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When facing radar target recognition, the main problems focus on the data representation capability and the robustness to cope with noise. The merits of deep learning such as automatic setting for training and hierarchical extraction of features. Most of existing deep networks are related to Restricted Boltzmann machine (RBM), which has played an important role in deep learning techniques. The models...
Due to the complexity of synthetic aperture radar (SAR) image formation and the sensitivity to partial occlusion, automatic target recognition (ATR) remains an important, yet challenging problem in SAR image interpretation. Scattering center model provides a concise and physically relevant description of target's electromagnetic scattering phenomenon which makes it an ideal candidate for ATR. In this...
We introduce a novel high range resolution (HRR) based multi-look automatic target recognition (ATR) method in this paper. First, the scattering center model established offline is used to reduce the data amount that needed in template construction, which makes the method efficient. Then, the correlation among the multiple looks of the same target is considered using the joint sparse representation...
Recognizing targets in synthetic aperture radar (SAR) images is an important, yet challenging problem in SAR image interpretation. Scattering center provides a concise and physically relevant description of the target's electromagnetic scattering phenomenon which makes it an ideal candidate for ATR. In this paper, scattering centers from template and measured data are used for ATR. The two scatter...
This paper presents an approach for synthetic aperture radar (SAR) scattering center detection based on electromagnetic model (EM-model) by hypothesis testing (HT). The scattering center extraction is a critical step in model-based SAR automatic target recognition. In traditional methods, the extraction of scattering centers merely make use of the measured SAR image and lack for the prior information...
LBP operator shows good performance on rotation invariant while LGRPH is robust to multiplicative noise and gradient changes. In order to combine merits of both operators, an improved rotation invariant feature for SAR image is proposed in this paper. Experiments on SAR images demonstrate that the proposed feature has a good performance on targets recognition and image texture patches matching with...
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