Binary Morphological Filtering of Dominant Scattering Area Residues for SAR Target Recognition

Abstract

A synthetic aperture radar (SAR) target recognition method is proposed in this study based on the dominant scattering area (DSA). DSA is a binary image recording the positions of the dominant scattering centers in the original SAR image. It can reflect the distribution of the scattering centers as well as the preliminary shape of the target, thus providing discriminative information for SAR target recognition. By subtracting the DSA of the test image with those of its corresponding templates from different classes, the DSA residues represent the differences between the test image and various classes. To further enhance the differences, the DSA residues are subject to the binary morphological filtering, i.e., the opening operation. Afterwards, a similarity measure is defined based on the filtered DSA residues after the binary opening operation. Considering the possible variations of the constructed DSA, several different structuring elements are used during the binary morphological filtering. And a score-level fusion is performed afterwards to obtain a robust similarity. By comparing the similarities between the test image and various template classes, the target label is determined to be the one with the maximum similarity. To validate the effectiveness and robustness of the proposed method, experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR) dataset and compared with several state-of-the-art SAR target recognition methods.

Discrimination and Correlation Analysis of Multiview SAR Images with Application to Target Recognition

“…In 2001, Zhao and Principe first introduced SVM into the field of SAR target recognition. Afterwards, it was widely used to classify different types of features as reported in [2,3,7]. With the objective to minimize the structural risk, SVM is able to find a hyperplane to separate two different patterns.…”

“…Discriminative features are first extracted from SAR images, e.g., geometrical properties, scattering characteristics, or transformation features. In [2,3] the geometrical features were used for SAR target recognition like target region and contour. e attributed scattering centers were adopted as the basic features in [4,5], which were matched for target recognition.…”

Head CT Image Convolution Feature Segmentation and Morphological Filtering for Densely Matching Points of IoTs

“…Therefore, it is suitable for parallelization and split calculation of the algorithm. The CT is placed in the space coordinate system with the coordinate axis as the edge, and the CT center is the intersection of the two virtual ray centerlines [33]. The distance from the source to the intersection of the rays is SAD, and the distance from the source to the plane coordinate system is SID.…”

Target Recognition of Synthetic Aperture Radar Images Based on Two-Phase Sparse Representation

“…Various types of features are extracted to convey the targets' characteristics in SAR images. The geometrical features are used to describe the physical sizes or shapes of the target such as target contour [6,7], region [8][9][10][11], and shadow [12]. The projection features are extracted to depict the intensity distribution of the target's image using mathematical transformations (e.g., principal component analysis (PCA) [13], nonnegative matrix factorization (NMF) [14], and other manifold learning algorithms [15][16][17]) or signal processing techniques (e.g., wavelet [18] and monogenic signal [19,20]).…”