Spatial constraint Hopfield-type neural networks for detecting changes in remotely sensed multitemporal images

In this article a spatio-contextual unsupervised change detection technique for multitemporal, multi spectral remote sensing images is proposed. The technique uses a Gibbs Markov Random Field (GMRF) to model the spatial regularity between the neighboring pixels of the multitemporal difference image. The difference image is generated by change vector analysis (CVA) applied to images acquired on the same area at different times. The change detection problem is solved using the Maximum a posteriori probability (MAP) estimation principle. The MAP estimator of the GMRF used to model the difference image is exponential in nature, thus a modified Hopfield type neural network is exploited for estimating the MAP. In the considered Hopfield type network, a single neuron is assigned to each pixel of the difference image and is assumed to be connected only to its neighbors. Initial values of the neurons are set by histogram thresholding. An Expectation Maximization (EM) algorithm is used to estimate the GMRF model parameters. The proposed technique is validated by testing on different multispectral and multitemporal remote sensing images and compared with existing state-of-the-art techniques.