A tensor voting approach to dark spot detection in RADARSAT-1 intensity imagery

This paper presents a tensor voting approach to automated detection of dark spots in RADARSAT-1 ScanSAR Narrow Beam mode images. First, a thresholding algorithm that well maximizes the ratio of between-class variance to within-class variance is used to detect potential dark spot candidates. Next, a tensor voting framework integrated with sparse and dense ball votings is carried out to suppress noise while maintaining dark spots. Then, a saliency map that reflects the probability of a pixel being located within a dark spot is generated using the saliencies of ball tensors. Finally, a segmentation method is applied to ascertain dark spots based on the saliency map. The proposed approach has been tested on a set of RADARSAT-1 ScanSAR Narrow Beam intensity images. Quantitative evaluations demonstrate that the proposed approach achieves an average commission error, omission error, and quality of 0.003, 0.037, and 0.956, respectively, for detecting dark spots in SAR intensity imagery.