基于改进SVM分类法的SAR图像水体面积提取研究

在复杂地表环境下的多云多雨地区,基于合成孔径雷达（SAR）图像提取水体时容易受到其它地物如水田、山体阴影等干扰,传统的灰度阈值法和SVM法未能考虑水体与其它地物在纹理和地形上的差异,因此水体提取结果精度较差。研究首先用Refined Lee滤波对SAR图像进行预处理;其后通过DEM建模和坡度计算提取地形特征,通过计算图像灰度共生矩阵以提取纹理特征（包括均匀性、角二阶矩和熵）,并结合SAR图像极化信息以及SDWI指数形成针对水体提取的特征空间,通过融合地形特征和图像纹理特征发展了改进SVM 分类法的水体提取模型。在使用Sentinel-1 SAR数据对所发展模型与SDWI水体指数法、传统SVM法水体提取结果进行比对后发现,改进SVM分类法提取的水体结果较好地剔除了水田和山体阴影,且提取的水体水面比传统的SVM法更加完整;该方法在总体精度、Kappa系数、漏分率和错分率指标上均优于SDWI法和传统的SVM法,总体精度达到98.06%,比SDWI法和传统的SVM法分别提高了23.24%和5.49%,有效提高了复杂环境下地表水体的提取精度。研究最后将所发展模型应用于2018年马哈韦利河流域逐月水体提取与变化分析,有效解决了山体阴影和水田误分问题。本文提出的改进SVM法可以实现复杂地表环境下大范围水体信息准确、完整提取。

Water Body Area Extraction from SAR Image based on Improved SVM Classification Method

In cloudy and rainy regions with complex surface environments, water body extraction based on Synthesis Aperture Radar (SAR) image is easily interfered by other surface features such as paddy field and mountain shadow. The traditional gray threshold method and SVM method fail to take into account the differences in texture and terrain information between water and other surface features, resulting in the low accuracy of water body extraction. In this paper, we first use refined Lee filter to pre-process the SAR image, then extract terrain features by modeling DEM and calculating the slope information. We extract the texture features, including the homogeneity (hom), Angular Second Moment (ASM), and entropy (ENT), by calculating image Gray Level Co-occurrence Matrix (GLCM) based on the SAR image. Meanwhile, polarization band information of the SAR image and SDWI index are combined to form the SVM feature space for water body extraction. Finally, an improved SVM classification method for water body extraction is proposed by fusing terrain features with image texture features. Compared with SDWI water index method and traditional SVM method based on Sentinel-1 SAR data, it is found that the improved SVM method can remove the shadows of paddy field and mountain. The result of water surface extraction by the improved SVM method is more complete than that of traditional SVM method. The result also shows that the improved SVM method performs better than SDWI method and traditional SVM method in terms of overall accuracy, kappa coefficient, leakage rate, and error rate. The overall accuracy of the improved SVM method is 98.06%, which is 23.24% and 5.49% higher than that of SDWI method and traditional SVM method, respectively, demonstrating that the improved SVM method can effectively improve the extraction accuracy of surface water in complex environments. The developed method is applied to monthly water extraction and change analysis of Mahaweli River Basin in 2018, which proves that the method can effectively solve the problems of mountain shadow and paddy field misclassification. The improved SVM method can realize the accurate and complete large-scale water body information extraction in complex surface environments.