Bridge detection and recognition in remote sensing sar images using pulse coupled neural networks

Abstract

A novel double-level parallelized firing pulse coupled neural networks (DLPFPCNN) model is presented in this paper, which is used for the segmentation of remote sensing image with water area as low contrast, low signal-to-noise ratio(SNR), and uniform slowly varying grayscale values of object or background. Its theory and work process is detailedly introduced as well, base on which the novel DLPFPCNN model is used to segment remote sensing image containing bridges above water. By a series of sequential processing combining with the priori knowledge of the bridge itself, such as linear feature et al., the target is finally recognized. Experimental results show that the proposed method has a good application effect. © 2010 Springer-Verlag Berlin Heidelberg.