A context-sensitive technique for unsupervised change detection based on Hopfield-type neural networks

In this paper, we propose a context-sensitive technique for unsupervised change detection in multitemporal remote sensing images. This technique is based on a modified Hopfield neural network architecture designed to model spatial correlation between neighboring pixels of the difference image produced by comparing images acquired on the same area at different times. Each spatial position in the considered scene is represented by a neuron in the Hopfield network that is connected only to its neighboring units. These connections model the spatial correlation between neighboring pixels and are associated with a context-sensitive energy function that represents the overall, status of the network. Change detection maps are obtained by iteratively updating the output status of the neurons until a minimum of the energy function is reached and the network assumes a stable state. A simple heuristic thresholding procedure is presented and adopted for initializing the network. The proposed change detection technique is unsupervised and distribution free. Experimental results carried out on two multispectral and multitemporal remote sensing images confirm the effectiveness of the proposed technique.