SAR image classification using the InSar coherence for soil degradation cartography in the south of Tunisia

In this work we demonstrate the potentiel of the InSAR coherence in classifying regions threatened by the desertification. We propose to combine features from InSAR amplitude and coherence images into a single 3D space in which every pixel is represented by its three-values vector (master image, slave image, coherence image) then to apply the multichannel Fuzzy C-Means (FCM) clustering algorithm. Our motivation behind this proposition lies first in the fact that correlation between both of InSAR images can reduce considerably effects of the noise while preserving image details. The second fact is that coherence represents a complementary information to the SAR image amplitude that can well indicates the spatial homogeneity behavior of each contextual class. Two ERS-1 SAR images over the region of Ben Guerdane, in the South-East of Tunisia, are considered for validation.