supported by operating system like Window, Mac OS, Linux and Android. A full-featured CUDA and OpenCV interfaces are being actively developed for the betterment of technology. OpenCV is written natively in C++ and has a template interface that works seamlessly with STL containers. For OpenCV to work efficiently with python 2.7 we need to install NumPy package first. NumPy is the fundamental package for scientific computing with Python [25] and it can be treated as an extension of the Python programming language with support for multidimensional matrices and arrays. The process of despeckling is implemented in python. Different multi-resolution algorithm such as Wavelet, Contourlet and Curvelet are implemented and tested on real time SAR images. The input image is despeckled using different methods. During despeckling parameters such as ENL (Equivalent Number of Looks), PSNR (Peak Signalto-Noise Ratio) and SSIM (Structural Similarity Index Matrix) are used to evaluate the results. ENL in the Equation 9, is calculated as the square of the ratio between the mean of the random array which is ‘ ’ and the variance of the image which is ‘ ‘ and is calculated as =( )
(9)
Given a m x n noise-free image ‘ I’ and its noisy approximation ‘ K’ , then the MSE (Mean Squared Error) is calculated as in Equation 10, =
∑
∑
[ ( , ) − ( , )]
(10)
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From this MSE value the PSNR ratio can be calculated following Equation 11. The PSNR (in db) is defined as = 20. log (
)
10. log
(11)
The SSIM between the images ‘x’ and ‘y’ is evaluated from Equation 12
( , ) =
2μ μ μ
μ
2
(12)
where, µ is the mean value of intensity, σ is the standard deviation and c1 and c2 are constants. Tables 1,2 3 ,4 and 5 compares the despeckling accuracy of various transform domain techniques in the context of SAR images. Here parameters such as ENL, PSNR and SSIM are compared. The comparison of different multiresolution techniques in Ku-band image of a baseball diamond on Kirtland AFB is given in the Fig. 4. and different parameter comparison is represented in Table 1. Curvelet has the higher ENL, PSNR and SSIM values. Value of ENL shows speckle suppression in homogeneous areas. Higher PSNR value shows the higher signal strength in the image .Higher SSIM value indicates higher edge preservation capability. From the despeckled image and comparison table, Curvelet is obtained as the best despeckling multiresolution scheme. Hence the Object Detection is to be done to the image after obtaining the Curvelet based despeckling.
Fig 4: Comparison of Despeckling a) Original b) Wavelet c) Contourlet d) Curvelet over the image of Ku-band image of a baseball diamond
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Table 1: Comparison of parameters in the image baseball diamond on Kirtland AFB
Method
ENL
PSNR
SSIM
Wavelet
7.43
50.06
0.983
Contourlet
5.90
24.426
0.752
Curvelet
8.81
68.817
0.998
The comparison of the image the Mini SAR Ku-band image of a helicopter park near Kirtland AFB is represented in Fig. 5. Different parameters of the multiresolution scheme is compared in Table 2. From the despeckled image and comparison table, Curvelet is having higher values for ENL, PSNR and SSIM showing that it is the better despeckling multi-resolution scheme.
Fig 5: Comparison of Despeckling a) Original b) Wavelet c) Contourlet d) Curvelet over the Mini SAR Ku-band image of a helicopter park
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Table 2: Comparison of parameters in theMini SAR Ku-band image, of a helicopter park near Kirtland AFB.
Method
ENL
PSNR
SSIM
Wavelet
13.11
58.08
0.89
Contourlet
10.87
26.17
0.78
Curvelet
14.41
60.25
0.98
The comparison of the image the Ka-band image of a golf course clubhouse at Kirtland AFB is shown in Fig. 6. Note the radar shadow of golfers on the putting green is represented here. Different parameters of Ka-band image of a golf course clubhouse at Kirtland AFB is compared in Table 3. Compared to wavelet and contourlet, curvelet is having higher values for ENL, PSNR and SSIM. This shows that the noise is better reduced with higher edge clarity in curvelet despeckling. Table 3: Comparison of parameters in the Ka-band image, of a golf course clubhouse at Kirtland AFB
Method
ENL
PSNR
SSIM
Wavelet
9.23
63.94
0.997
Contourlet
7.74
26.80
0.815
Curvelet
9.81
66.31
0.981
Fig 6: Comparison of Despeckling a) Original b) Wavelet c) Contourlet d) Curvelet over the image of Ka-band image of a golf course clubhouse
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Table 4: Comparison of parameters in the image Ka-band image of a reapplication yard Kirtland AFB.
Method
ENL
PSNR
SSIM
Wavelet
22.0
52.32
0.981
Contourlet
14.11
32.46
0.850
Curvelet
23.41
54.259
0.987
The comparison of the image of a reapplication yard Kirtland AFB is represented in Fig.7 and different parameters of the image is compared in the Table 4. The comparison of the image of lunar reconnaissance orbiter is represented in Fig. 8 and the comparison of different parameters in the image of lunar reconnaissance orbiter is given in the Table 5. From the despeckled image and comparison table, the Curvelet is obtained as the best despeckling multi-resolution scheme.
Fig 7: Comparison of Despeckling a) Original image b) Wavelet c) Contourlet and d) Curvelet over the image of a reapplication yard
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Fig 8: Comparison of Despeckling a) Original image b) Wavelet c) Contourlet d) Curvelet over the image of lunar reconnaissance orbiter. Table 5: Comparison of parameters in the image of lunar reconnaissance orbiter.
Method
ENL
PSNR
SSIM
Wavelet
10.83
2.99
0.702
Contourlet
11.97
1.21
0.402
Curvelet
12.14
5.39
0.999
Comparing the parameters in different multi-resolution techniques it can be concluded that Curvelet has comparatively better performance compared to other multiresolution techniques. Curvelets exhibits higher ENL value indicating preservation of homogeneous areas in the despeckled image. Higher value of SSIM indicates higher degree of edge preservation. Higher PSNR value indicates less noise content in the despeckled image. Hence curvelet is chosen as a better method for despeckling SAR images compared to other transform domain techniques in the context of multiplicative noise. Curvelets exhibit high directional selectivity using angular polar wedges. Real images have anisotropic features which is better represented by Curvelets. Hence the Object Detection is to be done to the image after Curvelet based despeckling. Different curvelet based despeckled images are taken and object detection is being performed using the OpenCV. Fig. 9, shows a comparison between the original image, curvelet based despeckled image and the detected image. The detected image has red rectangle box to represent the object. Different objects are detected in different images and they are as in Fig. 9.
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Fig 9: Object Detection on Curvelet Despeckled Images.
The first image is the Ku-band image of a baseball diamond on Kirtland AFB. Here trees, shadows from the ground etc. are being detected. Image 2 is the image of a helicopter park near Kirtland AFB and it detects a helicopter, surrounding trees, and other dark shaded areas. Image 3 is the image of a golf course clubhouse at Kirtland AFB and it detects the golfer ground and surrounding trees. Image 4 is the image of reapplication yard Kirtland AFB where different trucks are detected and image 5 is the image of lunar reconnaissance orbiter where certain dark lunar portions are being detected. Figure 9 illustrates the original image, the despeckled image and the objects being detected in the curvelet despeckled image using CFAR. From the results it is clear that the objects are well detected in curvelet despeckled images.
7. Conclusion SAR data is increasingly being used in the field of remote sensing applications due to its all-weather day and night imaging capabilities. Due to the coherent processing, SAR images are corrupted by speckle noise which
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needs different ways of filtering. Speckle being signal dependent, multiplicative random noise it is difficult to remove. In this paper different multi-resolution schemes are compared for removing speckle noise. Several parameters such as ENL, PSNR, SSIM etc. are used for evaluating the despeckling accuracy of various techniques like Wavelet, Contourlet and Curvelet. Based on the results a method for despeckling SAR images based on curvelet transforms is being proposed. Object detection is done on the despeckled Curvelet image using CFAR. After the process of Despeckling and Object Detection a high resolution image is obtained as output where the objects are being clearly identified. Curvelet based despeckling technique is time consuming and further research can be carried out to reduce the time taken for this despeckling process.
References [1] D.J. Crisp, The State-of-the-Art in Ship Detection in Synthetic Aperture Radar Imagery, DSTO, Inf. Sci. Lab, Edinburgh, Australia, 2004. [2] Siti Noraini Sulaiman, Nor Ashidi Mat Isa,―Denoising-based Clustering Algorithms for Segmentation of Low Level Salt-and-Pepper NoiseCorrupted Images, IEEE Transactions on Consumer Electronics, Vol. 56, No. 4, pp. 2702-2710, November 2010. [3] José M. Bioucas-Dias, Mário A. T. Figueiredo, Multiplicative Noise Removal Using Variable Splitting and Constrained Optimization,ǁ IEEE Transactions on Image Processing, vol. 19, No. 7, pp. 1720-1730, July 2010 [4] Mann, S., Haykin, S., “The Chirplet Transform:Physical Considerations”, IEEE Transactions on Signal Processing, vol. 43, No. 11, Page(s): 2745 -2761, Nov. 1995. [5] Florian Luisier, Thierry Blu, Michael Unser, ―Image Denoising in Mixed Poisson–Gaussian Noise,ǁ IEEE Transactions on Image Processing, Vol. 20, No. 3, pp. 696-708, March 2011. [6] Devapal, D., S. Kumar, and C. Jojy(2015). Comprehensive survey on SAR image despeckling techniques. Indian Journal of Science and Technology, 8(24). [7] Buades, A., B. Coll, and J.-M. Morel (2011). Non-local means denoising. Image Processing On Line, 1, 208–212. [8] Devapal, D., S. Kumar, and C. Jojy(2017). A novel approach of despeckling SAR images using nonlocal means filtering. Journal of the Indian Society of Remote Sensing, 45(3), 443–450. [9] C.P. Papageorgiou, M. Oren, T. Poggio, A general framework for object detection, in: ICCV ’98: Proceedings of the International Conference on Computer Vision, Washington, DC, USA,1998, pp. 555-562. [10] R. Lienhart, J. Maydt, An extended set of Haar-like features for rapid object detection, in: ICIP ’02: Proceedings of the International Conference on Image Processing, 2002, pp. 900-903 [11]http://en.wikipedia.org/wiki/Speckle_noise [12] J. S. Lee (1986).Speckle Suppression and Analysis for Synthetic Aperture Radar Images. Optical Engineering, vol. 25, no. 5, pp.636 -645. [13] LI Chuan-zhen, WANG Hui, WANG Jing-dong, ZHANG Lei. Curvelet Transform for Image Denoising Based on Bayes Shrink[J]. [14] R. Lienhart, J. Maydt, An extended set of Haar-like featuresfor rapid object detection, in: ICIP ’02: Proceedings of the International Conference on Image Processing, 2002, pp. 900-903 [15] Viola, P.; Jones, M., ”Rapid object detection using a boosted cascade of simple features,” Computer Vision and Pattern Recognition (CVPR), 2001 IEEE Conference on, June 2001. [16] Khushboo Khurana and Reetu Awasthi,“Techniques for Object Recognition in Images and Multi-Object Detection”,(IJARCET), ISSN:22781323,4th, April 2013 [17] Latharani T.R., M.Z. Kurian, Chidananda Murthy M.V,“Various Object Recognition Techniques for Computer Vision”, Journal of Analysis and Computation, ISSN: 0973-2861 [18] Nidhi, “Image Processing and Object Detection”, Dept. of Computer Applications, NIT, Kurukshetra, Haryana, 1(9): 396-399, 2015. [19] R. Hussin, M. Rizon Juhari, Ng Wei Kang, R.C.Ismail, A.Kamarudin, “Digital Image Processing Techniques for Object Detection from Complex Background Image,”Perlis, Malaysia: School of Microelectronic Engineering, University Malaysia Perlis, 2012. [20]Y. Cui, G. Zhou, J. Yang, Y. Yamaguchi, On the iterative censoring for target detection in SAR images, IEEE Geosci. Remote Sens. Lett. 8 (4) (2011) 641–645. [21] L.M. Novak, G.J. Owirka, W.S. Brower, A.L. Weaver, The automatic target recognition system in SAIP, Lincoln Lab. J. 10 (2) (1997) 187– 202. [22] Yanzhao Gao, Ronghui Zhan, Jianwei Wan, Jiemin Hu, Jun Zhang, CFAR target detection in ground Sar image based on KK distribution, Prog. Electromagnet. Res. 139 (2013) 721–742. [23] M. di Bisceglie, C. Galdi, CFAR detection of extended objects in high-resolution SAR images, IEEE Trans. Geosci. Remote Sens. 43 (4) (2005) 833–843. [24] Opencv.org, „About OpenCV‟, 2017. [Online]. Available:http://www.opencv.org/about [25] Numpy.org, 2017. [Online]. Available: http://www.numpy.org.
ScienceDirect Materials Today: Proceedings 11 (2019) 1102–1116
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I2CN_2018
Object Detection from SAR Images based on Curvelet Despeckling Mrs. Devi Devapal1, Mrs. Hashna N.2, Ms. Aparna V. P.3, Ms. Bhavyasree C.4, Ms. Jeena Mathai5, Ms. Sangeetha Soman K.6 Dept of Computer Science and Engineering, College of Engineering, Pathanapuram, Elikkattoor P O, Kollam, Kerala, Pin: 689696, India
Abstract SAR images are increasingly gaining importance in the field of remote sensing applications. The receiving SAR data are affected by multiplicative speckle noise which form granular patterns. Hence despeckling is done prior to object detection. Here a comparison of several transform domain techniques are carried out for despeckling SAR images. Performance parameters such as ENL, PSNR, SSIM etc. are used to evaluate the result. From the result, Curvelet is chosen as the optimum technique for despeckling. In this paper Curvelet based despeckling of SAR images is performed and further object detection is done using CA-CFAR. © 2018 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of International Multi- Conference on Computing, Communication, Electrical & Nanotechnology: Materials Science. Keywords: SAR; Speckle; CA-CFAR; ENL; PSNR; SSIM; Curvelet;
1. Introduction SAR (Synthetic Aperture Radar) is an active satellite imaging technique which produces a high resolution image of earth’s surface. SAR antenna is mounted on a moving platform such as an aircraft or spacecraft. To create a SAR image, successive pulses of radio waves are transmitted to illuminate a target scene, and the echo of each pulse is received and recorded. Airborne radar synthesizes a longer antenna. The distance the aircraft flies in synthesizing the antenna is known as the synthetic aperture.The SAR image can be captured in all weather * Corresponding author. Devi Devapal Tel.: +91-9446503136; fax: 2225959. E-mail address: [email protected] 2214-7853© 2018 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of International Multi- Conference on Computing, Communication, Electrical & Nanotechnology: Materials Science.
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Η I K Α T
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Variance Mean Noise True Image Noisy Image Scaling Factor Detection Threshold
conditions. These images are affected by a large amount of random multiplicative noise called speckle. The main goal in image despeckling is eliminating speckle noise and also preserving the details of the edges [1]. Speckle is a special phenomenon in lasers, SAR or ultrasound images. Despeckling is carried out as a preprocessing step to object detection, segmentation and classification. They are the coherent summation of the return scattered signals and the random interference of electromagnetic signals causes speckle. Multi-pass single-look and single-pass multilook techniques are also adopted for speckle reduction [2, 3]. The fundamental theory behind SAR is that it needs transmission and reception of linearly frequency modulated chirp signals, and Doppler processing of the encoded returned echo [4]. Coherent signal processing is done to attain high spatial resolution. However due to this coherent nature of signal, a grainy speckle noise occur in the image, which degrades its radiometric quality. SAR image is used in variety of application in the areas of agriculture, land use type discrimination, forestry, biomass estimation, geology, flood mapping, disaster zone mapping, oil spill detection etc. With speckle noise, the accuracies of image analysis involving classification, segmentation, texture analysis, target detection etc. are severely affected. Speckle results in dilation of fine details of image, reduce the contrast, deteriorating the shape or size of objects and blurs the edges of the image and makes interpretation of image difficult. So there is a need for an efficient despeckling method which adapts to discontinuities in the image while preserving the important geometrical features like edges and contours. Reducing noise always comes at a cost of image degradation, blurring, spatial resolution degradation and edge smearing. Apart from that, the type of noise present in the image is a critical factor in formulating the despeckling scheme. Images produced by coherent processing such as those of SAR are affected by speckle noise, which is difficult to remove. This paper discuss briefly about SAR image and the characteristics of speckle noise occurring in it. The transform domain based image despeckling techniques are compared here, with special emphasis on SAR images. An appropriate method is chosen based on performance measures such as ENL, PSNR, SSIM etc. Then the Object Detection in SAR images is done on the curvelet despeckled image which is obtained as the better despeckling scheme. In the section 2, the survey about despeckling and Object detection is done. The section 3 describes the proposed methodology. Section 4 describes the different multi-resolution schemes for despeckling SAR images. Section 5 specifies about the object detection in the despeckled SAR images and section 6 gives the result analysis. The results obtained from various despeckling scheme and the object detection from this despeckled images are discussed. Finally section 7 describes overall conclusion and future scope.
2. Literature Survey This section surveys different papers related to Despeckling and Object Detection of SAR images. Speckle noise follows a multiplicative noise model where the noise distribution is not Gaussian, with Rayleigh and Gamma being commonly used densities [5]. Despeckling can be carried out either during the image formation time or later. Despeckling techniques can be broadly classified into spatial domain techniques and transform domain techniques. The transform domain techniques make use of the transform coefficients for filtering purpose. The transform domain based filtering techniques can be classified into threshold based methods and the statistical model based methods [6]. They make use of various transforms such as wavelet, ridgelet, curvelet, contourlet, shearlet, bandlet etc for despeckling images. The spatial techniques carries out filtering using mask and estimate the local noise variance .This mask is moved pixel by pixel over the whole image and the central pixel was substituted with a mathematically processed value. These techniques are computationally less complex. They are suitable for reducing speckle in homogeneous areas but over smooth heterogeneous areas. The spatial speckle reduction techniques make use of standard filters like Lee, Kuan, Frost, Kalman etc. for speckle reduction. These methods are referred to as
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local methods since they exploit the spatial redundancy in the local neighborhood. In non-local approach [7, 8] the intensity of each pixel is determined from the whole image rather than the local neighborhood and hence called nonlocal means approach. BM3D is denoising technique algorithm that follows non-local means modeling and exploits the redundancy occurring in natural images. In object detection, single object recognition is done using set of local feature templates which uses corner detector and filters as explained in [9]. Here the authors have verified planar object recognition using SIFT features which can provide match for affine geometric alignment. Feature based object recognition technique uses bag of key points [10]. Here features are quantized into words and used for text categorization.
3. Proposed Methodology In this paper, the object detection is carried out in the despeckled SAR image in order to ensure greater accuracy. The first process is despeckling which is done after comparing the different multi-resolution techniques such as Wavelet, Contourlet and Curvelet. Based on the comparative study, Curvelet is found as the better one and hence curvelet transform is chosen to despeckle SAR images. The curvelet transform is applied to the input SAR image where they are converted to transformed coefficients. Then threshold is applied in order to remove the noise. All those noise value greater than threshold are eliminated and the inverse transformation is applied to obtain the original noise free image.
Fig 1: Flowchart of the Automatic Target Detection System
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The algorithm for the proposed methodology is Step 1: Curvelet Decomposition Perform Curvelet transformation of the input SAR image. Step 2: Threshold Computation Except for low frequency sub-band compute the GCV threshold for all sub-bands. Step 3: Image reconstruction Image is reconstructed from noise-free coefficients using inverse curvelet transform. Step 4: Object detection Define and set up a CA-CFAR detector. Compute the object detection threshold using the despeckled image. After despeckling the process of object detection is done. Then set up a cell averaged constant false alarm detection. A threshold is calculated to identify object. And the target object is detected using a hard threshold. Fig. 1 represent the flowchart of the curvelet based automatic target object detection. The GCV mentioned in the above algorithm is the Generalized Cross Validation, which is used to find the threshold is calculated using the Equation 1.
( )=
−
,
(1)
The optimal threshold is obtained here, that is the minimum value of GCV function. In the equation 1, Nd is the number of Curvelet transform coefficient in the subband and the noisy coefficient are represented as Yd .The number of coefficient below threshold which were set at zero is Nd,0.
4. Despeckling In SAR Image Noise is a random variation of brightness or color information in images. It is also known as unwanted signals, which can be multiplicative or additive. The speckle noise is random noise which is multiplicative in nature. This speckle noise is formed as a result of the random interference between the coherent returns from active imaging sensors such as LASER, SAR etc. Radar pulses are transmitted coherently and depending on the exact distance travelled, the returning wave may be in phase or out of phase. When the returning waves are in phase, the intensity of the resulting signal will be amplified resulting in constructive interference (bright spots). When the returning waves are out of phase, they tend to cancel each other reducing the intensity of the signal resulting in destructive interference (dark spots). This constructive and destructive interference of signal produces speckle noise. It appears as granular patterns in the image and make the interpretation of image difficult. Speckle occurs when object illuminated by coherent radiation have rough surface. If ‘K’ denotes the output scattering coefficient of a target, ‘I’ is its true value, and ‘η’ is the speckle noise affecting the input signal then the response is represented as in Equation 2. = ∗η
(2)
Multiplicative noise is the characteristics formed to a fully developed speckle noise as shown by [11]. Speckle model is explained by using the signal geometry for a SAR sample in Fig.2. A particular resolution cell contains the scattering from a large number of scatters with a wavelength which is comparable to the roughness of the terrain or object being imaged. Hence the response from one cell is a coherently summed signal from many scatters. Speckle appears as random placement of dark and bright spots on the image. The basic aim of denoising technique is to remove noise, irrespective of the spectral content of the noisy signal. The filter should also preserve the spatial
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Fig 2: SAR Speckle Model
variability ie, the textural information for areas with texture. The aim is to clean the image without a change in the geometric resolution. General noise removal techniques are effective for additive noise present in optical images. Since speckle is a coherent type of noise which is multiplicative, general techniques do not work effectively on such images. Multi-look technique is one of the method applied for speckle noise reduction in the SAR signal. It is done in the frequency domain during the SAR data processing in the azimuth direction [12]. The signal bandwidth is split up into smaller segments and processed in the frequency domain. The outputs are incoherently summed up to get the output having better radiometric resolution, i.e. less noise. However in the process, the spatial resolution is degraded. For ‘N’ look processing the effective spatial resolution becomes N times the original spatial resolution, while the radiometric resolution is improved by a factor of √N. In order to overcome this problem, several filtering techniques have evolved, over the last decade. Among the different multi-resolution techniques, wavelet is the first one. A wavelet is a wave like oscillation that begins at amplitude zero, increases, and then decreases back to zero. Wavelets are brief oscillations and can be combined with known portions of a damaged signal to extract information from the unknown portions. It is done using a reverse, shift, multiply and integrate technique called convolution. Wavelet can be used as a mathematical tool for information extraction from different kind of data, audio and images. Thus, sets of complementary wavelets which are reversible are useful in wavelet based compression or decompression algorithms where it is used to recover the original image information with minimal or less loss. Another multi-resolution technique is contourlets, which is a multi-resolution directional tight frame designed and it is used to efficiently approximate images made of smooth regions separated by smooth boundaries. The contourlet fast implementation is based on a Laplacian Pyramid (LP) decomposition followed by Directional Filter-Banks (DFB) applied on band-pass sub-band. This transform uses a double filter bank structure to get the smooth contours of images. The double filter bank, the LP is first used to capture the point discontinuities. Then DFB is used to form those point discontinuities into linear structures. Only one band-pass image is produced by the decomposition of LP in the multi-dimensional signal processing, which can avoid the frequency scrambling. Since the DFB will leak the low frequency signals in its directional sub-bands, it is only fit for high frequency signals. Therefore the DFB and LP are combined which is multi-scale decomposition and then it remove the low frequency. Hence, the image signals pass through the LP sub-bands to get band-pass signals and pass those signals through DFB to capture the directional information of image. This double filter banks also called as Pyramid Directional Filter Bank (PDFB) which is the structure of combination of LP and DFB, and this transform approximates the original image by using basic contour. It is also called discrete contourlet transform.
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Another image processing multi-resolution technique is based on Curvelet which is a multi-scale directional transform which allows an optimal non-adaptive representation of edges designed to represent images at different scales and different angles [13]. Here the curved singularities are approximated with some coefficients. Curvelets are non-adaptive and remain coherent waveforms under the action of the wave equation in a smooth medium. The Discrete Curvelet Transform of a continuum function ( , ) makes use of a dynamic sequence of scales, and a bank of filters ( , ∆ , ∆ , … . . )with the property that the pass-band filter ∆ is concentrated near ]. The wavelet theory describes decomposition into dyadic sub-bands[2 , 2 ]. In the frequencies [2 , 2 contrast, the sub-bands used in the Discrete Curvelet Transform is represented as in the Equation 3. ( , )=
( , )
∑
( , )
(3)
Curvelet decomposition is the sequencing process consisting of subband decomposition as shown in Equation 4. ,∆ ,∆ ,…..)
(
(4)
Equation 5 shows smooth partitioning where each subband is smoothly windowed into squares of appropriate scale. ∆f
(
∆ )
(5)
In renormalization, each resulting square is renormalized to unit scale as represented in Equation 6. (
) (
∆ )
(6)
In the ridgelet analysis each square is analyzed via the discrete ridgelet transform where the two dyadic sub-bands are merged before applying the ridgelet transform.
Fig.3: Curvelet Transform Flow Diagram. Fig. 3 illustrates the decomposition of the original image into subbands followed by the spatial partitioning of each sub-band. The ridgelet transform is then applied to each block. It introduces inter-scale orthogonality by means of sub-band filtering. Different levels of the multi-scale ridgelet pyramid are used to represent different sub-
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bands of a filter bank output and also this sub-band decomposition imposes a relationship between the width and length of the important frame elements so that they are anisotropic and obey the relation ℎ= ℎ . Therefore anisotropic features that occur in real images are better represented by Curvelets.
5. Object Detection in SAR Images The incoming radar waves are reflected by most of the man-made objects, from background which clutters the detection of these objects. Due to the bright spots induced by the interference of reflected coherent waves, the SAR image interpretation is difficult [14]. By thresholding the output of the receiver, a target is detected from the radar signals. If the output is greater than a fixed threshold, it is considered as a target presence otherwise a target absence. If the threshold is fixed too high, it is a missed detection where strong target echoes would be detected and weak echoes might be lost. The false alarm occurs if the fixed threshold is too low, even the noise alone might be able to exceed the threshold [15]. If a proper threshold is fixed along with the noise, the output would not exceed the fixed threshold. Thus the selection of a perfect fixed threshold is a compromise between false alarm and missed detection. Thus to keep a constant false alarm rate, threshold has to be varied adaptively and is achieved by using constant false alarm rate detectors. In order to achieve the targeted objective [16] more efficiently and accurately an improvement in object detection is still required [17]. In the input image the process of object detection analysis is to determine the number, location, size and position of the objects. The process of object detection is the basic concept for tracking and recognition of objects. It affects the efficiency and accuracy of object recognition. The color-based approach is the common object detection method. It detects the objects based on their color values [18]. This method is used because of its strong adaptability and robustness, hence, the detection speed needs to be improved, because it requires testing all possible windows by exhaustive search and has high computational complexity. The challenging application in the image processing is the object detection from a complex background. The goal of this project is to identify objects placed over a surface from a complex background image using various techniques. The Intersection-over-Union (IOU) means the standard performance measure that is commonly used for the object category segmentation problem [19]. The similarities between the predicted region and the ground-truth region for an object present in the given image can be found with the help of IOU measures and can be defined as the size of the intersection divided by the union of the different regions. For example, if any particular algorithm predicts each and every pixel of an image to be its background, the IOU measure is given as the intersection between the predicted and ground-truth regions which would be zero. OpenCV (Open Source Computer Vision) library implemented in python2.7 along with the help of Numpy is used for object detection. A virtual ANN (Artificial Neural Network) is created using Sci-kit tool. The Edge matching technique involves the uses of edge detection techniques to find the edges. Effect of changes in lighting and color and it also count the number of overlapping edges. The Divide and Conquer search is the next process where all positions are to be considered as a set and the lower bound is determined at best position in the cell. Next is the gray scale matching where the pixel distance is computed as a function of both pixel intensity and position. Last one is the gradient matching. In gradient matching, comparing image gradients can also be helpful in making it robust to illumination change and matching is performed like matching gray scale images. The steps include calculating the Euclidean distance between center of the circle and the connected points. First convert the image to a gray scale image, detect edges, move along edges, and draw normal which will intersect at center. And then repeat this process for entire circle or find connected edges and then calculate Euclidean distance between center of the circle and the connected points. The manual interpretation of SAR images for object detection is difficult and error prone. Automatic algorithms for the purpose of object detection [20] mostly uses Constant False Alarm Rate (CFAR) algorithm. In such detectors the clutter distribution is always locally estimated according to a given probability of false alarm. Cell-averaging CFAR (CA-CFAR) detector is the most common among the CFAR detectors. These CFAR detectors works well in situation where the speckle noise in the SAR images are relatively low. The role of the constant false alarm rate is to determine the power threshold above which any return can be considered to probably originate from a target. If this threshold is too low, then more targets will be detected with expense of increased numbers of false alarms [21]. If the threshold is too high, then fewer targets will be detected, but the number of false alarms will also be low. In most radar detectors, the threshold is set in order to achieve a required probability of false alarm.
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If the background of the targets is constant with time and space, then a fixed threshold level can be chosen that provides a specified probability of false alarm, governed by Gaussian probability density function. But in scenario like SAR imaging where the noise level changes both spatially and temporally [22], a changing threshold is needed. Here the threshold level is raised and lowered to maintain a constant probability of false alarm. The key task on detection is coming up with an appropriate threshold. In general, the threshold is a function of both the probability of detection and the probability of false alarm. In many phased array systems [23], it is desirable to have a detection threshold that maximizes the probability of detection and keeps the probability of false alarm below a preset level. In CFAR, when the detection is needed for a given cell, often termed as the Cell Under Test (CUT), the noise power is estimated from neighbouring cells. Then the detection threshold ‘ T ’ is given by the Equation 7. =
(7)
where ′ ′ is the noise power estimate and ‘ ‘ is a scaling factor called the threshold factor. From the equation 7, it is clear that the threshold adapts to the data. With the appropriate threshold factor ‘ , the resulting probability of false alarm can be kept at a constant, hence the name CFAR. The cell averaging CFAR detector is probably the most widely used CFAR detector. It is also used as a baseline comparison for other CFAR techniques. In a cell averaging CFAR detector, noise samples are extracted from both leading and training cells around the CUT. The noise estimate can be computed using Equation 8. = ∑
(8)
where ‘N’ is the number of training cells , ‘ ′ is the sample in each training cell and ‘ ‘ represents the estimated noise power. Guard cells are placed adjacent to the CUT, in both leading and lagging cells to avoid signal components from leaking into the training cell, which could adversely affect the noise estimate.
6. Results and Discussion The effectiveness of the proposed despeckling and object detection algorithm using Curvelet on real SAR images is presented in this section. Different SAR images of resolution 200x200 pixels with speckle noise are given as input to the Curvelet based despeckling algorithm for evaluating the performance. OpenCV is an open source computer vision and machine learning software library [24]. It supports real-time vision applications and these algorithms are categorized under classic algorithms, computer vision algorithms and machine learning algorithms. These algorithms are easily implemented in Java, MATLAB, Python, C, C++ etc. and are well supported by operating system like Window, Mac OS, Linux and Android. A full-featured CUDA and OpenCV interfaces are being actively developed for the betterment of technology. OpenCV is written natively in C++ and has a template interface that works seamlessly with STL containers. For OpenCV to work efficiently with python 2.7 we need to install NumPy package first. NumPy is the fundamental package for scientific computing with Python [25] and it can be treated as an extension of the Python programming language with support for multidimensional matrices and arrays. The process of despeckling is implemented in python. Different multi-resolution algorithm such as Wavelet, Contourlet and Curvelet are implemented and tested on real time SAR images. The input image is despeckled using different methods. During despeckling parameters such as ENL (Equivalent Number of Looks), PSNR (Peak Signalto-Noise Ratio) and SSIM (Structural Similarity Index Matrix) are used to evaluate the results. ENL in the Equation 9, is calculated as the square of the ratio between the mean of the random array which is ‘ ’ and the variance of the image which is ‘ ‘ and is calculated as =( )
(9)
Given a m x n noise-free image ‘ I’ and its noisy approximation ‘ K’ , then the MSE (Mean Squared Error) is calculated as in Equation 10, =
∑
∑
[ ( , ) − ( , )]
(10)
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From this MSE value the PSNR ratio can be calculated following Equation 11. The PSNR (in db) is defined as = 20. log (
)
10. log
(11)
The SSIM between the images ‘x’ and ‘y’ is evaluated from Equation 12
( , ) =
2μ μ μ
μ
2
(12)
where, µ is the mean value of intensity, σ is the standard deviation and c1 and c2 are constants. Tables 1,2 3 ,4 and 5 compares the despeckling accuracy of various transform domain techniques in the context of SAR images. Here parameters such as ENL, PSNR and SSIM are compared. The comparison of different multiresolution techniques in Ku-band image of a baseball diamond on Kirtland AFB is given in the Fig. 4. and different parameter comparison is represented in Table 1. Curvelet has the higher ENL, PSNR and SSIM values. Value of ENL shows speckle suppression in homogeneous areas. Higher PSNR value shows the higher signal strength in the image .Higher SSIM value indicates higher edge preservation capability. From the despeckled image and comparison table, Curvelet is obtained as the best despeckling multiresolution scheme. Hence the Object Detection is to be done to the image after obtaining the Curvelet based despeckling.
Fig 4: Comparison of Despeckling a) Original b) Wavelet c) Contourlet d) Curvelet over the image of Ku-band image of a baseball diamond
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Table 1: Comparison of parameters in the image baseball diamond on Kirtland AFB
Method
ENL
PSNR
SSIM
Wavelet
7.43
50.06
0.983
Contourlet
5.90
24.426
0.752
Curvelet
8.81
68.817
0.998
The comparison of the image the Mini SAR Ku-band image of a helicopter park near Kirtland AFB is represented in Fig. 5. Different parameters of the multiresolution scheme is compared in Table 2. From the despeckled image and comparison table, Curvelet is having higher values for ENL, PSNR and SSIM showing that it is the better despeckling multi-resolution scheme.
Fig 5: Comparison of Despeckling a) Original b) Wavelet c) Contourlet d) Curvelet over the Mini SAR Ku-band image of a helicopter park
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Table 2: Comparison of parameters in theMini SAR Ku-band image, of a helicopter park near Kirtland AFB.
Method
ENL
PSNR
SSIM
Wavelet
13.11
58.08
0.89
Contourlet
10.87
26.17
0.78
Curvelet
14.41
60.25
0.98
The comparison of the image the Ka-band image of a golf course clubhouse at Kirtland AFB is shown in Fig. 6. Note the radar shadow of golfers on the putting green is represented here. Different parameters of Ka-band image of a golf course clubhouse at Kirtland AFB is compared in Table 3. Compared to wavelet and contourlet, curvelet is having higher values for ENL, PSNR and SSIM. This shows that the noise is better reduced with higher edge clarity in curvelet despeckling. Table 3: Comparison of parameters in the Ka-band image, of a golf course clubhouse at Kirtland AFB
Method
ENL
PSNR
SSIM
Wavelet
9.23
63.94
0.997
Contourlet
7.74
26.80
0.815
Curvelet
9.81
66.31
0.981
Fig 6: Comparison of Despeckling a) Original b) Wavelet c) Contourlet d) Curvelet over the image of Ka-band image of a golf course clubhouse
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Table 4: Comparison of parameters in the image Ka-band image of a reapplication yard Kirtland AFB.
Method
ENL
PSNR
SSIM
Wavelet
22.0
52.32
0.981
Contourlet
14.11
32.46
0.850
Curvelet
23.41
54.259
0.987
The comparison of the image of a reapplication yard Kirtland AFB is represented in Fig.7 and different parameters of the image is compared in the Table 4. The comparison of the image of lunar reconnaissance orbiter is represented in Fig. 8 and the comparison of different parameters in the image of lunar reconnaissance orbiter is given in the Table 5. From the despeckled image and comparison table, the Curvelet is obtained as the best despeckling multi-resolution scheme.
Fig 7: Comparison of Despeckling a) Original image b) Wavelet c) Contourlet and d) Curvelet over the image of a reapplication yard
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Fig 8: Comparison of Despeckling a) Original image b) Wavelet c) Contourlet d) Curvelet over the image of lunar reconnaissance orbiter. Table 5: Comparison of parameters in the image of lunar reconnaissance orbiter.
Method
ENL
PSNR
SSIM
Wavelet
10.83
2.99
0.702
Contourlet
11.97
1.21
0.402
Curvelet
12.14
5.39
0.999
Comparing the parameters in different multi-resolution techniques it can be concluded that Curvelet has comparatively better performance compared to other multiresolution techniques. Curvelets exhibits higher ENL value indicating preservation of homogeneous areas in the despeckled image. Higher value of SSIM indicates higher degree of edge preservation. Higher PSNR value indicates less noise content in the despeckled image. Hence curvelet is chosen as a better method for despeckling SAR images compared to other transform domain techniques in the context of multiplicative noise. Curvelets exhibit high directional selectivity using angular polar wedges. Real images have anisotropic features which is better represented by Curvelets. Hence the Object Detection is to be done to the image after Curvelet based despeckling. Different curvelet based despeckled images are taken and object detection is being performed using the OpenCV. Fig. 9, shows a comparison between the original image, curvelet based despeckled image and the detected image. The detected image has red rectangle box to represent the object. Different objects are detected in different images and they are as in Fig. 9.
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Fig 9: Object Detection on Curvelet Despeckled Images.
The first image is the Ku-band image of a baseball diamond on Kirtland AFB. Here trees, shadows from the ground etc. are being detected. Image 2 is the image of a helicopter park near Kirtland AFB and it detects a helicopter, surrounding trees, and other dark shaded areas. Image 3 is the image of a golf course clubhouse at Kirtland AFB and it detects the golfer ground and surrounding trees. Image 4 is the image of reapplication yard Kirtland AFB where different trucks are detected and image 5 is the image of lunar reconnaissance orbiter where certain dark lunar portions are being detected. Figure 9 illustrates the original image, the despeckled image and the objects being detected in the curvelet despeckled image using CFAR. From the results it is clear that the objects are well detected in curvelet despeckled images.
7. Conclusion SAR data is increasingly being used in the field of remote sensing applications due to its all-weather day and night imaging capabilities. Due to the coherent processing, SAR images are corrupted by speckle noise which
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needs different ways of filtering. Speckle being signal dependent, multiplicative random noise it is difficult to remove. In this paper different multi-resolution schemes are compared for removing speckle noise. Several parameters such as ENL, PSNR, SSIM etc. are used for evaluating the despeckling accuracy of various techniques like Wavelet, Contourlet and Curvelet. Based on the results a method for despeckling SAR images based on curvelet transforms is being proposed. Object detection is done on the despeckled Curvelet image using CFAR. After the process of Despeckling and Object Detection a high resolution image is obtained as output where the objects are being clearly identified. Curvelet based despeckling technique is time consuming and further research can be carried out to reduce the time taken for this despeckling process.
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