COHERENCE ESTIMATION IN THE LOW-BACKSCATTERING AREA USING MULTITEMPORAL TERRASAR-X IMAGES AND ITS APPLICATION ON ROAD DETECTION

In order to study the coherence characteristics of low-backscattering objects such as roads and rivers, we introduce a coherence estimation approach based on clustering method. When the approach is applied to multi-temporal high-resolution TerraSAR-X images in urban areas, the results show that the coherence of roads is higher than that of rivers and shadows. This indicates that coherence features can be used to distinguish roads and water bodies. Further, this paper proposes a road detection method of synthetic aperture radar (SAR) images based on path operators and support vector machine (SVM), which combines the backscattering and coherence characteristics of the roads. Experimental results show that coherence features can be used for road detection.