This work deals with unsupervised classification approach for mapping the flood impacts using interferometric synthetic aperture radar (InSAR) images. In order to estimate the flooding extent, we went towards considering the interferometric coherence information in view of the fact that flooded regions present a loss of coherence between two image acquisitions before and after the flooding. In this paper, we are adopting a fuzzy clustering algorithm to discriminate the flooded and dry areas in our region of interest. The definition of the input attribute vector was developed in [1], where features from InSAR amplitude and coherence images were combined into a single 3D space in which every pixel is represented by three-values vector (master image, slave image, coherence image). Then, an improved multichannel fuzzy C-means (FCM) clustering algorithm is developed in order to perform the classification process. Our main contribution is the optimization of the similarity measure of the FCM clustering algorithm using the InSAR coherence map. Experimental results and quantitative evaluation are given considering the Envisat data acquired over the Mellegue river in the north of Tunisia in 2005.
['Chayma Chaabani', 'Riadh Abdelfattah']
Optimized fuzzy algorithm based on modified similarity measure for mapping flood impacts