A Multistage Change detection methodology applying statistical multisource

Assessment and detection of environmental changes is one the most frequent applications in remote sensing. As a result, there has been a great proliferation of research on this particular topic, leading to different methodologies for detecting changes (Radke 2005, Foody 2009, Kennedy 2009) from data supplied by multitemporal images acquired from spaceborne sensors. The basic objective in a change detection process is to detect groups of pixels that are "significantly different" within a set of registered images of the same geographic area. Moreover it must be taken into account that in the recent decades, advances in space technologies made possible to collect a large amount of information about the Earth Surface and its environment. Since these data have been acquired from multiple sources, their quantitative exploitation requires optimal strategies to benefit from their interactions, so that information of high quality and great applicability for the proposed objectives can be extracted. (Petit 2001