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遥感技术与应用  2019, Vol. 34 Issue (5): 1028-1039    DOI: 10.11873/j.issn.1004-0323.2019.5.1028
遥感应用     
凸体几何光谱解混研究进展及若干问题浅析
许宁1,2(),胡玉新1,2,3,耿修瑞1,2,3
1. 中国科学院空间信息处理与应用系统技术重点实验室,北京 100190
2. 中国科学院电子学研究所,北京 100190
3. 中国科学院大学,北京 100049
A Review and Brief Analysis of Convex Geometry-based Spectral Unmixing Methods for Hyperspectal Imagery
Ning Xu1,2(),Yuxin Hu1,2,3,Xiurui Geng1,2,3
1. Key Laboratory of Technology in Geo-spatial Information Processing and Application System, IECAS, Beijing 100190, China
2. Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China
3. University of Chinese Academy of Sciences, Beijing 100049, China
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摘要:

高光谱图像在高维特征空间中的凸体特性是凸体几何类光谱解混方法的理论依据,这类光谱解混方法具有直观性强、复杂度低、效率高等优点,是高光谱图像光谱解混方法研究的一个重要分支。本文旨在对国内外基于凸体几何理论的光谱解混方法进行回顾,指出这类方法研究中需要特别关注的若干问题,并着重对:①数据降维对凸体几何端元提取方法的影响,②两类经典的单形体体积衡量标准,③3个单形体体积计算公式及其关系3个问题进行简要分析,得到初步分析结果。

关键词: 凸体几何端元提取丰度估计光谱解混高光谱数据    
Abstract:

Convex geometry theory is the foundation of geometric spectral unmixing approaches in highly dimensional feature space for hyperspectral imagery. It is an important research field of spectral umixing for the geometric methods, and they have the characteristics of intuition, simplicity and high performance. A review of convex geometry-based spectral unmixing methods is summarized, and some primary problems the researchers usually confronted are concluded in the paper. Principally, three main problems are briefly analyzed herein: (1) the influence of Dimensionality Reduction (DR) on the geometric endmember extraction methods for hyperspectral imagery; (2) the difference of two classic simplex volume criterions for spectral unmixing; (3) Three formulas and their relationships for simplex volume calculating of spectral unmixing for the hyperspectral imagery. Finally, some elementary analysis results are obtained in the paper.

Key words: Convex geometry    Endmember extraction    Abundance estimation    Spectral unmixing    Hyperspectral imagery
收稿日期: 2018-08-13 出版日期: 2019-12-05
ZTFLH:  TP394.1  
基金资助: “十三五”背景预研项目(105060301);中国地质调查局地质调查项目(1212011120226)
作者简介: 许 宁(1982?),男,四川邛崃人,助理研究员,主要从事光学遥感图像配准、融合以及高光谱图像处理方面的研究。E?mail:shouwang131@sohu.com
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引用本文:

许宁,胡玉新,耿修瑞. 凸体几何光谱解混研究进展及若干问题浅析[J]. 遥感技术与应用, 2019, 34(5): 1028-1039.

Ning Xu,Yuxin Hu,Xiurui Geng. A Review and Brief Analysis of Convex Geometry-based Spectral Unmixing Methods for Hyperspectal Imagery. Remote Sensing Technology and Application, 2019, 34(5): 1028-1039.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2019.5.1028        http://www.rsta.ac.cn/CN/Y2019/V34/I5/1028

图1  三类凸体几何光谱解混方法的几何描述
算法ATGPATGP-DRN-FINDRN-FINDR-DRCCVACCVA-DR
20 dB[8163;6033;5936][3374;5936;1184][5490;5936;8179][3282;2757;4322][8179;2757;6737][8179;3939;5936]
30 dB[1465;4352;7178][4352;8662;3511][3511;1645;4352][9226;2757;4352][3511;4352;7500][4352;2356;8662]
40 dB[5198;2461;2757][4352;6189;1569][2173;2461;2757][2757;4352;5198][4352;2757;2173][2757;4352;2173]
[5198;4352;2757][4352;5198;2757][5198;4352;2757][2757;4352;5198][4352;2757;5198][4352;2757;5198]
表1  3种端元情况下不同方法提取端元的位置
图2  6种方法得到端元与真实端元的SAM比较
图3  模拟数据单形体计算结果图示
体积公式编号典型算法公式特点
公式(2)N-FINDR、SQ N-FINDR、改进策略N-FINDR、SC N-FINDR、IN-FINDR、Random N-FINDR,代数余子式N-FINDR、LDU-N-FINDR用到该公式的变形,以及SGA、RT SGA、PBP-SGA需首先对高光谱数据进行降维处理,保证端元矩阵的方阵特性
公式(3)DN-FINDR、KNSGA算法,矩阵三角分解、FGDA、快速SGA用到该公式的变形无需对数据进行降维处理,需计算像元向量差值及内积
公式(4)Cayley-Menger行列式方法无需对数据进行降维处理,需计算像元间欧式距离
表2  3个体积公式及其对应的典型端元提取算法
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