LAUSR

In this article, we propose a new line segment detection framework based on a novel pixel orientation estimation (POE) method, which detects line segments from binary edge maps and can be combined with any edge detectors. We show its efficiency by testing it in SAR images. The proposed POE-based line segment detection framework aims to leverage the success of deep learning models for edge detection in 1-look SAR images. The novel POE method estimates the orientation of each edge pixel by counting the number of edge pixels along a set of orientations. As the most edge pixels exist along the orientation of the line segment, the orientation of the edge pixel is given by the orientation that gives the maximum number of counts. Counting the number of edge pixels along different orientations is equivalent to convolving the local neighbourhood of each edge pixel with a set of carefully designed window functions with each window function corresponding to a fixed orientation. With the estimated orientations of pixels in the edge map, pixels can be grouped with a region growing step to form line support regions. Regions with their size larger than a size threshold will be accepted. Finally, rectangles are used to approximate the accepted regions and those rectangles are detected line segments. Experiments in both simulated SAR dataset and real SAR images demonstrate the efficiency of the proposed method. In particular, we advance the state-of-the-art performances by 18% (F1-score) on the 1-look dataset simulated from YorkUrban-LineSegment Dataset.