Bridge Detection and Recognition in Remote Sensing SAR Images Using Pulse Coupled Neural Networks

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.

[1]  DeLiang Wang,et al.  Range image segmentation using a relaxation oscillator network , 1999, IEEE Trans. Neural Networks.

[2]  Heggere S. Ranganath,et al.  Perfect image segmentation using pulse coupled neural networks , 1999, IEEE Trans. Neural Networks.

[3]  John L. Johnson,et al.  PCNN models and applications , 1999, IEEE Trans. Neural Networks.

[4]  Biao Jiang,et al.  Aerial targets detection using improved ULPCNN combined with contour tracking , 2008, Applied Optics and Photonics China.

[5]  Reinhard Eckhorn,et al.  Feature Linking via Synchronization among Distributed Assemblies: Simulations of Results from Cat Visual Cortex , 1990, Neural Computation.

[6]  Biao Jiang,et al.  Image segmentation based on double-level parallelized firing PCNN in complex environments , 2007, SPIE/COS Photonics Asia.

[7]  Jason M. Kinser,et al.  Finding the shortest path in the shortest time using PCNN's , 1999, IEEE Trans. Neural Networks.

[8]  Jiao Licheng,et al.  Segmentation and recognition of bridges in high resolution SAR images , 2001, 2001 CIE International Conference on Radar Proceedings (Cat No.01TH8559).