Study On SAR Images Target Recognition

Automatic target recognition (ATR) based on synthetic aperture radar (SAR) images is of great importance in the modern battlefield and has become a very hot research topic. In recent years, ATR based on SAR images has made great progress in related techniques including SAR images preprocessing, feature extraction, classifier design, and so on. This dissertation provides our researches for SAR target recognition, which are supported by Advanced Defense Research Programs of China and Natural Science Foundations of China.The main content of this dissertation is summarized as follows:1. In the first part, several SAR images filtering methods are first analyzed and compared according to the characteristics of the SAR speckles, and then the filtered results of some SAR images with different resolutions are presented.2. To solve the problems in many literatures, a SAR ATR method based classifier fusion is proposed where target and shadow images are first segmented via SAR image pre-processing, and then the shape information of target and its shadow and the intensity distributed information of a target are extracted based on polar mapping, and shape descriptors of a target and its shadow are also extracted, finally SAR targets are classified by the combined classifier based on the average rule.3. Principal component analysis (PCA) is a classical method in the pattern recognition area. However, when PCA is used to feature extraction for 2D images, 2D image matrices need to be transformed into 1D image vectors, this will bring on some problems such as a loss of 2D space structure information, disaster of dimensionality, etc. To solve these problems, 2DPCA is proposed recently where the projection directions are sought out from 2D image matrices, thus being more efficient. In this dissertation, 2DPCA is divided into right 2DPCA (R-2DPCA) and left 2DPCA (L-2DPCA) according to the ways of projection. To overcome the problem of more features of 2DPCA, we present four improved methods which not only can reduce feature dimensions but also can improve recognition performances.4. Linear discriminant analysis (LDA) is also a popular feature extraction method in the pattern recognition field. Similar to PCA, when LDA is used to 2D images recognition tasks, some problems (such as a loss of 2D space structure information, disaster of dimensionality, singularity, etc) will occur. Therefore, 2DLDA is presented to solve the above problems, which constructs the scatter matrices based on 2D image matrices. 2DLDA can also be divided into right 2DLDA (R-2DLDA) and left 2DLDA (L-2DLDA). However, a drawback of R-2DLDA and L-2DLDA is that they need more features. To overcome this problem, we propose three improved approaches, two-stage R-2DLDA, two-stage L-2DLDA and two-directional 2DLDA.5. LDA is a popular method for linear dimensionality reduction. LDA assumes that all the classes obey the unimodal distribution, singularity usually occurs when used to 2D image recognition tasks, and the dimensionality of features obtained by LDA is only c ? 1 ( c is the total number of classes). To alleviate the above-mentioned limitations, clustering-based discriminant analysis (CDA) is presented recently. In this method, k-means algorithm is employed for finding the clusters of each class, thus the final recognition results are unstable, not optimal and depend on the initial cluster centers. In this dissertation, we propose an improved CDA (ICDA) where the fast global k-means clustering algorithm is employed for finding the optimal cluster structures, thus the final results are stable and optimal.6. This section presents several image feature extraction methods, which aim at dealing with the multimodal distributions of SAR images. The main work concerns the following three aspects: (1) Two-dimensional CDA (2DCDA) and its improved algorithms are developed. 2DCDA constructs the cluster scatter matrices from 2D image matrices, thus overcoming the problems (such as disaster of dimensionality, singularity, etc.) of CDA, and hence 2DCDA combines the capability to model the multiple cluster structures embedded within a single class with the computational advantage that is characteristic of 2D subspace analysis methods, such as 2DPCA and 2DLDA. In this section, 2DCDA is also divided into right 2DCDA (R-2DCDA) and left 2DCDA (L-2DCDA). Moreover, in order to solve the problem of too much features of 2DCDA, we propose four improved algorithms, two-stage R-2DCDA, two-stage L-2DCDA, two-directional 2DCDA ((2D)2CDA), and generalized 2DCDA (G2DCDA). (2) Two-dimensional maximum clustering-based scatter difference discriminant analysis (2DMCSD) and its improved algorithms are proposed. In 2DCDA, the inverse matrix of the within-cluster scatter matrix has to be calculated, however, usually the inverse matrix does not exist in"small sample size"(SSS) problems. To solve this problem, we propose a novel image feature extraction method, 2DMCSD, which adopts the difference of between-cluster scatter and within-cluster scatter as the discriminant criterion for finding the projection vectors. So it not only can deal with the multimodal distribution problems but also is capable of avoiding the inverse matrix calculation and the"SSS"problems. Besides, an alternative 2DMCSD is presented. Two-directional 2DMCSD ((2D)2MCSD) discriminant analysis is also developed for further dimensionality reduction. (3) Diagonal CDA (DiaCDA) is proposed. In 2DPCA, 2DLDA and 2DCDA, the projection vectors only reflect the variations between rows (or columns) of images while the omitted variations between columns (or rows) of images may be also useful for recognition. To preserve the correlations between variations of both rows and columns of images, diagonal CDA (DiaCDA) is proposed, which seeks the optimal projection vectors from the diagonal images and takes into account the possibility of embedded multiple cluster structures within a single class. However, an inextricable problem of DiaCDA is that it requires vast memory for representation of images. To alleviate this problem, we combine DiaCDA with 2DCDA (DiaCDA+2DCDA).