Image Target Recognition Based on Deep Learning

Target recognition image is of great significance to the acquisition of ground and sea targets in the synthetic aperture radar (SAR) field. It has become a hot issue to realize automatic target detection and improve the accuracy of target recognition. In order to accurately obtain target information in images and solve the problem of over-fitting in deep neural network training, this study applied SAR image iterative denoising based on non-local adaptive dictionary to process SAR images, and constructed CNN network to extract SAR image features. Experimental results show that the proposed method can effectively improve the recognition accuracy of SAR images from sample data, and the recognition rate reaches 97.25% on MSTAR data sets.