Shi Chao, Zuo Xiaoqing, Zhang Jianming, Zhu Daming, Li Yongfa, Bu Jinwei
Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China.
Sensors (Basel). 2024 Apr 29;24(9):2834. doi: 10.3390/s24092834.
SAR imagery plays a crucial role in geological and environmental monitoring, particularly in highland mountainous regions. However, inherent geometric distortions in SAR images often undermine the precision of remote sensing analyses. Accurately identifying and classifying these distortions is key to analyzing their origins and enhancing the quality and accuracy of monitoring efforts. While the layover and shadow map (LSM) approach is commonly utilized to identify distortions, it falls short in classifying subtle ones. This study introduces a novel LSM ground-range slope (LG) method, tailored for the refined identification of minor distortions to augment the LSM approach. We implemented the LG method on Sentinel-1 SAR imagery from the tri-junction area where the Xiaojiang, Pudu, and Jinsha rivers converge at the Yunnan-Sichuan border. By comparing effective monitoring-point densities, we evaluated and validated traditional methods-LSM, R-Index, and P-NG-against the LG method. The LG method demonstrates superior performance in discriminating subtle distortions within complex terrains through its secondary classification process, which allows for precise and comprehensive recognition of geometric distortions. Furthermore, our research examines the impact of varying slope parameters during the classification process on the accuracy of distortion identification. This study addresses significant gaps in recognizing geometric distortions and lays a foundation for more precise SAR imagery analysis in complex geographic settings.
合成孔径雷达（SAR）图像在地质和环境监测中起着至关重要的作用，特别是在高地山区。然而，SAR图像中固有的几何畸变常常会影响遥感分析的精度。准确识别和分类这些畸变是分析其成因并提高监测工作质量和准确性的关键。虽然叠掩和平行投影阴影图（LSM）方法通常用于识别畸变，但在对细微畸变进行分类时存在不足。本研究引入了一种新颖的LSM地距斜率（LG）方法，专门用于精确识别微小畸变，以增强LSM方法。我们在小江、普渡河和金沙江在滇川边境交汇的三角区域的哨兵-1 SAR图像上实施了LG方法。通过比较有效监测点密度，我们将传统方法——LSM、R指数和P-NG——与LG方法进行了评估和验证。LG方法通过其二次分类过程在辨别复杂地形中的细微畸变方面表现出卓越性能，从而能够精确全面地识别几何畸变。此外，我们的研究考察了分类过程中不同斜率参数对畸变识别准确性的影响。本研究弥补了几何畸变识别方面的重大空白，并为复杂地理环境下更精确的SAR图像分析奠定了基础。
