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A new linear-features detection method is proposed for extracting straight edges and lines in synthetic-aperture radar images. This method is based on the localized Radon transform, which produces geometrical integrals along straight lines. In the transformed domain, linear features have a specific signature: They appear as strongly contrasted structures, which are easier to extract with the conventional ratio edge detector. The proposed method is dedicated to applications such as geographical map updating for which prior information (approximate length and orientation of features) is available. Experimental results show the method's robustness with respect to poor radiometric contrast and hidden parts and its complementarity to conventional pixel-by-pixel approaches. OCIS codes: 100.5010, 280.6730.

This paper presents an almost unsupervised fusion algorithm on linear features (LF) extraction in synthetic aperture radar (SAR) interferometric data, in particular for mangroves/shorelines and thin internal channels. The spatial information on LFs is first extracted in the coherence image, where they are wider and more visible: water regions (in particular thin internal channels) are dark areas (low coherence) due to the temporal decorrelation of backscattering signals in these and surrounding regions, whereas conventional vegetation regions are brighter areas (high coherence). These approximate locations of LFs are further refined by using the edge map coming from a semantic fuzzy fusion of the coefficient of variation (CV) and the ratio of local means (RLM) measured in the amplitude image. The final detection of LFs is then performed by merging the two fuzzy inputs: the spatial information and the edge location map. The membership degree statistics of CV and RLM semantic fusion measures are introduced in order to illustrate the location detection ability. The originality of this method in comparison with conventional approaches is in the fusion scheme that follows the interpreter behavior by using first the coherence image for a fuzzy detection where thin LFs are more visible, but have low location accuracy, and then the amplitude image where they are poorly visible, but with higher location accuracy, to obtain improved results. A quantitative performance evaluation is also presented. The method has been applied on real interferometric SAR images from European Remote Sensing satellites over the western part of Cameroon. Index Terms--Information fusion, linear features, mangrove areas, synthetic aperture radar (SAR) interferometric imagery.