PCNN for automatic segmentation and information extraction from X-band SAR imagery

The extremely high number of synthetic aperture radar (SAR) images provided by the current spaceborne missions demand for the development of even more effective automatic techniques for data processing. In this context, neural approaches can give significant contributions being characterised by a high level of automatism. In particular, rather interesting potential is provided by the pulse-coupled neural networks (PCNNs), which have been designed with the idea of simulating the visual cortex of small mammals. In this article, the performance of PCNNs for automatic object extraction from satellite with very high-resolution SAR images is examined by applying them to different cases of interest.