SAR Image Target Recognition Based on Improved Residual Attention Network

Aiming at the multiple target recognition problems in large-scene SAR image with strong speckle, a robust full-process method from target detection, feature extraction to target recognition is studied in this paper. By introducing a simple 8-neighborhood orthogonal basis, a local multiscale decomposition method from the center of gravity of the target is presented. Using this method, an image can be processed with a multilevel sampling filter and the target’s multiscale features in eight directions and one low frequency filtering feature can be derived directly by the key pixels sampling. At the same time, a recognition algorithm organically integrating the local multiscale features and the multiscale wavelet kernel classifier is studied, which realizes the quick classification with robustness and high accuracy for multiclass image targets. The results of classification and adaptability analysis on speckle show that the robust algorithm is effective not only for the MSTAR (Moving and Stationary Target Automatic Recognition) target chips but also for the automatic target recognition of multiclass/multitarget in large-scene SAR image with strong speckle; meanwhile, the method has good robustness to target’s rotation and scale transformation.

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Multiple Feature Aggregation Using Convolutional Neural Networks for SAR Image-Based Automatic Target Recognition

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2018.2865608 ◽

2018 ◽

Vol 15 (12) ◽

pp. 1882-1886 ◽

Cited By ~ 16

Author(s):

Jun Hoo Cho ◽

Chan Gook Park

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Target Recognition ◽

Automatic Target Recognition ◽

Sar Image ◽

Multiple Feature ◽

Feature Aggregation

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Classification via Sparse Representation of Steerable Wavelet Frames on Grassmann Manifold: Application to Target Recognition in SAR Image

IEEE Transactions on Image Processing ◽

10.1109/tip.2017.2692524 ◽

2017 ◽

Vol 26 (6) ◽

pp. 2892-2904 ◽

Cited By ~ 36

Author(s):

Ganggang Dong ◽

Gangyao Kuang ◽

Na Wang ◽

Wei Wang

Keyword(s):

Sparse Representation ◽

Grassmann Manifold ◽

Target Recognition ◽

Sar Image ◽

Wavelet Frames

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Feature Extraction Based on Non-Subsampled Shearlet Transform (NSST) with Application to SAR Image Data

Mathematical Problems in Engineering ◽

10.1155/2020/8885887 ◽

2020 ◽

Vol 2020 ◽

pp. 1-6

Author(s):

Huijie Ding ◽

Arthur K. L. Lin

Keyword(s):

Feature Extraction ◽

Sparse Representation ◽

Target Recognition ◽

Image Feature ◽

Sar Image ◽

Translation Invariant ◽

Shearlet Transform ◽

Machine Learning Classification ◽

Joint Sparse Representation ◽

Multilevel Representations

Considering the defaults in synthetic aperture radar (SAR) image feature extraction, an SAR target recognition method based on non-subsampled Shearlet transform (NSST) was proposed with application to target recognition. NSST was used to decompose an SAR image into multilevel representations. These representations were translation-invariant, and they could well reflect the dominant and detailed properties of the target. During the machine learning classification stage, the joint sparse representation was employed to jointly represent the multilevel representations. The joint sparse representation could represent individual components independently while considering the inner correlations between different components. Therefore, the precision of joint representation could be enhanced. Finally, the target label of the test sample was determined according to the overall reconstruction error. Experiments were conducted on the MSTAR dataset to examine the proposed method, and the results confirmed its validity and robustness under the standard operating condition, configuration variance, depression angle variance, and noise corruption.

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