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. INDEX TERMS Anisotropic diffusion, feature extraction, pulse coupled neural network, speckle reducing, SAR image signature.

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Topological invariant features take priority over other vision features in early visual perception stage, which is the core idea of topological perception theory. In order to improve the robustness and distinguishability of the invariant features extracted by pulse coupled neural network (PCNN), the topological properties are integrated into PCNN. The improved PCNN model is called as topological property motivated PCNN (TPCNN), which adopts the saliency map calculated by the spectral residual approach as the important topological properties (the connectivity, and the number of holes in target). In TPCNN, firstly, the normalized saliency map is used as a linking coefficient to enhance the importance of saliency object when we calculating the invariant features. Secondly, the entropy signature of the saliency map is treated as an additional new feature and merged into original features calculated by PCNN, then the final invariant feature is obtained. The proposed TPCNN is used to calculate the invariant feature of different kinds of fish in the paper. Experimental results show that TPCNN outperforms the state-of-art models on invariant features extraction.INDEX TERMS Pulse coupled neural network, invariant feature extraction, topological perception theory, saliency map, spectral residual approach.

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