Performance of change detection using remotely sensed data and evidential fusion: comparison of three cases of application

Abstract

The detection of changes affecting continental surfaces has important applications in hydrological, meteorological and climatic modelling. Using remote sensing data, numerous change indices have already been proposed. Previous work showed the interest of combining several of these to improve change detection performance, using the Dempster–Shafer evidence theory framework. This study analyses the performance of different change indices and their combination in different cases of application: forest logging either in pine forest or in mixed forest, and winter vegetation cover of fields in intensive farming areas, in comparison to the forest fire case presented in previous work. The interest of indices derived from Information Theory, some of which are original, is shown.

Acknowledgement

This work was partly supported by the French Programme National de Télédétection Spatiale, and the French Réseau Terre et Espace (project METIS‐forêt). The SPOT data were provided through the ISIS program, and are © CNES.