A Deep Learning Fusion Recognition Method Based On SAR Image Data

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Procedia Computer 00 (2019) 000–000 Computer Science 147 (2019) 533–541 2018 InternationalProcedia Conference onScience Identification, Information and Knowledge www.elsevier.com/locate/procedia in the Internet of Things, IIKI 2018 2018 International Conference on Identification, Information and Knowledge in the Internet ofRecognition Things, IIKI 2018 Deep Learning Fusion Method 2018AInternational Conference on Identification, Information and Based Knowledge in the Internet of Things, IIKI 2018 A Deep Learning Recognition OnFusion SAR Image Data Method Based

A, DONG Deep Learning Method OnFusion b SAR Image b ZHAI Jiaa,∗ Guangchang , CHENRecognition Fengb ,Data XIE Xiaodan , QiBased Chengmingc , Li d OnbSAR Image Lin ZHAI Jiaa,∗, DONG Guangchang , CHEN Fengb ,Data XIE Xiaodanb , Qi Chengmingc , Li a Science and Technology on Electromagnetic Scattering d China b b Laboratory, Beijing 100000, b LinFengLaboratory, b Science Guangchang ZHAI Jiaa,∗, DONG CHEN , XIE Xiaodan , Qi Chengmingc , Li and Technology on ,Optical Radiation Beijing 100000, China a Science and Technology c Beijing dBeijing 100000, on Electromagnetic Laboratory, Union University, ChinaBeijing 100000, China LinScattering b d Abstract

Science and Technology on Optical Radiation Laboratory, Beijing 100000, The Fourth Academy of China Aerospace Sience And Industry Corporation, BeijingChina 100000, China a Science and Technology c Beijing on Electromagnetic Scattering Laboratory, Union University, Beijing 100000, ChinaBeijing 100000, China b Science and Technology on Optical Radiation Laboratory, Beijing 100000, China d The Fourth Academy of China Aerospace Sience And Industry Corporation, Beijing 100000, China c Beijing Union University, Beijing 100000, China d The Fourth Academy of China Aerospace Sience And Industry Corporation, Beijing 100000, China

In view of the research status and existing problems of synthetic aperture radar (SAR) target recognition, a new Abstract method of deep learning fusion recognition is proposed. Firstly, the 1-D features extracted with principle component In view of the research existing synthetic aperture radar (SAR) target recognition, new Abstract analysis(PCA) are used status as theand input of theproblems stacked of autoencoder(SAE) network to extract deep features, awhich method deep recognition learning fusion recognition is proposed. Firstly, the 1-D extracted component achieves of target based on 1-D PCA feature data. Then, the features SAR target imageswith are principle used as the input of In view of theneural research status existing of synthetic radar (SAR) target recognition, new analysis(PCA) are used as theand input theproblems stacked autoencoder(SAE) network torecognition extract deep features, convolutional network(CNN) to of extract deep features, whichaperture achieves target based on 2-Dawhich SAR method of deep learning fusion recognition is proposed. Firstly, the 1-D features extracted with principle component achieves targetdata. recognition 1-D PCA feature algorithm data. Then, the SAR target are used as the input of image feature Finally, based a deep on learning recognition of decision-level andimages feature-level fusion is proposed analysis(PCA) are used as the input thedata. stacked autoencoder(SAE) network torecognition extract deep features, convolutional neural network(CNN) to of extract deep which achieves target based on 2-D SAR for the different kinds of SAR image feature Thefeatures, experiment analysis shows that the proposed method ofwhich deep achieves target recognition based on 1-D PCA feature data. Then, the SAR target images are used as the input of image feature data. Finally, a deep learning recognition algorithm of decision-level and feature-level fusion is proposed learning fusion recognition in this paper is adaptive and robust to the attitude angle, background and noise. convolutional neural to extract deep which achieves target recognition based on 2-D for the different kindsnetwork(CNN) of SAR image feature data. Thefeatures, experiment analysis shows that the proposed method of SAR deep image feature data. Finally, in a deep learning algorithm decision-level and feature-level is proposed learning fusion recognition this is recognition adaptive and robust toofthe attitude angle, background fusion and noise. © 2019 The Authors. Published bypaper Elsevier B.V. for the different kinds of SAR image feature data. The experiment analysis shows that the proposed method of deep This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nclearning fusion recognition in this is adaptive and robust to the attitude angle, background and noise. © 2019 The Authors. Published bypaper Elsevier B.V. nd/4.0/) This an Authors. open article under the CC BY-NC-ND (https://creativecommons.org/licenses/by-ncPeer-review under access responsibility of the B.V. scientific committee oflicense the 2018 International Conference on Identification, © 2019isThe Published by Elsevier © 2019 Authors. Published by Internet Elsevier B.V. nd/4.0/) Information and Knowledge in the of Things. This is anThe open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) This is an under open access article thecommittee CC BY-NC-ND (https://creativecommons.org/licenses/by-ncPeer-review under responsibility ofscientific the scientific committee oflicense the 2018 International Conference on Information Identification, Peer-review responsibility of theunder of the 2018 International Conference on Identification, and nd/4.0/) aperture radar recognition, principle component analysis(PCA), stacked autoencoder Keywords: Information and Knowledge in the (SAR), Internettarget of Things. Knowledge insynthetic the Internet of Things. (SAE), convolutional neural network(CNN) Peer-review under responsibility of the scientific committee of the 2018 International Conference on Identification, Keywords: aperturein radar recognition, principle component analysis(PCA), stacked autoencoder Information synthetic and Knowledge the (SAR), Internettarget of Things. (SAE), convolutional neural network(CNN)

Keywords: synthetic aperture radar (SAR), target recognition, principle component analysis(PCA), stacked autoencoder (SAE), convolutional neural network(CNN)

1. INSTRUCTION

1. With INSTRUCTION the development of large-scale integrated circuits and high-performance electronic devices, synthetic aperture radar (SAR) imaging technology has gradually matured. As an active sensor using microwave 1. INSTRUCTION With the development of large-scale integrated circuits and high-performance electronic devices, synthetic aperture radar (SAR) imaging technology has gradually matured. As an active sensor using microwave With the development of large-scale integrated circuits and high-performance electronic devices, synthetic ∗ Corresponding author. Tel.: +18511332608 ; fax: +0-000-000-0000. aperture radar (SAR) imaging technology has gradually matured. As an active sensor using microwave E-mail address: [email protected]

Corresponding author. Tel.: +18511332608 ; fax: +0-000-000-0000. 1877-0509 2019 The Authors. Published by Elsevier B.V. E-mail © address: [email protected] ∗ Corresponding author. Tel.: +18511332608 ; fax: +0-000-000-0000. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) E-mail © address: [email protected] Peer-review under responsibility of the scientific committee 1877-0509 2019 The Authors. Published by Elsevier B.V. of the 2018 International Conference on Identification, Information and Knowledge the Internet of Things. This is an open in access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) 1877-0509 ©© 2019 Authors. Published Elsevier B.V. 1877-0509 2019The The Authors. Published by Elsevier B.V. of the 2018 International Conference on Identification, Information Peer-review under responsibility of thebyscientific committee This is article thethe CCCC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) This is an an open openaccess access articleunder under BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) and Knowledge in the Internet of Things. Peer-review under responsibility of the scientific committee of the 2018 International Conference on Identification, Information and Peer-review under responsibility of the scientific committee of the 2018 International Conference on Identification, Information Knowledge in the Internet of Things. and Knowledge in the Internet of Things. 10.1016/j.procs.2019.01.229 ∗

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sensing, SAR imaging is less affected by weather, illumination and other conditions. It can be used for allweather, all-day, long-distance observation of the target of interest, thus becoming a main detection means of target and environment sensing. With the improvement of SAR imaging technology and the increasing resolution accuracy of SAR, how to interpret and utilize these SAR images has become an urgent problem. Among them, SAR automatic target recognition is the most important research branch of SAR image interpretation technology, which has great application value in military and civilian. At present, there are two kinds of SAR target recognition methods, one is based on image matching[1], the other is based on feature matching [2] [3] [4] [5]. The method based on image matching can realize SAR target recognition by finding the best match between the measured image and the stored target template image. However, this method has some problems, such as huge template data and low efficiency of matching computation, which cannot meet the real-time processing requirements of airborne and space borne SAR platforms. Based on the prior knowledge and model of target electromagnetic scattering characteristics and mechanism, the method based on feature matching is used to realize SAR target recognition through feature extraction, feature reduction and feature matching in the original data. However, this method has some difficult problems, such as feature sensitivity, multi feature optimization, effective association and integration. With the successful application of deep learning in optical image recognition [6] [7], the research work of SAR target recognition based on deep learning has also begun [8] [9]. The recognition technology based on deep learning has the ability of multi-level feature expression and nonlinear data fitting. It can automatically mine the features with good applicability and solve the problem of multi-feature fusion and multi-source information fusion. Xu Feng [10] applies the deep convolutional neural network to the SAR target classification dataset MSTAR, and the average classification accuracy of the 10 classes of targets is over 99%. For polarimetric SAR images with phase, this paper proposes a complex deep convolutional neural network, which is applied to the classification of polarimetric SAR images, and the average classification accuracy of Flevoland 15 objects is 95%. However, the current research of SAR target recognition based on deep learning mainly focuses on data enhancement. Low complexity network is designed for specific problems and over-fitting is avoided, but less consideration is given to background noise, attitude angle and other factors. Due to the coherent imaging mechanism of SAR system, the original SAR image contains not only the target of interest, but also the unavoidable existence of non-Gaussian coherent speckle noise, and background noise will seriously affect the recognition performance. Unlike optical images, SAR images are very sensitive to the changes of target pose (pitch angle and azimuth). When the relative position of SAR and the target is changed, the scattering center of the target will also change, which leads to the obvious difference between the target in different attitudes. The difference between the SAR images of the same target in different attitudes may be greater than the difference between the SAR images of different targets in the same attitude. Therefore, this paper proposes a deep learning fusion recognition method based on SAR image features by extracting PCA features and deep learning features, combining stacked autoencoder (SAE) and convolutional neural network (CNN). Experimental results show that the proposed deep learning fusion method has good adaptability and robustness to attitude angle, background and noise. 2. A DEEP LEARNING RECOGNITION METHODS BASED ON PCA OF SAR DATA 2.1. Feature Extraction of SAR Image with Target Principal Component Analysis Principal component analysis (PCA) is a commonly used mathematical transformation and dimensionreduction method, which transforms a given set of related variables into another set of unrelated variables by linear transformation. These variables are arranged in descending order of variance, and the total variance of the variables is kept constant in the mathematical transformation. PCA is a multivariate data analysis method which uses a number of linear independent principal components to replace the original input data, ensuring maximum retention of information contained in the original input data. In order to obtain the principal components, it is necessary to ensure that each variable is zero when using the PCA. For SAR target data X = (x1 , ..., xn )Rd×N , d is the number of pixels of the SAR target slice,



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and N is the number of training samples, i.e. each sample xi is a vector with d elements. The covariance matrix of X can be obtained using the following formula: Cov =

(1)

1 T N X X,

where Cov is a symmetric matrix and the input matrix X is a real number. Then the orthogonal transformation is used to get the diagonal matrix or the real matrix, which can be expressed as the following form: (2)

λ = AT gCovgA,

where the element in λ represents the eigenvalue of Cov , and each column in A represents a feature vector of Cov , where each eigenvalue in λ corresponds to a feature vector at the corresponding position in A. And the total number of columns of A is p. In PCA, λi can be represented as the contribution value of the original data X represented by the principal component corresponding to this feature, and the percentage of λi in λ can be expressed as the contribution rate of the original input data X represented by the principal component corresponding to the eigenvalue, which is expressed as the following form: ηi =

λ p i

j=1

λj

(3)

× 100(%).

When the PCA is applied, all feature vectors are sorted in descending order according to the corresponding contribution rate, the first c feature vectors are combined into the transformation matrix U = [u1 , u2 , u3 , ..., uc ] . Finally, the SAR feature vector set Yout after PCA is obtained by using formula (4). (4)

Yout = XU.

The whole process of PCA is completed after the above process, which the SAR feature vector set with less dimension is used to maximize the retention of the original SAR image information. 2.2. A Target Recognition Method of Stack Autoencoder Based on PCA Feature of SAR Image Autoencoder is an unsupervised feature learning method that automatically learns the necessary features from sample data which is not labeled. A simple autoencoder is composed of three layers containing the encoding and decoding two parts, and its output target value is the value of input itself. In the part of encoding, the input is a vector of d dimensions, assuming x ∈ d , the x is encoded by the hidden layer and can be represented as (5)

h = α(W x + b), 

where h is a vector of d dimensions, which is represented as h ∈ d . W is the weight matrix with d × d.  b is a bias vector of d . α(g) indicates the activation function of the hidden layer. In the part of decoding, the main purpose of decoding is to reconstruct the original input in the autoencoder. The input in decoding is the output h of the hidden layer, and the decoded output is set to y, which can be expressed as 





(6)



y = β(w h + b ),

where y is a vector of d dimensions which is represented as y ∈ d . W is the weight matrix with d × d. b is a bias vector of d. β(g) indicates the activation function of the output layer. The main purpose of the autoencoder is the refactor of output by the input with as little error as possible, so we can define the following penalty function 

J = 12  y − x 2 .



(7)

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After the entire self-encoder is trained completely, we can think that the vector h is a feature vector that can represent the input well. The stack autoencoder (SAE network) is a neural network composed of multilayer autoencoders. The entire depth network is divided into unsupervised pre-training and supervised fine-tuning. First, the parameters of each layer of the network are initialized randomly. After the hidden layer features learned by each layer are represented, they are used as inputs of the next layer. Then, the next layer is self-encoded to achieve unsupervised training for each layer of the network. The Softmax classifier is used to output the category of the target image, then the gradient descent method is used to adjust the whole network by tagged data, which realizes supervised fine-tuning of the network. The output layer uses the Softmax function σ(z) j =

ez j λ k=1

ezk

.

The layer l output of the network can be expressed as  Z l k (n) = σ( λj=1 Zk l−1 · W l j,k + bk l (n)),

(8)

(9)

where l is the network layer, w is the network weight, b is bias, and σ is a tanh. The final output of the network can be expressed as zk (n) = tanh(zk 3 (n)).

The loss function of the entire network is defined as  LΘ = λ1 λj=1 Z j · lnX j + X j · lnZ j .

(10)

(11)

The frame of the stack autoencoder target recognition method based on the PCA features of SAR image is shown in Figure 1. The random Gaussian noise is added to the original SAR target image, which makes the network have stronger anti-noise ability. After image slices are descended dimension by PCA, a onedimension vector is obtained. As the input, the one-dimension vector is extracted the depth feature by the SAE network. The SAE network uses an autoencoder structure with four hidden layers where the number of neurons is 1600, 160, 16, 3, and the last layer uses the softmax to obtain the probability of each class. This paper designs a recognition framework based on the deep learning feature of SAR images, which is implemented as follows. 1) Build a convolutional neural network (CNN) which consists of 5 convolutional layers, 3 pooled layers, 1 dropout layers, and a Softmax output layer, as shown in Figure 2. Each of the first two convolutional layers contains 32 convolution cores, the third convolution layer contains 64 convolution cores, and the first three convolution cores are 5×5, 5×5 and 6×6 respectively. The first three convolutional layers are connected by maximum pooling layers of which size is 2 and stride is 2 pixels. The fourth convolution layer has 128 convolution cores, the convolution core size is 5×5, and then the dropout layer is added. The fifth convolution layer has 5 convolution cores, and the convolution core is 3×3, of which output contains 5 neurons. Finally, the Softmax layer is connected to obtain the probability of classification category. Relu is used as the activation function, and all convolution strides are 1 without edge expansion. There is no fully connected layer in the network structure, which the network parameters are reduced, and the dropout operation is added after the second-to-last convolution layer to prevent the overfitting. 2) For training set data, the mean data is first subtracted from the original data. The deep learning platform is tensorflow1.2, the learning rate is set to 1e-3, the number of samples per batch is 100, the maximum iteration batch is 1000 epoch, the optimization method is the random gradient descent method, and the early stop is set. When the correct rate of test set is greater than 0.97, the learning rate decreases to 1/10 times, and the optimal model is preserved finally. 3) For test set data, mean data is first subtracted from the original data, then is fed into the trained model in 2) to classify. Softmax outputs probability, and the SAR target category decision probability is recorded. Finally, the label with maximum probability is took as the final classification result.

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5

537

Fig. 1. Deep learning recognition framework based on SAR PCA feature

Fig. 2. CNN framework

3. DEEP LEARNING FUSION RECOGNITION METHOD BASED ON THE FEATURES OF SAR IMAGE The deep learning fusion recognition method based on the features of SAR image can be divided into the decision-level fusion and feature-level fusion. 3.1. Deep Learning Recognition Method of Decision –level Fusion The deep learning recognition method of decision-level fusion is as follows. 1) Use the one-dimensional vector test data is obtained by preprocessing the SAR image with PCA as input, and extract the depth feature through the trained SAE network to obtain the class judgment probability P1 ;

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Fig. 3. Feature fusion framework flowchart

2) Use the data which subtract the mean data from the SAR image testing data as input, and extract the depth feature through the trained CNN network to obtain the class judgment probability P2 ; 3) The decision probability of the two deep learning network output is weighted and fused. The probability fusion weight parameter is set as 0.5 by experiments verifying, and the final fusion judgment probability P is: P = α · P1 + (1 − α) · P2

(12)

4) Take the label of the highest probability in the fusion judgment probability P as the result of fusion classification. 3.2. Deep Learning Recognition Method of Feature-level Fusion The deep learning recognition method of feature-level fusion is implemented as follows. 1) Use the PCA feature training data of the SAR image as input. The depth feature is extracted through the SAE network, and the output of the fourth layer is taken as a one-dimensional PCA feature vector Fea1; 2) Use the data which subtract the mean data from the SAR image as input. Extract the depth features through the CNN network, and take the output of the fourth layer convolution layer as the two-dimensional CNN feature vector Fea2; 3) Connect the vector Fea1 and the vector Fea2 directly; 4) Design a fully connected neural network with two-layer, the number of neurons in the first layer is 256, and the number of neurons in the second layer is 3, followed by the Softmax classifier; 5) Train the fully connected neural network in 4) with the fusion feature vector obtained in 3), and saving the optimal model; 6) With the steps 1) 2) 3), extract the fusion feature vector Fea of the test data; 7) Input the fusion feature vector into the trained two-layer fully connected neural network in 4), and output the final classification result by the Softmax layer.



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4. EXPERIMENTAL ANALYSIS For a deep learning recognition method based on principal component analysis features with SAR image, a deep learning feature recognition method based SAR image, and two deep learning fusion recognition methods based on SAR image features, this paper use MSTAR data to carry out algorithm experiments under different elevation angles, backgrounds and noise conditions. The sensitivity analysis is carried out and relevant conclusions are drawn as follows.

4.1. Influence of Overlooking Angle on SAR Target Recognition Accuracy In order to test the influence of the overlooking angle on the accuracy of SAR target recognition, we divide the MSTAR data into the following two cases. 1) Use the target image with depression angle of 17° as the training data and test data to measure accuracy of the SAR target recognition based on deep learning. The MSTAR images with depression angle of 17° are used as samples, which are five categories: 2S1, BMP2-9563, BRDM2, BTR60, and BTR70-C71, and the number of samples per class is 299, 233, 298, 256, 233. For the stack autoencoder target recognition framework based on PCA feature of SAR image, cut 60×60 target slices centering on each sample target center and use different step sizes to translate to obtain 125000 samples. For the recognition framework based on deep learning feature of SAR image, the target slice of 88×88 is cut and use different step sizes to translate to obtain 13,500 samples. 80% of the sample is used as the training set and the remaining 20% is used as the testing set. 2) By using the target image with depression angle of 17° as the training data and using target image with depression angle of 15° as the test data, the accuracy of SAR target recognition based on deep learning is tested. The samples with depression angle of 17° in 1) are used as training data. The target images with depression angle of 15° are used as the test set. For the stack autoencoder target recognition framework based on PCA feature of SAR image, cut 60×60 target slices centering on each sample target center. For the recognition framework based on deep learning features of SAR image, the target slice of 88×88 is cut. Then, we get 1134 test data respectively. And the experimental results are shown in Table 1. The experimental results show that the accuracy of SAR target recognition using 17° training data and 15° test data is reduced compared with the training and test data of which the depression angles are both 17°. The 2° difference leads to the rapid decline of the recognition rate of the stack autoencoder target recognition framework based on PCA feature, but has little effect on the recognition framework and fusion framework based on the deep learning features of SAR image. In practical applications, the difference between the attitude angles of the training data and the test data will be greater. The deep learning fusion recognition method proposed in this paper can make more use of the different feature information of SAR image data and has better adaptability to the change of depression angle. Table 1. SAR target recognition rate at different depression angle. test/training set depression angle chosen

17°training 17°test

17°training 15°test

Target recognition rate of stack self-encoder based on PCA features of SAR image Recognition rate based on deep learning features of SAR images Feature-level fusion framework Recognition rate Decision-level fusion framework Recognition rate

0.9829 0.9930 0.9866 0.9964

0.9471 0.9815 0.9533 0.9815

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4.2. Influence of Background on SAR Target Recognition and Detection Accuracy The one-dimensional vector data formed by the SAR image loses the neighborhood information of the target in the image, so the target recognition accuracy is deeply affected by the surrounding background of the target. Considering the impact of the surrounding background of the target, we take the following two measures. 1) PCA dimensionality reduction for one-dimensional vector data of SAR images If the influence of the surrounding background of the target is not considered, the one-dimensional vector data corresponding to the SAR image is directly trained by the SAE network, and the recognition accuracy of the test set is only 91.36%. After PCA is used for dimensionality reduction, the characteristic information of the original data is preserved, which can improve the convergence speed and robustness of the algorithm. Experiments show that when PCA reduces the one-dimensional vector data corresponding to the SAR image to 256 dimensions, the recognition accuracy of the test set can reach 96.78%. 2) Perform translation transformations on the target images to decrease the influence of the background around the target The one-dimensional vector data corresponding to the SAR image loses the domain information of the target and the SAE network does not have translation invariance to the one-dimensional input, so the target center position and the surrounding background of the target have a great influence on the target recognition. Performing translation transformations on the target images, the influence of the background around the target can be suppressed. Experiments show that for the one-dimensional vector data corresponding to the SAR image, the detection and recognition accuracy of the target in the background image is reduced by nearly 80% compared with the translation transformation. 4.3. Noise sensitivity analysis A certain ratio of pixel points is randomly selected in the SAR slice images, and the pixel value of the point is substituted by the pixel value which is independent evenly distributed. Then test the influence of the SAR image noise on the recognition accuracy of the deep learning. The experimental results are shown in Table 2. The experimental results show that the target recognition accuracy decreases after adding random noise. The recognition rate of the CNN frame decreases rapidly with the addition of noise, because convolution is based on feature extraction of local image blocks. However, the one-dimensional frame is affected only by the pixels that are replaced, and the noise has less influence on the target recognition. The experimental results also show that the proposed SAR target recognition method based on deep learning is robust to noise. When the noise pixel ratio is lower than 4%, the target recognition rate can reach 90% or more. Table 2. Parameter values Noise pixel ratio % Target recognition rate of stack self-encoder based on PCA features of SAR image Deep recognition feature recognition rate based on SAR image Feature level fusion recognition rate Decision-level fusion recognition rate

1

3

4

5

10

15

0.9471

0.9427

0.9506

0.9533

0.8739

0.7566

0.9709 0.9462 0.9806

0.9241 0.9109 0.9594

0.8730 0.8457 0.9691

0.8201 0.8236 0.9489

0.5670 0.5326 0.7954

0.4012 0.3677 0.5970

4.4. Applicability analysis of the above identification methods The CNN network has translation invariance to the input and can extract the deep features of the SAR image, so, the convergence speed is fast during training, and it is robust to background and noise showing good generalization performance. The detection and recognition accuracy of SAR target recognition can



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reach more than 98%. The one-dimensional PCA feature loses the domain information of the image, which leads to the decrease of target recognition accuracy of the deep learning recognition framework based on SAE network. The recognition accuracy for the target slice is 94.71%, and the detection and recognition accuracy for the target in the background is 93.39%. The experimental results show that the target recognition accuracy based on CNN can achieve almost the ultimate in MSTAR images, and it is difficult to achieve substantial improvement in both feature-level fusion and decision-level fusion framework. However, the deep learning fusion recognition method based on SAR image features has better complementarity and applicability when the SAR image is affected by noise in practical application, which is of great significance for SAR target detection and recognition. 5. CONCLUSION Traditional SAR target recognition methods need special feature extraction module to construct pattern recognition system. There are many problems such as huge template data and sensitive features. In this paper, one and two-dimensional features of SAR images are extracted that based on the theory of target electromagnetic scattering, combined with SAE and CNN Networks, a deep learning fusion recognition method based on SAR image features is proposed. Experiments on MSTAR data show that the deep learning fusion recognition method proposed in this paper has good adaptability and robustness to attitude angle, background and noise, and target recognition rate can still reach more than 90% in the absence of twodimensional SAR data. In practical application, the target and background of SAR are highly complex and diverse, even if there are some differences in configuration and structure between two different targets belonging to the same category, the training data can never represent all the situations in the real world. The deep learning fusion recognition method based on SAR image features has better complementarity and applicability, which is of great significance for SAR target variant recognition with incomplete data sets. 6. REFERENCES References [1] Zhao Q, Principe J C. (2001) “Support vector machines for SAR automatic target recognition[J]. IEEE Transactions on Aerospace and Electronic Systems, 37 (2):643-54. [2] Sun Y, Liu Z, Todorovic S. (2007) “Adaptive boosting for SAR automatic target recognition[J]. IEEE Transactions on Aerospace and Electronic Systems, 43 (1). [3] Jianxiong Z, Zhiguang S, Xiao C. (2011) “Automatic target recognition of SAR images based on global scattering center model[J]. IEEE Transactions on Geoscience and Remote Sensing, 49 (10):3713-29. [4] Park J I, Park S H, Kim K T. (2013) “New discrimination features for SAR automatic target recognition[J]. IEEE Geoscience and Remote Sensing Letters, 10 (3):476-80. [5] Clemente C, Pallott L, Prou I. (2015) “Pseudo-Zernike-based multi-pass automatic target recognition from multi-channel synthetic aperture radar[J]. IET Radar, Sonar & Navigation, 9 (4):457-66. [6] Krizhevsky A, Sutskever I, Hinton G E. (2012) “Imagenet classification with deep convolutional neural networks[J]. Advances in neural information processing systems, 1097-1105. [7] He K, Zhang X, Ren S. (2016) “Deep residual learning for image recognition[C]. The IEEE conference on computer vision and pattern recognition,770-778. [8] Ding J, Chen B, Liu H. (2016) “Convolutional neural network with data augmentation for SAR target recognition[J]. IEEE Geoscience and remote sensing letters, 13 (3):364-8. [9] Chen S, Wang H, Xu F. (2016) “Target classification using the deep convolutional networks for SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 54 (8):4806-17. [10] Xu F, Wang H P, Jin Y Q. (2017) “Deep learning as applied in SAR target recognition and terrain classification[J]. Journal of Radars,6 (2): 136-148.