Attributed scattering center matching based on deep belief network and application in target recognition of SAR images

Synthetic aperture radar (SAR) image target recognition is an important application of SAR image interpretation. In order to improve the robustness of SAR target recognition,this paper proposed an attribute scattering center matching method based on deep belief network(DBN). The attribute scattering center had rich parameters,which could well reflect the local scattering characteristics of the target. DBN took advantage of deep learning to achieve robust matching between the scattering center sets from test samples and template samples,which could also better adapt to noise interference,partial absence and other situations. Based on the matching correspondence of the attribute scattering center sets,the similarity measure criterion was defined. The target label of the test sample was determined based on the principle of the maximum similarity. Experiments were carried out based on MSTAR dataset, and the proposed method was proved to be effective and robust for SAR target recognition.