A NEARLY LOSSLESS 2D REPRESENTATION AND CHARACTERIZATION OF CHANGE INFORMATION IN MULTISPECTRAL IMAGES

In this paper a framework for the detection of multiple changes in multitemporal and multispectral remote sensing images is presented. The framework is based on: i) a compressed yet efficient (i. e., nearly lossless) 2-dimensional (2D) representation of the change information; and ii) a 2-step automatic decision strategy. At first, the original BD feature space to be explored for the solution of the change-detection (CD) problem is compressed to a 2D space in which the change information is clearly represented; then, the retrieved 2D space is explored for extracting in an automatic way the different kinds of change, thus generating the CD map. This procedure is conducted by applying a 2-step decision strategy based on the Bayes decision theory. Results obtained on a Landsat-5 and a QuickBird data sets confirm the effectiveness of the proposed approach in both representing the information in the 2D space and generating the CD map.