基于多特征SVM的PolSAR图像水陆分割

doi:10.13474/j.cnki.11-2246.2025.0510

摘要/Abstract

摘要： 针对传统方法的极化合成孔径雷达(PolSAR)图像水陆分割边缘不平滑完整、狭小水域分割效果不佳等问题,本文提出了基于多特征支持向量机(SVM)的PolSAR图像水陆分割方法。首先,利用Cloude、Yamaguchi目标分解法提取像素的7维极化散射特征,结合其Sentinel-1双极化水指数(SDWI)构建像素的8维特征向量;遍历PolSAR图像的所有像素,得到PolSAR图像的特征集合。然后,将训练样本的特征集合和地物类别集合构成训练数据集,对SVM分类器进行训练再利用该分类器实现PolSAR图像的水陆分割;最后,对PolSAR图像进行水陆分割试验。结果表明,基于多特征SVM的水陆分割法可更好地实现PolSAR图像水陆分割,Kappa系数均值达0.979 3,分类总精度均值达98.98%。

关键词: PolSAR图像, 目标分解, SDWI, 支持向量机, 水陆分割

Abstract: Aiming at the problems of polarimetric synthetic aperture radar (PolSAR) image water and land segmentation with non-smooth and complete edges, and poor segmentation results in small water areas, this paper proposes a PolSAR image segmentation method based on multi-feature support vector machines (SVM) algorithm. Firstly, seven polarization scattering features of a pixel are extracted by Cloude and Yamaguchi target decomposition method, and eight-dimension feature vector of the pixel is constructed by combining with its Sentinel-1 dual-polarized water index (SDWI). All the pixels of the PolSAR image are traversed and obtain the feature set of the PolSAR image. Then, the training dataset are constituted by the feature and class sets of the training samples to train the SVM classifier, which is utilized to realize the water and land segmentation for PolSAR images. Finally, the water and land segmentation experiments of PolSAR images are carried out using the proposed and comparison methods. The experimental results show that the proposed method can realize the water and land segmentation better, the mean values of Kappa coefficients and total accuracy are 0.979 3 and 98.98%, respectively.

Key words: PolSAR image, target decomposition, SDWI, SVM, water-land segmentation

中图分类号:

P237

王玉, 梁菘元, 李泽辰, 石雪. 基于多特征SVM的PolSAR图像水陆分割[J]. 测绘通报, 2025, 0(5): 59-65.

WANG Yu, LIANG Songyuan, LI Zechen, SHI Xue. Water-land segmentation of PolSAR image based on multi-feature SVM algorithm[J]. Bulletin of Surveying and Mapping, 2025, 0(5): 59-65.

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