Multi-scale data fusion using Dempster-Shafer evidence theory

In the remote sensing domain, the combination of multi-scale satellite data appears as a new challenge. It should provide significant improvements in Earth monitoring by use of the complementary of the data presenting either high spatial resolution or high time repetitiveness. Here, we propose an algorithm based on the Dempster-Shafer evidence theory, which allows the modeling of the mixed feature of the low spatial resolution pixels, and the modeling of the class confusion when time information is not sufficient, by consideration of compound hypotheses such as unions of classes. It has been applied on SPOT/HRV image and NOAA/AVHRR series, and the results have clearly shown the improvement brought by the proposed data fusion.