Fast Line Segment Detection and Large Scene Airport Detection for PolSAR

Abstract

In this paper, we propose a fast Line Segment Detection algorithm for Polarimetric synthetic aperture radar (PolSAR) data (PLSD). We introduce the Constant False Alarm Rate (CFAR) edge detector to obtain the gradient map of the PolSAR image, which tests the equality of the covariance matrix using the test statistic in the complex Wishart distribution. A new filter configuration is applied here to save time. Then, the Statistical Region Merging (SRM) framework is utilized for the generation of line-support regions. As one of our main contributions, we propose a new Statistical Region Merging algorithm based on gradient Strength and Direction (SRMSD). It determines the merging predicate with consideration of both gradient strength and gradient direction. For the merging order, we set it by bucket sort based on the gradient strength. Furthermore, the pixels are restricted to belong to a unique region, making the algorithm linear in time cost. Finally, based on Markov chains and a contrario approach, the false alarm control of line segments is implemented. Moreover, a large scene airport detection method is designed based on the proposed line segment detection algorithm and scattering characteristics. The effectiveness and applicability of the two methods are demonstrated with PolSAR data provided by UAVSAR.

CTMANet: A CNN-Transformer Hybrid Semantic Segmentation Network for Fine-Grained Airport Extraction in Complex SAR Scenes

“…Kou et al proposed a remote sensing image airport extraction method based on Line Segment Detector [38]. Wang et al introduced a fast line segment detection algorithm with a new filter for SAR images [39]. This group of methods concentrates on extracting airport by detecting the edges of airport runways.…”

Line segment detection algorithm in image extraction improvement study

“…The key to automatic computerized image processing technology is to remove unwanted redundant background information and extract the desired targets directly from the image [3]. Compared with traditional image features such as edge curves or feature points, straight line segment (LS) features contain richer information about the angles, centroids, geometric relationships and topology of the most basic straight LSs in the scene [4][5]. In order to analyze the basis of the image content or to detect the high-level features of the image, the information of the image is extracted using the line segment detection (LSD) method.…”