Automatic object extraction from VHR satellite SAR images using pulse coupled neural networks

Fabio Del Frate; Daniele Latini; Chiara Pratola

doi:10.1117/12.867918

22 October 2010 Automatic object extraction from VHR satellite SAR images using pulse coupled neural networks

Fabio Del Frate, Daniele Latini, Chiara Pratola

Proceedings Volume 7829, SAR Image Analysis, Modeling, and Techniques X; 782905 (2010) https://doi.org/10.1117/12.867918
Event: SPIE Remote Sensing, 2010, Toulouse, France

Abstract

In this paper we investigate an unsupervised neural network approach for automatically extracting objects of interest from very high resolution (VHR) SAR images. The technique is based on the use of Pulse-Coupled Neural Networks (PCNN) which is a relatively novel technique based on models of the visual cortex of small mammals. The study discusses the use of PCNN technique in different applications. In a first case the extraction procedure is focused on the detection of buildings. In the second case the segmentation of a dark spot representing an oil spill in a SAR image is considered. The performance yielded by the PCNN is evaluated and critically discussed for a set of new generation of X-band SAR images taken by COSMO-Skymed and TerraSAR-X systems.

Citation Download Citation

Fabio Del Frate, Daniele Latini, and Chiara Pratola "Automatic object extraction from VHR satellite SAR images using pulse coupled neural networks", Proc. SPIE 7829, SAR Image Analysis, Modeling, and Techniques X, 782905 (22 October 2010); https://doi.org/10.1117/12.867918

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