Automatic Change Detection in Very High Resolution Images with
Pulse-Coupled Neural Networks

Fabio Pacifici, and Fabio Del Frate

Abstract

A novel approach based on Pulse-Coupled Neural Networks (PCNN) for image change detection is presented. PCNN are based on the implementation of the mechanisms underlying the visual cortex of small mammals and with respect to more traditional neural networks architectures such as Multi-Layer Perceptron (MLP) own interesting advantages. In particular, they are unsupervised and context sensitive. This latter property may be particularly useful when very high resolution images are considered, as in this case an object analysis might be more suitable than a pixel-based one. Qualitative and more quantitative results are reported. The performance of the algorithm has been evaluated on a pair of QuickBird images taken over the test area of Tor Vergata University, Rome..

Index Terms— Pulse-coupled neural networks, unsupervised change detection, very high resolution images

This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.

This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

.