Thin linear features extraction in SAR images by fusion of amplitude and coherence information

In this paper, we propose a new method to extract thin linear decorrelated features in SAR interferometric images by fusioning the information provided by the amplitude and the coherence. A first detection is the result of an unsupervised classification performed on the coherence image, where linear features correspond to dark areas (low coherence) and non-linear features to brighter areas. This approximate location of the linear features is further refined by using edge information extracted in the amplitude data by two measures: the coefficient of variation (CV) and the ratio of local means (RLM). A precise detection is then performed by using the results of coherence classification and the fusion of the two previous measures. The method has been applied on ERS SAR images from the western part of Cameroon to extract thin river networks in mangrove areas. The goal is to update - or create geographical maps.