Use of the Evidence Theory to Combine Change Detection Indices and a priori Information

Digital change detection deals with the quantification, from multi-date imagery, of temporal phenomena, such as Aforestation-Reforestation-Deforestation, agricultural field rotation, abnormal evolution of the land surface. Despites the numerous change detection indices already proposed, none is sufficiently precise and reliable. We propose then to detect changes by considering not only one but several change indices, as well as information available from other source than remote sensing, i.e. derived from surface evolution model or a priori. For fusion, we chose the framework of the Dempster-Shafer evidence theory. It allows for some global ignorance, which is either present at the borders between the ´No-Change´ and ´Change´ classes, or is due to the poor quality of some change indices. The performance of the Non Remote Sensing (NRS) data change prediction, when known (e.g. statistical error of a model), can be taken into account the discounting of the mass functions. We present the results obtained in two different cases of application: forest logging and winter vegetation cover of fields in intensive farming areas. Remote sensing data are SPOT/HRV images. Considering the performance in terms of Non-Detection and False Detection rates, the interest of combining at least two change indices was clearly stated. The interest of NRS information has been then evaluated in the case of the field winter coverage application.