SAR Image Invariant Feature Extraction by Anisotropic Diffusion and Multi-Gray Level Simplified PCNN

Besides the target in synthetic aperture radar (SAR) image, the structural information remained in speckle and shadow is also very important when we extract the invariant feature of SAR image. However, the output of the classical pulse coupled neural network (PCNN) is a binary value. All the information remained in speckle and shadow is lost, when the classical PCNN is used to process SAR image directly. To overcome this problem, a multi-gray level simplified PCNN (MSPCNN) is proposed in this paper. All the useful information in speckle, shadow, and target can be considered when we use MSPCNN to compute the invariant feature of the SAR target. In order to suppress the negative influence of speckle noise and keep the useful structured information, the improved speckle reducing algorithm (SRAD) is used first. By an adaptive threshold delt0, we can keep the remained speckle in different SAR images to an ideal same level. The negative influence of the remained speckle to different targets' signature becomes basically the same and can be ignored. Combining with SRAD and MSPCNN, the SAR image invariant feature extraction scheme AD-MSPCNN is put forward. After analyzing the performance of different signature computing methods, we splice the time signature and the entropy signature together and use the new spliced vector as the invariant feature of the SAR target. The validity and robustness of AD-MSPCNN are proved by the experimental results on MSTAR database.