A SAR image target recognition method based on denoising self-coding network

The invention discloses a SAR image target recognition method based on a denoising self-coding network. The method comprises the following steps: denoising an image by using a three-dimensional modulematching algorithm (BM3D); for the original image and the denoised training image, the dense feature points are extracted by the sliding window and stored in pairs according to the corresponding positions, which are used for the input of the depth denoising encoder in the later training. Using the SIFT feature extracted from the denoised image as the original input x. The SIFT extracted from thecorresponding undenoised image is used as the noisy input to train the deep denoising self-coding network. The spatial pyramid model (SPM) is used to compute the image feature vector expression, and the maximum pooling (Max Pooling) is used to summarize the local features to obtain the final image description vector. A large number of dense SIFT features are used for deep network training, and thehigh-level representation