基于极化SAR图像的玛纳斯河流域典型区积雪识别

南京大学学报(自然科学版) ›› 2015, Vol. 51 ›› Issue (5): 966–975.

基于极化SAR图像的玛纳斯河流域典型区积雪识别

郭金金1,2,3，肖鹏峰1,2,3*，冯学智1,2,3，朱榴骏1,2,3，周淑媛1,2,3

出版日期:2015-09-09 发布日期:2015-09-09
作者简介:(1. 江苏省地理信息技术重点实验室,南京大学,南京,210023; 2. 卫星测绘技术与应用国家测绘地理信息局重点实验室,南京大学,南京,210023; 3. 南京大学地理信息科学系,南京,210023)
基金资助:
国家自然科学基金项目（41271353），国家高分辨率对地观测系统重大专项项目（95-Y40B02-9001-13/15-04）

Recognizing snow from polarimetric SAR imagesin typical area of ManasiRiver Basin

Guo Jinjin1,2,3, Xiao Pengfeng1,2,3*, Feng Xuezhi1,2,3, Zhu Liujun1,2,3, Zhou Shuyuan1,2,3

Online:2015-09-09 Published:2015-09-09
About author:(1. Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing, 210023, China; 2. Key Laboratory for Satellite Mapping Technology and Applications of State Administration of Surveying, Mapping and Geoinformation of China, Nanjing University, Nanjing, 210023, China; 3. Department of Geographic Information Science, Nanjing, 210023, China)

摘要/Abstract

摘要： 极化合成孔径雷达具备全天候的积雪观测能力，而且能提供丰富的极化特征用于积雪识别。本文选取2014年3月19日新疆玛纳斯河流域典型区Radarsat-2数据，首先对全极化SAR数据进行目标分解提取积雪极化特征，再利用J-M距离（Jeffreys-Matusita）进行特征选择，分析不同极化特征对积雪的可分性，最后利用最优特征集和支持向量机（Support vector machine,SVM）进行积雪识别。结果表明：Yamaguchi分解和Freeman分解的体散射分量、相干矩阵特征值和香农熵四种极化特征对积雪有较强的识别能力；多种极化特征联合识别相对于单一特征识别积雪具有较大优势，基于四种极化特征的积雪识别精度达到84%。利用极化特征进行积雪识别可获得较好效果，能够弥补可见光遥感难以识别云下积雪的不足。

Abstract: Polarimetric synthetic aperture radar sensors can not only provide an all-weather snow observational capacity, but also provide a wealth of polarization characteristics, which have the potential to discriminate the snow-cover from other natural scatters. In this paper, the data we acquired was Radarsat-2 image in typical area of Manasi River Basin, Xinjiang Province on 19 March 2014. At first, we used polarimetric decomposition methods to extract polarimetric features for snow recognition. Second, Jeffreys-Matusita (J-M) distance was applied for feature selection. We analyzed the separability of different polarimetric features to discriminatebetween snow and snow-free areas. At last, snow recognition was completed by using the best features and support vector machine (SVM). The results show that the volume scattering component of Yamaguchi and Freeman decomposition, eigenvalue of coherent matrix and Shannon entropy have strong recognition ability for snow; compared with the single feature, combining several polarimetric features for snow recognition can obtain a better result and the accuracy based on the four polarimetric characteristics reached 84%.The snow identification by polarization features can acquirebetter effect and can remedy the limitationinsnow identification by visible spectral remote sensing under the cloud condition

郭金金1,2,3，肖鹏峰1,2,3*，冯学智1,2,3，朱榴骏1,2,3，周淑媛1,2,3. 基于极化SAR图像的玛纳斯河流域典型区积雪识别[J]. 南京大学学报(自然科学版), 2015, 51(5): 966–975.

Guo Jinjin1,2,3, Xiao Pengfeng1,2,3*, Feng Xuezhi1,2,3, Zhu Liujun1,2,3, Zhou Shuyuan1,2,3. Recognizing snow from polarimetric SAR imagesin typical area of ManasiRiver Basin[J]. Journal of Nanjing University(Natural Sciences), 2015, 51(5): 966–975.

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