Complex Background SAR Target Recognition Based on Convolution Neural Network

Deep learning networks are widely being applied to remote sensing image recognition and have achieved promising results. In this paper, we researched the influence of background with different scattering characteristics for synthetic aperture radar (SAR) target recognition based on convolutional neural network (CNN). Firstly, a two-parameter CFAR image segmentation method based on Weibull distribution was used to extracted SAR target and its shadow. And then, SAR datasets with road, farmland and grassland background environment is synthesized to analyze the CNN classifier. Experiments results show that the method by mixing training sets with different background together can improve the recognization rate when the backgrounds are complex.

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