A Generalized Volume Scattering Model-Based Vegetation Index From Polarimetric SAR Data

IF 4 3区地球科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Geoscience and Remote Sensing Letters Pub Date : 2019-04-23 DOI:10.1109/LGRS.2019.2907703

D. Ratha, D. Mandal, Vineet Kumar, H. Mcnairn, A. Bhattacharya, A. Frery

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引用次数: 30

Abstract

In this letter, we propose a novel vegetation index from polarimetric synthetic-aperture radar (PolSAR) data using the generalized volume scattering model. The geodesic distance between two Kennaugh matrices projected on a unit sphere proposed by Ratha et al. is used in this letter. This distance is utilized to compute a similarity measure between the observed Kennaugh matrix and generalized volume scattering models. A factor is estimated corresponding to the ratio of the minimum to the maximum geodesic distances between the observed Kennaugh matrix and the set of elementary targets: trihedral, cylinder, dihedral, and narrow dihedral. This factor is then scaled and multiplied with the similarity measure to obtain the novel vegetation index. The proposed vegetation index is compared with the radar vegetation index (RVI) proposed by Kim and van Zyl. A time series of RADARSAT-2 data acquired during the Soil Moisture Active Passive Validation Experiment 2016 (SMAPVEX16-MB) campaign in Manitoba, Canada, is used to assessing the proposed RVI.

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基于广义体散射模型的极化SAR植被指数研究

本文提出了一种基于广义体散射模型的极化合成孔径雷达(PolSAR)植被指数。本文采用Ratha等人提出的单位球面上投影的两个Kennaugh矩阵之间的测地线距离。这个距离被用来计算观测到的Kennaugh矩阵和广义体积散射模型之间的相似性度量。根据观察到的Kennaugh矩阵与一组基本目标(三面体、圆柱体、二面体和窄二面体)之间的最小测地线距离与最大测地线距离的比值来估计一个因子。然后对该因子进行缩放并与相似度度量相乘，得到新的植被指数。将提出的植被指数与Kim和van Zyl提出的雷达植被指数(RVI)进行了比较。在加拿大马尼托巴省的土壤湿度主被动验证实验2016 (SMAPVEX16-MB)活动中获得的RADARSAT-2数据时间序列用于评估建议的RVI。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Geoscience and Remote Sensing Letters 工程技术-地球化学与地球物理

CiteScore

7.60

自引率

12.50%

发文量

1113

审稿时长

3.4 months

期刊介绍： IEEE Geoscience and Remote Sensing Letters (GRSL) is a monthly publication for short papers (maximum length 5 pages) addressing new ideas and formative concepts in remote sensing as well as important new and timely results and concepts. Papers should relate to the theory, concepts and techniques of science and engineering as applied to sensing the earth, oceans, atmosphere, and space, and the processing, interpretation, and dissemination of this information. The technical content of papers must be both new and significant. Experimental data must be complete and include sufficient description of experimental apparatus, methods, and relevant experimental conditions. GRSL encourages the incorporation of "extended objects" or "multimedia" such as animations to enhance the shorter papers.