Kalman filtering as a multilayer perceptron training algorithm for detecting changes in remotely sensed imagery

The multilayer perceptron is usually trained by the backpropagation (BP) algorithm for computing the synaptic weights. In this paper, we investigate the use of Kalman filtering (KF) as a training algorithm for detecting changes in remotely sensed imagery. By using SPOT images and based on some evaluation criteria, the detailed comparison indicates that the KF algorithm is preferable compared to the BP algorithm in terms of convergence rate, stability and change detection accuracy.