Multi-index change detection using Dempster-Shafer evidence theory: application to land-cover monitoring

The detection of changes affecting continental surfaces has important applications in hydrological, meteorological, and climatic modelling, so that numerous change indices have already been proposed that use remote sensing data. In this work, we show the interest of combining several of them to improve change detection performance. The combination is done in the Dempster-Shafer evidence theory framework, therefore allowing ignorance modelling. Each mass function is defined either based on the result of the corresponding mono-index analysis, that is done using an 'a contrario' approach, or from generic sigmoid function in the absence of pdf assumption. Using actual SPOT/HRV data, we analyse the performance of different change indices, and their combination in different application cases: forest fires, forest logging either in pine forest or in mixed forest, and winter vegetation cover of fields in intensive farming areas. Finally, we also show the interest of the indices derived from the Information Theory, some of which being original.