SAR图像独立极化方位角整合的极化目标分解法

doi:10.11947/j.AGCS.2023.20220552

测绘学报 ›› 2023, Vol. 52 ›› Issue (12): 2141-2153.doi: 10.11947/j.AGCS.2023.20220552

SAR图像独立极化方位角整合的极化目标分解法

李能才¹, 胡粲彬¹, 王威², 全斯农², 项德良^1,3

1. 北京化工大学信息科学与技术学院, 北京 100029;
2. 国防科技大学电子科学学院, 湖南长沙 410073;
3. 北京化工大学软物质科学与工程高精尖创新中心, 北京 100029

收稿日期:2022-10-24 修回日期:2023-11-08 发布日期:2024-01-03
通讯作者: 项德良 E-mail:xiangdeliang@buct.edu.cn
作者简介:李能才(1994-),男,硕士生,研究方向为微波遥感信息处理。E-mail:linengcai@qq.com
基金资助:
国家自然科学基金(62171015;61901500;62001487)

Polarimetric SAR target decomposition method based on independent polarization orientation angle integration

LI Nengcai¹, HU Canbin¹, WANG Wei², QUAN Sinong², XIANG Deliang^1,3

1. College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China;
2. College of Electronic Science, National University of Defense Technology, Changsha 410073, China;
3. Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China

Received:2022-10-24 Revised:2023-11-08 Published:2024-01-03
Supported by:
The National Natural Science Foundation of China (Nos. 62171015;61901500;62001487)

摘要/Abstract

摘要： 基于物理模型的极化目标分解方法由于其物理意义明确且易于实现,成为了极化SAR非相干极化目标分解的主流方法之一,然而这类方法在大方位角城市建筑区域往往存在体散射过估计和散射机制模糊问题,为此,本文提出了一种基于独立极化方位角整合的六分量极化目标分解方法。该方法不再采用极化方位角补偿的策略,而是引入3个独立的极化方位角分别对偶次散射模型、偶极子模型和四分之一波模型进行描述,得到了更一般的改进的偶次散射模型、广义偶极子模型及广义四分之一波模型。本文方法简化了表面散射模型和偶次散射模型,在降低未知变量的同时,有效地解耦了各散射分量;所采用的6种模型完全解释极化相干矩阵的9个元素,充分利用了极化相干矩阵信息;分析了极化相干矩阵主对角线元素的旋转不变性,利用该性质避免了极化方位角参与计算,同时分解过程避免了除法导致的计算异常,保证了极化分解结果的稳定性。利用GF-3和UAV SAR全极化数据进行试验,试验结果表明,本文方法能够在保证森林植被、海洋水体等自然区域的主导散射正确提取的情况下,有效地改善城市建筑特别是大方位角建筑区域的体散射高估问题,总体分解结果更为符合实际地物散射过程。

关键词: 极化相干矩阵, 极化目标分解, 极化散射机制, 体散射过估, 独立极化方位角

Abstract: The polarimetric decomposition method based on physical models has become one of the mainstream methods for PolSAR incoherent target decomposition due to its clear physical significance and ease of implementation. However, such methods often have the problem of volume scattering overestimation and scattering mechanism ambiguity in urban building areas with large orientation angle. Therefore, a six-component polarimetric target decomposition method based on independent polarization orientation angle integration is proposed in this paper. Instead of using the polarization orientation angle compensation strategy, three independent polarization orientation angles were introduced to describe the double-bounce scattering models, dipole scattering models and quarter-wave scattering models, and the generalized dipole scattering model, the generalized quarter-wave scattering model and the improved double-bounce scattering model were obtained. The surface scattering model and double-bounce scattering model are simplified, and the scattering components are decoupled effectively while the unknown variables are reduced. At the same time, the six scattering models fully explain the nine elements and make better use of the information of the polarization coherence matrix. The rotation invariance of the main diagonal elements of the polarization coherence matrix is analyzed, and the polarization orientation angles is avoided to participate in the calculation by using this property. At the same time, the calculation anomaly caused by division is avoided during the decomposition process, and the stability of the polarization decomposition results is ensured. GF-3 and UAV SAR full-polarization data were used for the experiment, the experiment result shows that thedecomposition method can effectively solve the overestimation of volume scattering of urban buildings, especially in large orientationangle building areas, while ensuring the correct extraction of dominant scattering power from forest vegetation and ocean areas, and the overall decomposition results are more consistent with the actual scattering process of the land covers.

Key words: polarimetric coherence matrix, polarimetric model-based decomposition, polarimetric scattering mechanism, overestimation of volume scattering, independent polarization orientation angle

中图分类号:

P234

李能才, 胡粲彬, 王威, 全斯农, 项德良. SAR图像独立极化方位角整合的极化目标分解法[J]. 测绘学报, 2023, 52(12): 2141-2153.

LI Nengcai, HU Canbin, WANG Wei, QUAN Sinong, XIANG Deliang. Polarimetric SAR target decomposition method based on independent polarization orientation angle integration[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(12): 2141-2153.

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SAR图像独立极化方位角整合的极化目标分解法

Polarimetric SAR target decomposition method based on independent polarization orientation angle integration

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