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For the reason of different images with different space distribution of gray levels, we proposed a texture representation based on simplified pulse coupled neural networks (PCNN) model which output a series of binary images corresponding to different gray levels. Then we transformed the images into 1D temporal sequence by calculating their variances to form feature vectors. Experiments show that the texture representation was rotation invariant which provided high classification rate for natural texture images. When used to classify side-scan sonar seafloor images of 12 types of sediment, accurate recognition rate of 100% was obtained. With the inherent parallel capability of PCNN, the method is more suited for real-time processing of sonar systems.

Image Feature Extraction based on Pulse coupled neural Networks for Seafloor sediment classification