A new fuzzy measurement approach for automatic change detection using remotely sensed images

Accepted Manuscript A New Fuzzy Measurement Approach for Automatic Change Detection using Remotely Sensed Images Vahid Sadeghi, Farshid Farnood Ahmadi, Hamid Ebadi PII: DOI: Reference:

S0263-2241(18)30487-1 https://doi.org/10.1016/j.measurement.2018.05.097 MEASUR 5598

To appear in:

Measurement

Received Date: Revised Date: Accepted Date:

15 February 2017 13 April 2018 24 May 2018

Please cite this article as: V. Sadeghi, F. Farnood Ahmadi, H. Ebadi, A New Fuzzy Measurement Approach for Automatic Change Detection using Remotely Sensed Images, Measurement (2018), doi: https://doi.org/10.1016/ j.measurement.2018.05.097

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A New Fuzzy Measurement Approach for Automatic Change Detection using Remotely Sensed Images

Vahid Sadeghi Assistant Professor, Department of Geomatics Engineering, Faculty of Civil Engineering, University of Tabriz 29 Bahman Boulevard, Tabriz, IRAN Email: [email protected] FAX: +98 411 3344287

Farshid Farnood Ahmadi Associate Professor, Department of Geomatics Engineering, Faculty of Civil Engineering, University of Tabriz 29 Bahman Boulevard, Tabriz, IRAN Email: [email protected] TEL: +98 914 3138723 FAX: +98 411 3344287

Hamid Ebadi Professor, Faculty of Geodesy & Geomatics Engineering, K.N.Toosi University of Technology No. 1346, Vali-Asr Street, Mirdamad Cross, Tehran, IRAN Email: [email protected] TEL: +982188786212 FAX: +982188786213

Abstract This paper presents a new fuzzy change detection and measurement approach to overcome the drawbacks of traditional thresholding methods in remote sensing. The proposed technique is taking the advantages of following concepts: 1- asymmetric thresholding in order to improve the reliability and accuracy of change detection in each spectral bands, 2- fuzzy measurement approach to consider ambiguity in thresholding of difference image, and represent the changes in fuzzy form and fusing the obtained change maps from various spectral bands, and 3- non-linear modeling with artificial neural networks for relative

radiometric normalization (RRN). The performance of developed technique is evaluated on a pair of Landsat 5, 7 images which were taken over the southern part of Urmia Lake, Iran. The major merits of developed technique compared to prevalent thresholding techniques are: higher accuracy in change detection and measurement, higher capability in detection of multiple changes and lower dependence on quality of RRN process. Keywords: Automatic; Change Detection; Fuzzy Measurement; Remote sensing.

Abbreviations ADI ANN BCM CD DI DN FPOC LC LU LT MF NegC NC PozC RCSS RRN RS TCCM UT

Absolute Difference Image Artificial Neural Networks Binary Change Map Change Detection Difference Image Digital Number Fuzzy Possibility Of Change Land Cover Land Use Lower Threshold Membership Function Negative-Change No-Change Positive-Change Radiometric Control Set Samples Relative Radiometric Normalization Remote Sensing Three-Class Change Map Upper Threshold

1. Introduction Change detection is a fundamental and multi-disciplinary subject of research, including remote sensing [1, 2], civil engineering [3-6], mechanical engineering [7, 8], surveillance [9], medical diagnosis

[10], and etc. In Remote Sensing (RS) applications, change

detection means measuring the change on the Earth's surface by analyzing co-registrated multi-temporal images of the same geographical area acquired at different times. The demand for natural resources experience a significant increase by encountering a boom in urbanization and population, which leads to side effects in land cover (LC) and land use (LU) such as soil erosion, woodcutting, coastal degradation, lake disappearing and etc. For management and planning purposes, the changes in LC and LU should be identified accurately in a timely manner as its changes are quit dynamic. The RS imagery especially in the optical (visible-infrared) region of the electromagnetic spectrum has become a major

source for change detection and measurement studies because of its high temporal frequency, suitable digital format for computation, synoptic view and wider selection of spatial and spectral resolutions. The pivotal principle for utilizing RS data for change detection and measurement is that changes in the object of interest will alter the spectral behavior (reflectance value or local texture) that is separable from changes caused by other factors (e.g. atmospheric conditions, illumination and viewing angles, and soil moistures) [11-13]. In RS based change detection(CD) the following characteristics of change can be identified: geographic location, extent and distribution and nature of the change (e.g., “from-to” change information) [14]. Developing of CD methods in RS is an unremitting research agenda. The two main approaches for CD problem in RS literature, can be considered as: (1) supervised and (2) unsupervised approaches. The former is based on supervised classification methods, which requires the convenience of multi-temporal training samples in order to guide the learning process of the classifiers. The latter performs CD by providing a direct comparison of the multi-temporal considered images without relying on any additional information. Despite the fact that the supervised approach demonstrates some merits over the unsupervised one, the collecting appropriate multi-temporal training samples are usually a troublesome and costly task, especially when archival RS images are used for change detection and measurement. Consequently, for a wide variety of applications in which a training samples is not available or quick overview of products are needed as preliminary output, the employment of effective unsupervised CD methods is fundamental. In the RS literature, several unsupervised CD methods have been proposed for multispectral RS images acquired by optical sensors [15-28]. A widely employed technique among them is histogram thresholding [14, 29, 30]. This technique generally consists of two steps: (1) engendering change enhanced images and (2) selecting the appropriate threshold to generate binary change masks. Appropriate selection of the threshold is crucial to discriminate “changed” region from “unchanged” region in change-enhanced images. In spite of its relative simplicity and widespread use, the considered CD method demonstrates three major drawbacks, as follows:  Symmetric assumption of the histogram The first demerit in prevalent thresholding methods to identify changes in RS imagery is assuming the histogram of difference image (DI) as symmetrical which leads to a symmetric thresholding to discriminate the “changed” regions from “unchanged” ones by analysing the absolute difference image (ADI) instead of DI (see Fig. 2). However, assuming the histogram

of DI as symmetrical cannot be applied for practical applications in real world. The consequence of such presumption is the low reliability of CD. Im et al. [14] made efforts for considering an asymmetrical characteristic for histogram of DI by utilizing the “moving thresholding window” instead of symmetric threshold window. Although the improvements of CD results are reported in this paper, but the necessity for training samples in this technique has led to elimination of having no need for training data in thresholding method as a major advantage.  Necessity for near-perfect radiometric correction of multi-temporal images The other disadvantage of thresholding techniques that should be taken into consideration is the high dependency of CD results on the condition of multi-temporal imaging such as atmospheric condition, the target-sensor-illumination geometry and sensor calibration. The radiometric correction is a pre-process that should be taken precisely into account in these techniques to accurate CD results. So, the necessity for ideal conduction of relative radiometric normalization (RRN) process, which is not feasible in some cases, decreases the efficiency of thresholding techniques in analyzing the multi-temporal RS imagery with high radiometric differences. 

The one-dimensional nature of thresholding techniques

It is shown based on experience that the occurred changes in environment are in a way that the different spectral changes of phenomenon can be detected in different parts of electromagnetic spectrum. Hence, utilization of several spectral bands can supply a higher accuracy in CD. However, the prevalent thresholding techniques are developed for onedimensional space and they are not appropriate for CD in multi-dimensional spaces of RS images. So, one of the following approaches can be taken: 1- by fusion of multi-spectral change image and acquiring the one-dimensional change index ( “magnitude of change”), the dimension of data is reduced [30]. 2- some modifications are made in the structure of thresholding techniques to provide them with the capability of thresholding of multidimensional data [31]. 3- the thresholding procedure is applied on each band independently and then the results are fused [29]. In the first approach, although obtaining and employing the magnitude of change index facilitates the implementation of thresholding techniques in analysis of multi-spectral images, but in practice it eliminates precious data which are acquired through RS multi-spectral sensors. Researches show that the equations in second

approach are severely sophisticated and are capable to support two-dimensional data for now [31] . Also, we should not lose the sight of the fact that calculation in this approach is time consuming. The last approach which is the data fusion technique in decision level, is an applicable method. Decision level fusion makes a global decision by taking the decisions from each sensor or decision maker (for instance: thresholding techniques) as inputs and fusing them. Many methods have been developed for data fusion in decision level such as:. Bayesian methods [29, 32], Dempster-Shafer [32, 33], fuzzy logic [32, 34], decision tree [35], major voting [36]. Whereas, it is enigmatic to decide which of mentioned methodologies is the most appropriate one, a common particularity in all these approaches (except major voting) is their supervised nature, as the analyst must establish the rules which can be the best fit to a certain application and dataset. In order to obviate the drawbacks of the thresholding technique in RS-based change detection and measurement, a general strategy is proposed which will be discussed in methodology section.

2. Methodology This paper presents a new fuzzy unsupervised change detection and measurement approach to overcome the drawbacks of traditional thresholding methods in remote sensing. The main contributions of this research are:

(1) developing a new fuzzy measurement

approach to consider ambiguity in thresholding of difference image, represent the changes in fuzzy form and fusing the obtained change maps from various spectral bands, (2) extending the conventional bi-level thresholding concept to asymmetric thresholding in order to detect multiple changes and provide higher accuracy and reliability in change detection and measurement, along with determining the parameters of fuzzy membership functions automatically, (3) evaluating the different architectures of artificial neural networks for relative radiometric normalization (RRN) problem and applying this nonlinear model to improve the accuracy of RRN and change detection and measurement. It is worth noting that all of the; parameterization of the membership functions in fuzzyification process, threshold estimation in asymmetric thresholding and RCSS selection in RRN, carried out automatically without involvement of expert. Flowchart 1, conveys the developed approach in this study for fuzzy detecting and displaying of changes in optical RS images. The proposed technique is comprising the following major phases: 1. Generating the change index 2. Generating the histogram of change index

3. Asymmetric thresholding of histogram 4. ANN-based relative radiometric normalization 5. Applying fuzzy change model 5.1. Fuzzification of change index 5.1.1. Membership function determination 5.1.2. Membership degrees calculation 5.2. Aggregation 5.3. De-Fuzzification 5.4. Fuzzy visualization of changes The developed method will be explained in details in following sub-sections.

Fig. 1. Flow diagram of the proposed fuzzy change detection approach.

2.1. Artificial Neural Network-based Relative Radiometric Normalization The optical RS imagery is influenced by a number of factors, such as atmospheric absorption and scattering, sensor-target-illumination geometry, sensor calibration and image data processing procedures which tend to change through times [37]. In order to detect genuine landscape changes as revealed by changes in surface reflectance from multi-temporal RS images, it is necessary to carry out radiometric correction [38]. In general, these variation effects can be corrected on optical RS images either by absolute calibration of the single images or by Relative Radiometric Normalization (RRN) between the images. RRN utilizes one image as a reference image and adjusts the radiometric properties of the other one known as subject image to match the reference image, radiometrically [38]. Many approaches have been developed for RRN of multi-temporal RS images. There are two main drawback in the traditional methods of RRN. Most of these methods assume that the percentage of changed regions in an image is small relative with respect to the entire image. Hence, in the estimation of normalization coefficients, they use all area of the image (such as HM, HC, MM, MS, and SR methods) [38]. Performance of such methods is often not as appropriate as those methods which use radiometric control set samples (RCSS) in terms of normalization accuracy [38]. On the flip side, those methods that employ RCSS, are time consuming. Additionally, the normalization results severely depend on the performance of operator in determining the empirical parameters. Another major demerit of RRN methods is linear modeling of normalization process. Most of RRN methods assume that in the subject image, the RCSS

values follow a linear function in the reference image. Linearity assumption of this relation affects the normalization results. Hence, this relation does not typically follow a linear model and this assumption is mainly applied only because of simplification of modeling. In previous study, an automated RRN method was developed to adjust a non-linear model based on an ANN and RCSS to radiometric correction of multi-temporal optical RS images [38]. The proposed method was comprising the following phases: (1) automatic detection of RCSS, (2) assessing the different architectures of perceptron neural networks to find the best structure for the specific task and (3) training and recalling the considered network for normalizing the subject image using Eq. 1[38]. (1) where,

is the digital number (DN) of band i in normalized subject image,

is the DN of

band i in raw subject image, and f (.) is a non-linear function (trained ANN). This ANNbased approach has been adopted in this study. The RCSS are selected by automatic thresholding of change image. Regarding the fact that RCSS selection is an important stage in RRN, to enhance this process, asymmetric thresholding was used instead of traditional symmetric thresholding. The asymmetric thresholding will be discussed in details in section 2.2.2. After, producing RCSS, 75% of RCSS, called ‘‘training RCSS’’ were selected randomly for training ANN and the rest of the RCSS called ‘‘test RCSS’’ were used as evaluation data. In applying ANN for RRN of remote sensing images, both input and output layers were formed of one neuron which was the gray value of RCSS in subject and reference images, respectively. Regarding the scatter plot of reference image versus subject image gray values in RCSS, one hidden layer with variable neuron number was considered as an appropriate architecture of ANN for RRN problem. This ANN architecture has already been confirmed experimentally for RRN process [38]. Although the neuron number of hidden layer is an important factor, there is no rigorous protocol to determine this parameter. In this study, various ANN models were tested changing the number of neurons in the hidden layer between 1 and 21 with the increments of five. All mentioned ANN models were evaluated 5 times and the minimum of Root Mean Square Error (RMSE) of test RCSS was considered as performance of each model. The less RMSE, the better the model is. Figs. 2, and 3 show the variation trends of RMSE of test RCSS and network’s training time for increased neuron numbers of hidden layer in bands 1–5, and 7, respectively. As seen in Figs. 2, and 3, increasing the hidden layer neuron number from 6 to 21, does not decrease the RMSE

dramatically, but increases significantly the training time of ANN. In order to reach optimum RRN accuracy and appropriate network’s training time, hidden layer neuron number set to 6. Fig. 2. Trend plots of RRN’s RMSE values versus hidden layer neuron numbers in different spectral bands. Fig. 3. Trend plots of network’s training time versus hidden layer neuron numbers in different spectral bands.

2.2. Asymmetric (Three-Level) Thresholding of Change Index 2.2.1 Principles of thresholding Let us consider two co-registered multi-spectral images X1={xα1(i,j) |1≤i≤I, 1≤j≤ J, 1≤α≤γ, xα1(i,j) ∈ w, w= (0, 1,...,L−1)} and X2={xα2(i,j) |1≤i≤I, 1≤j≤ J, 1≤α≤γ, xα2(i,j) ∈ w, w= (0, 1,..., L−1)} of size of and

acquired over the same area at different times T 1 and T2. Here

are the DNs of a pixel at the spatial position (i,j) in the αth band of images X1 and

X2, respectively. There are two major approaches for discriminating the changed pixels from unchanged ones: 1) analyzing the histogram of difference image (DI) and 2) analyzing the histogram of absolute difference image (ADI). The first approach is based on the utilization of the statistical characteristics of difference image XD={ xαD (i,j) |1≤ i ≤I, 1≤ j ≤ J, xαD (i,j)= xα2 (i,j) – xα1 (i,j) } such as the mean and standard deviations [39-42]. The pixels with closest values to the mean are generally associated with no-change class. Thus, by using standard deviations (e.g., ±0.5 standard deviation, ±1 standard deviation, etc.) as a threshold, changed pixels are identified (Fig.4). This approach generally works well if changes have occurred in limited locations, for instance ±5 percent of the entire study site showing normal distribution of the pixel values in the DI. In the following approach, the absolute difference image XAD={xAD(i,j) |1≤ i ≤I, 1≤ j ≤ J, xAD(i,j) ∈ w, w=(0, 1,...,L−1)} which was obtained by applying the CVA technique [29, 30] (Eq.2), is analyzed to discriminate changed pixels from unchanged ones. In Eq.2, int (.) is the nearest integer function which returns the nearest integer to XAD.

(2)

When observing the ADI, the changed values are found on the right of the histogram, while the unchanged ones are located to the left [Fig.5]. In this case (ADI thresholding), the purpose of thresholding is to divide the pixels into ‘Change’ and ‘No-Change’ classes.

Fig.4. Threshold identification in DI [14].

Fig.5. Threshold identification in ADI [17]. Given a threshold T ∈ w, w= (0, 1,...,L−1), the binary change map BCM ={ BCM (i,j) |1≤ i ≤I, 1≤ j ≤ J, BCM (i,j) ∈{0,1}} takes the following value: (3) According to [14] selecting a decision threshold value has an extreme relevance, as the accuracy of the BCM will depend strikingly on this selection. Wide ranges of techniques have been introduced for automatic determination of optimum threshold T in ADI. Otsu’s method [43], Kapur’s method [44], Kittler’s method [45], EM’s method [17] are the most widely employed non-fuzzy histogram thresholding techniques, along with Huang’s method [46] and Liu’s method [47] as the commonly used fuzzy histogram thresholding techniques. The related results and comparisons of these techniques are given in [29, 30, 48]

2.2.2. Asymmetric (Three-Level) Thresholding Symmetric thresholding (thresholding of ADI) generally works well if the radiometric correction of the remotely sensed data is near-perfect and multiple changes have not been occurred in the study area. However, near-perfect radiometric correction is rarely achieved as many factors influence the reflectance of the same material in the scene between two dates. These factors can be the look angle of the sensor(s), atmospheric effects, soil moisture conditions [14]. As well as, multiple changes usually occurred in practice. Thus, the probability of encountering a symmetric threshold is low. Consequently, as illustrated in Fig. 6, asymmetric thresholds: 1) Lower Threshold (LT) and 2) Upper Threshold (UT) may work better than a single symmetric threshold. As shown in Fig. 6, these thresholds, partition the

histogram of DI into three main regions: 1) negative-change (NegC), 2) no-change (NC), and 3) positive-change (PozC).

Fig. 6. Asymmetric thresholding in DI. The lower threshold (LT) and upper threshold (UT) partition the histogram into three regions: Negative-Change, NoChange, and Positive-Change. The Sahoo et al. [48] concluded that Otsu’s method was one of the better threshold selection methods for general real world images with regard to uniformity and shape measures. This method still remains one of the most referenced thresholding methods [49] and [29] As well as it is easy to extend the conventional bi-level Otsu technique to multi-level thresholding. This method has been adopted in this research. In the case of bi-level thresholding of an image (thresholding of ADI), the pixels are divided into two classes, No-Change with DNs [0, …, t] and Change with DNs [t+1, …, L-1] , by a threshold at DN t. Let the number of pixels in the image with DN i be fi. Then the total number of pixels in the DI is

. The probability of occurrence of DN i is: (4)

Then, the DNs probability distributions for the two classes are: (5)

Where, the cumulative probabilities (

and

) for classes No-Change and Change,

respectively, are given by, = =

(6)

, ,

Also, the means for classes No-Change and Change are: ,

,

(7)

Using discriminant analysis, Otsu [43] defined the between-class variance of the thresholded image as: (8) Where

(the mean intensity of the original whole image) is defined as (9)

For bi-level thresholding, Otsu verified that the optimal threshold t* is chosen so that the between-class variance

is maximized; that is, (10)

In the case of multi-level thresholding of DI, the previous formula can be easily extended to calculated the asymmetric thresholds: LT and UT. The optimal thresholds [50] are chosen by maximizing

as follows: (11)

Where (12) with =

=

,

=

,

(13)

,

After estimating two thresholds of LT and UT the DI can be categorized into three main regions of NegC, NC and PozC and a Three-Class Change Map (TCCM) can be obtained using equation 14. So, not only asymmetric thresholding has more accuracy in change detection and measurement in comparison with symmetric method, but also it can determine the direction of changes (positive and negative changes). If the CD is only considered in binary form (including Change and No-Change classes), the equation 15 can produce the BCM.

(14)

(15)

Despite the fact that after estimating the two thresholds of LT and UT it will be possible to create the change map (BCM or TCCM) in each band of DI, but in this study the fuzzy logic is used to fuse the change map which was obtained from asymmetric thresholding of each band of DI. Fusion of change map with fuzzy logic can develop the thresholding technique further to support the multi-spectral images, can recognize the multiple changes and provide higher accuracy in change detection and measurement. This phase will be discussed in details in section 2.3.

2.3.Fuzzy Change Detection Since most real world problems encounter imprecise and uncertain data, fuzzy logic helps to accurate the methods in vague environment [51]. This issue is also raised in remote sensing for change detection problem. In this study, the fuzzy logic is utilized with following purposes: 1- fuzzy modeling of changes, 2- fusion of change map which is acquired through spectral bands of RS images and 3- fuzzy display of changes. The main reasons for employing the Fuzzy logic will be explained in details in following: 1. Although it is feasible to engender change map in each spectral band of DI after determining the LT and UT thresholds, but the restricted width and range of spectral bands of RS images will lead these bands to have a restricted capability in change detection and measurement. This issue can be explained in this way that different biophysical changes of surface are detectable in different part of electromagnetic spectrum. Thus, it is inevitable to use the entire data of all spectral bands of DI to have an accurate detection of multiple changes. The fusion of change map with fuzzy logic can develop the thresholding technique to support the multi-spectral images along with the feasibility to detect multiple changes with high performance. 2. The changes that take place on the surface, rarely have the crisp mode, so that a varying degree of changes can be observed between binary modes of “Change” and “No-Change”. A fuzzy system with modeling capabilities of vagueness and

imprecision can be a suitable method for modeling and visualizing the gradual changes of surface. The literature review in this regard indicates that a few studies have carried out on employing the fuzzy logic for CD application. In most of these studies for supervised approach, the concept of fuzzy logic was used to classify the multi-temporal images and detection of changes in procedure of comparison was conducted afterwards [52, 53]. The conducted research by Metermicht [41] can be addressed as a direct utilization of fuzzy logic. In this study, it has tried to fit the membership functions to histogram of change image and exhibiting the changes in fuzzy form in following stage. De Souza et al [54], have given the CD results in three classes of Low-changes, Medium-changes and High-changes by developing a fuzzy rule-base system in supervised mode. In unsupervised approach, the fuzzy logic is employed to calculate accurately the threshold in ADI [29, 30]. The studies by Huang and Liu can be addressed as popular works in this regard which are known as fuzzy thresholding techniques [46, 47].These techniques that has been formerly used for segmentation of images, are demonstrated to have more precise results in CD based on RS images in comparison with non-fuzzy techniques [29, 30]. Fig. 7 conveys the general system for fuzzy image processing methods. The input image after entering the system must be initially fuzzified using membership function and then following processes can be performed on the image. As the principals of fuzzy image processing are appropriately described in textbooks, the theoretical background is not in the focus point of this paper. For a comprehensive study of fuzzy logic, please refer to Tizhoosh [55].

Fig. 7. The general system for the fuzzy image processing technique [55].

2.3.1. Fuzzification Fuzzification, involves division of the input DI into fuzzy sets, each specified by a fuzzy membership function (MF). The definition of fuzzy MF is the kernel of applications of fuzzy logic theory. MF maps the inputs into the range of [0, 1] as a degree of membership. The selection of MFs and the width of each fuzzy subspace is certainly case dependent. In symmetric thresholding of ADI, for defining the MFs of two classes of “Change” and “NoChange”, the S-shaped and Z-shaped functions of Zadeh [56] are utilized [29]. In thresholding of DI, Metternicht [41] employed bell-shaped function to define the No-change fuzzy set. As it was noted, the histograms of DI is mainly following the multiple distributions and their asymmetric shape prevents a single threshold from appropriate detection of changed

areas. Despite the fact that the Gaussian and bell MFs achieve smoothness, but they are unable to realize asymmetric characteristic which are important in asymmetric thresholding. As it was stated before, the histogram of DI in developed technique is divided into three main classes of NegC, NC and PozC (see Fig. 6). Corresponding to three allocated classes, it is essential to design three fuzzy MFs. In current study, for asymmetric modeling contributed to histogram of DI the bi-Gaussian MF was utilized according to equation 16 to define the fuzzy set of No-Changes in order to acquire the most appropriate fit to histogram of DI. (16) μ

Where c

the centre,

is the left width and

is the right width of bi-Gaussian MF.

In µNC, the maximum membership degree corresponds to c (center of MF) which diminishes by moving toward the sides and equals to 0.5 in cross points. These two points are contributed to two thresholds of LT and UT that are obtained in thresholding phase (see Fig. 8). Therefore, two parameters of σ1 and σ2 can be determined automatically without involvement of expert as follows: (17)

,

In case that the RRN is carried out ideally, it is expected that the central peak point related to histogram of DI has accordance with zero DN. In this case, the c parameter will have zero value. The complement of µNC in right and left sides are known as positive changes’ MF (μ

) and negative changes’s MF (μ

) respectively (equations 18 and 19). (18)

μ

(19)

μ

These MFs, depicted in Fig.8. Fig. 8. The 𝛍 , 𝛍

, and 𝛍

MFs, designed for No-change, Negative-Change and

Positive-Change fuzzy sets, respectively.

After obtaining the MFs, the degree of membership for all of the fuzzy sets (comprising Nochange, Negative-Change and Positive-Change sets) can be calculated for each DN in DI. This process is pursued for all of the spectral bands in DI.

2.3.2. Inference The outcome of fuzzification stage is the fuzzy change map in each of spectral bands of DI which can independently illustrate the occurred changes in study area. In order to acquire the changes map in the highest level of information, it is inevitable to fuse the fuzzy change maps with each other. This issue is proceeded in inference. The Fuzzy Possibility Of Change (FPOC) membership degree in each digital number of DI is calculated by aggregating the all of No-change membership degrees using equation 20: (20) μ

Where the center,

weight of μ

(No-change MF for the αth band of DI), and

is the left width and

,

is

is the right width of No-change MF for αth band of DI.

In this paper, we consider all fuzzy change maps contribute equally, i.e.,

.

2.3.3. Fuzzy visualization of changes The changes on the LC occur almost gradually which results in emergence of changes in environment with different intensities. Hence, experts use linguistic variables instead of binary state for describing the intensity of changes. Although determining the membership degree for each pixel with respect to fuzzy no-change class in the range of [0, 1] makes it feasible to continuously display the intensity of changes, but as it was stated, experts utilize only a limited number of linguistic variables to describe the intensity of changes. This issue can be satisfied by discretization of FPOC. Fig. 9 illustrates a schematic procedure for discretization of FPOC with the main aim of fuzzy displaying of changes in 5 classes.

Fig.9. Process of discretization of FPOC with the aim of fuzzy displaying of changes.

2.3.4. Defuzzification Although the fuzzy displaying of changes in comparison with binary displaying is more closer to real world and like the type a human describe the surrounding environment, but depending on the application and circumstances, the binary displaying is preferable to fuzzy

one (and vice versa). Moreover, in order to evaluate the reliability of the change map, it is essential to compare it with the reference map which has a binary form. Due to the stated reasons it is inevitable to harden the fuzzy change map. This can be realized using standard defuzzification process. In this research the FPOC was converted to a BCM by placing the threshold at different FPOC- membership degrees. The values of 0.4, 0.5 and 0.6 were tested in order to find the best decision threshold. 3. Results and discussions The experimental results are presented in this section, after describing the used data set and study area. The data set is provided by a couple of acquired images on the Urmia Lake (Iran) by the ETM+ sensor (mounted on the Landsat-7 satellite) and TM sensor (mounted on the Landsat-5 satellite) in August 1999 and September 2010 respectively. This data set is characterized by a spatial resolution of 30m×30m and 7 spectral bands ranged from blue light to shortwave infrared (0.45~2.35 µm). It is worth noting that the 6th band of these images, which is in thermal infrared ranged, is not utilized due to low spatial resolution. The selected area is a section (960×534 pixels) of two full scenes, including southern part of Urmia Lake. The color composite of 4–3–2 spectral bands of the 1999 and 2010 images are shown in Fig. 10.

Fig.

10.

Images

of

Urmia

Lake

in

southern

part.

a)

false color composite (Bands 4, 3 and 2) of the Landsat ETM+ image acquired in August 1999; b) false color composite (Bands 4, 3 and 2) of the Landsat TM-5 image acquired in September 2010.

Urmia Lake, situated in northwestern corner of Iran, is one of the largest permanent hypersaline lakes in the world and the largest lake in the Middle East [57, 58]. During the last decade (since 2000), Urmia Lake has been shrinking significantly and its depth has fallen by almost seven meters compared to pre-2000 levels. Experts believe that surface flow diversions, groundwater extraction and the lack of efficient water management are the main causes [58]. Diminution of the lake water level will lead a remarkable reduction in lake area specially in the southern and eastern parts of the lake which are shallower than the northern half of the lake. Scientists have warned that this continuous water decline would lead to increased salinity, collapse of the lake’s food chain and ecosystem, loss of wetland habitat, wind-blown “salt storms,” alteration of local climate and serious negative impacts on local

agriculture and livelihoods as well as regional health not only in East and West Azerbaijan provinces of Iran, but also in neighboring countries such as Turkey, Iraq and Azerbaijan [59]. The land cover of study area comprises of five thematic classes as: water, bare land, wet-salty land, dried-salty land and vegetation including pasture and agriculture areas. In order to evaluate quantitatively the effectiveness of proposed method, a reference BCM was manually generated according to a detailed visual analysis of both the available optical RS images (see Fig. 10) and the DI with a ground surveying as following stage. Different color composites of the considered images were used to highlight all the portions of the changed area in the most appropriate practical way. The major changes that took place between the two acquisition dates, are: the conversion of lake bed to wet-salty and dried-salty lands, conversion of wet-salty lands to dried-salty lands, alteration of bare lands with driedsalty lands, destruction of vegetation in favor of bare and salty lands, conversion of bare lands to vegetation and transformation of bare and salty lands to water surfaces. This procedure resulted in a reference BCM comprising 9467 changed pixels, including all kinds of mentioned changes, and 7294 unchanged pixels. First of all, the August image was registered to the September one with an RMS error less than 0.3 pixel. Then the multi-spectral DI generated via pixel-by-pixel subtraction of coregistrated images. Analyzing the histogram of DI reveals significant radiometric differences between these images (Fig. 11 ). For unsupervised approach of CD, thresholding techniques in particular, being influenced by radiometric differences of multi-temporal images, makes a substantial importance for RRN process in these techniques. Fig. 11 conveys the effect of normalization on histogram of DI using ANN-based RRN.

Fig.11. Histogram of DI extracted from reference and raw subject images (dr) together with the histogram of DI extracted from reference image and normalized subject image (dnANN) produced with AAN-based RRN for bands1, 2, 3, 4, 5 and 7.

It can be concluded by scrutinizing Fig. 11 that ANN-based RRN could correct the radiometric differences between multi-temporal images in corresponding bands with a high quality. Fitting the central peak of histogram to zero DN and increasing the frequency of zero DN and adjacent values lead to a reduction and increase in histogram width and height respectively, where all these issues authenticate the high quality of RRN process [38]. It is expected that the consequence of RRN process will be observed in change detection and measurement results. This issue will be assessed in last part of this section.

After radiometric correction of images by ANN-based RRN, the proposed asymmetric thresholding of each individual band of DI is conducted to calculate the LT and UT thresholds. Table 1 represents the LT and UT thresholds along with obtained single threshold by non-fuzzy Otsu method.

Table 1. The optimum threshold(s) estimated by non-fuzzy symmetric thresholding (Otsu method) and asymmetric thresholding (proposed method). T: single threshold, LT: lower threshold and, UT: upper threshold. After determining the LT and UT thresholds in each band of DI, the fuzzy MFs are determined. Then, membership degree for each digital number of DI is calculated with respect to different classes of NegC, NC and PozC. The outcome of this stage is the Fuzzy change map in every band of DI. Fig.12 depicts the calibrated MFs in each spectral bands of DI.

Fig.12. Calibrated MFs to fuzzifiying the DI into NegC, NC and PozC fuzzy sets in all spectral bands of DI.

The restricted width and range of spectral bands in optical RS images makes their capabilities restricted in detection of different spectral changes of LC. The final FPOC is provided by fusing the obtained fuzzy change map from spectral bands of DI. In the following stage, in order to display the intensity of changes, the FPOC is discretized by linguistic variables (see Fig. 9). Fig. 13, illustrates the fuzzy display of changes in southern part of Urima Lake produced with the proposed approach. A scale is used to represent changes in 5 fuzzy change classes ranging from white to black which are representing the pixels with "extremely high changes" and pixels with an absolute certainty of "no change" respectively. In this way of visualization it is easy to visualize not only location of changed regions, but also the intensity of the changes in these regions.

Fig. 13. Fuzzy change map obtained with the proposed fuzzy measurement approach.

The disappearance of southern part of lake (conversion from lake bed to salty land), the increase in vegetation in the northwestern corner of study area (due to agricultural development) and dam watering (one of the 52 dams in operation) can be observed clearly in

this change map. Also, the other changes with different intensities, such as conversion of salty-area to bare land, and conversion of vegetated area to bare land can be seen in this change map. In the next step,

the fuzzy change map is transformed to BCM in

defuzzification phase. In order to investigate the capabilities of developed fuzzy measurement approach for automatic and unsupervised detection of changes, the outcomes of this method is compared with the results of known state-of-the-art non-fuzzy Otsu thresholding method. Fig. 14 illustrates the engendered BCM with the developed fuzzy measurement approach along with the acquired BCM by non-fuzzy Otsu thresholding. Fig. 14. Qualitative comparison of the CD results obtained with a) non-fuzzy Otsu’s method, and b) proposed fuzzy measurement approach on test images as shown in Fig. 10. Black and white colors represent the unchanged and the changed areas, respectively.

In addition to quality evaluation which can be realized by comparison of produced BCM by these techniques (Fig 14-a, b), we compared the performance of our proposed fuzzy measurement approach with the state-of-the-art non-fuzzy Otsu’s thresholding, quantitatively. In order to have a quantitative assessment, the accuracy of BCM is calculated and represented in Table 2 with respect to parameters of Overall Accuracy rate (the proportion of changed and unchanged pixels correctly detected- OA%), False Alarms rate (the proportion of unchanged pixels identified as changed ones - FA%), Missed Alarms rate (changed pixels categorized as unchanged ones - MA%), and Total Errors rate (The sum of FA and MA – TE%). Higher value of OA indicates better change detection, whereas for FA, MA, and TE, lower values point out the same.

Table 2. Change detection accuracy assessment of proposed fuzzy measurement approach versus non-fuzzy Otsu method, including Overall accuracy %, total errors%, false alarms %, and missed alarms %.

The main issues that can be inferred from Table 2 are: 1. The asymmetric thresholding of DI in proposed fuzzy measurement approach has more accuracy in CD in comparison with the symmetric non-fuzzy thresholding of ADI (the Otsu’s method). The accuracy of provided change map with proposed technique is higher than the Otsu in all spectral bands. As changes with high intensities such as alteration of lake bed to salty lands take place simultaneously

alongside changes with low intensities like changes of vegetation, the symmetric thresholding would not be able to detect these multiple changes with different intensities. This issue can be justified by domination of high intensity changes over lower ones. However, asymmetric thresholding in proposed fuzzy measurement approach makes it possible to detect changes with various intensities (multiple changes). The high values of OA and low value of FA and MA in proposed fuzzy measurement approach in comparison with the non-fuzzy Otsu method and also comparing the corresponding change maps (comparing Fig. 14-a with Fig. 14-b) authenticates this issue. 2. By evaluating the acquired BCM from various spectral bands of DI (see Fig.15-a to f and table 2) it is revealed that none of the spectral bands have the potential to detect the multiple changes. The changes related to vegetation are detected in 3rd and 4th bands, whereas the changes like conversion of bare lands to salty lands are detected in 2nd band. This issue can be contributed to the restricted width and range of spectral bands in optical RS images. The fusion of fuzzy change maps utilizing equation 20 can provide the detection of multiple changes to a high extent. The binarized version of FPOC using decision threshold=0.4 is illustrated in Fig 15-g. As is evident, the FPOC represents more information about occurred multiple changes, in comparison with acquired results from each spectral band. 3. The developed fuzzy measurement approach method has a remarkable capability in change detection and it could recognize all kinds of the occurred changes in study area. However, the known state-of-the-art non-fuzzy Otsu method could detect some of these changes. The overall accuracy of developed approach is 90.35% which is 15.64% higher than Otsu method. This issue can be justified by asymmetric thresholding instead of symmetric thresholding (which was explained in case 1) and also by fuzzy integration of change map (equation 20) instead of employing the magnitude of changes (equation 2).

Fig. 15. Qualitative comparison of different spectral bands of DI for proposed fuzzy change detection approach: a) Band 1, b) Band 2, c) Band 3, d) Band 4, e) Band 5, f) Band 7, and g) all spectral bands (FPOC).

It is worth noting that the conducted RRN process has a scant effect on the proposed technique which can be considered a remarkable advantage. This is due to the fact that in this

method the radiometric differences of multi-temporal images are implicitly modeled and the necessity for RRN process is obviated. On the contrary, the prevalent thresholding methods are severely sensitive to the quality of conducted RRN process. As the radiometric differences are high, these sensitivity will be higher too. In order to investigate the reliability of this issue, the change map is provided without conducting the RRN process. Fig. 16 represents The change detection results (BCM) for proposed fuzzy measurement approach and non-fuzzy Otsu methods with and without conducting RRN process.

Fig. 16. Comparison of CD results with and without conducting RRN process for differennt CD methods: a) non-fuzzy Otsu method without RRN, b) non-fuzzy Otsu method with RRN, c) proposed fuzzy measurement approach without RRN, and d) proposed fuzzy measurement approach with RRN.

Visual comparison of BCMs before and after RRN process indicates that in overall, RRN process had a positive impact on CD, but Otsu technique is affected by RRN process far more than proposed technique. In order to have a quantitative evaluation of this issue, similarity measure (SM) between BCMs before and after RRN process, BCM pre-RRN and BCMpost-RRN respectively, were determined by equation 21 and considered as sensitivity parameter for RRN process. SM illustrates the amount of similarity between two BCMs in the range of 0 and 1. As SM has a higher value, the more similarity between BCMs can be concluded before and after RRN process which demonstrates the scant influence of RRN on CD and vice versa. In the best condition SM will equals to 1 which represents the insensitivity of CD technique to RRN process. (21)

SM has the value of 0.87 in the developed fuzzy measurement approach which is remarkably high compared to non-fuzzy Otsu technique with 0.66 for SM. This issue indicates the low sensitivity of proposed method to RRN process. This fact conveys the capability of developed fuzzy measurement approach in change detection of multi-temporal images with high radiometric differences. In asymmetric thresholding of DI the following circumstances are implicitly modeled which obviate the necessity for an ideal RRN: the lack of symmetry in histogram of DI and lack of correspondence for central peak of histogram to zero DN which is a prevalent issue in utilization of multi-temporal images (see Fig. 11). The obviation of

necessity for ideal conduction of RRN process, which is not feasible in some cases, is a significant advantage for unsupervised CD techniques. 4. Conclusions In this study, an unsupervised fuzzy measurement approach for change detection (CD) in multi-temporal and multi-spectral remote sensing (RS) images is proposed based on asymmetric thresholding and fuzzy logic. The asymmetric thresholding of difference image (DI) facilitated the identification of changed areas with unchanged ones with higher accuracy, along with determining the parameters of fuzzy membership functions automatically. Furthermore, utilization of fuzzy logic realized the following issues: 1- the ambiguity in the boundaries between changed and unchanged areas in histogram of DI is considered, 2- by applying the fusion process, the data from all spectral bands of DI are employed in change detection and measurement, and 3- by fuzzy visualization instead of binary visualization, the intensity of changes are displayed in several fuzzy classes in addition to changed regions. The acquired qualitative and quantitative results from Landsat-5, 7 images confirmed the effectiveness of the proposed fuzzy measurement approach for the change detection and measurement, and visualization of the certainty and intensity of changes. The higher accuracy in CD, significant capability in detection of multiple changes and very low dependence on the quality of conducted relative radiometric normalization (RRN) are the major features of developed approach. The overall accuracy of obtained binary change map (BCM) by proposed fuzzy measurement approach is 90% which demonstrates a 15% improvement in overall accuracy compared with the acquired BCM by known state-of-the-art non-fuzzy Otsu method. In proposed technique the multiple changes are extracted, where in prevalent thresholding techniques such as Otsu, only the dominant changes are detected. The similarity of BCM in developed method before and after applying the RRN process equals to 0.87 which conveys the low sensitivity of proposed technique to the quality of conducted RRN. This similarity value is 0.66 for Otsu technique which indicates the high dependence of prevalent methods to the quality of conducted RRN. In the proposed fuzzy measurement approach, fusing the acquired fuzzy change maps from different spectral bands of DI, provided the feasibility of detecting the multiple changes and led to an increase in the accuracy of change detection and measurement. However, the equal weights of fuzzy change maps in fusion process, has led to an increase in false alarm (FA) rate. Regarding the influence of weights on fusion process, determining the optimum weights can have a significant impact on improving the accuracy of CD and reducing the FA error.

Utilizing the knowledge about study area and the natures of occurred changes along with the knowledge of experts in this application, is a strategy that will be taken in future studies for determining the optimum weights of fusion process.

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Table 1 The optimum threshold(s) estimated by non-fuzzy symmetric thresholding (Otsu method) and asymmetric thresholding (proposed method). T: single threshold, LT: lower threshold and, UT: upper threshold.

Spectral band(s)

symmetric thresholding (Otsu method) T

Band 1 (0.45-0.52 ) Band 2 (0.52-0.60 ) Band 3 (0.63-0.69 ) Band 4 (0.76-0.90 ) Band 5 (1.55-1.75 ) Band 7 (2.08-2.35 ) All Bands (using Eq.2)

52 37 54 49 72 60 52

asymmetric thresholding (proposed method) LT UT -33 -37 -39 -35 -48 -31 -

+18 +23 +44 +43 +61 +52 -

Table 2 Change detection accuracy assessment of proposed fuzzy measurement approach versus nonfuzzy Otsu method, including Overall accuracy %, total errors%, false alarms %, and missed alarms %.

CD approach

Otsu (non-fuzzy method)

Proposed fuzzy method

 

accuracy assessment parameters Overall Total False Missed Accuracy (%) Errors(%) Alarms (%) Alarms (%) 56.78 43.22 1.24 75.54 ADI-Band 1 ADI-Band 2 61.89 38.11 9.68 60.01 ADI-Band 3 81.24 18.76 0.36 32.95 ADI-Band 4 73.65 26.35 1.92 45.18 ADI-Band 5 66.42 33.58 0 59.46 ADI-Band 7 77.57 22.43 0.05 39.66 ADI-all bands 75.71 24.29 0.15 42.88 67.51 32.49 22.05 40.50 DI-Band 1 DI-Band 2 71.07 28.93 18.23 37.17 DI-Band 3 88.40 11.6 15.99 8.21 DI-Band 4 81.00 19.00 7.32 28.00 DI-Band 5 86.67 13.33 0.29 23.39 DI-Band 7 90.00 10.00 6.83 12.44 DI-all bands 90.35 9.65 10.47 9.02 Change Index

ADI-Band αdenotes the absolute difference image using the α-th bands of multi-spectral image (Eq.2) . DI-Band αdenotes the difference image using the α-th bands of multi-spectral image (Eq.20).