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高光谱分类体积的端元提取

Classification and Volume for Hyperspectral Endmember Extraction

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摘要

为求解高光谱图像中各物质的分布及含量, 将高光谱分类引入端元提取, 提出了一种新的端元提取方法。首先利用虚拟维度评估端元数目; 然后引入高光谱分类的思想, 通过K-means聚类算法对高光谱图像进行非监督分类, 对各类物质进行大致分类; 在每类物质中提取出光谱值最大的像元, 用这些像元构成端元候选集; 最后, 依据单形体理论, 将高光谱图像的像元点在高维空间中构成单形体, 体积最大的单形体的顶点即为端元。模拟和真实高光谱数据证明, 此端元提取方法相对于传统方法具有高效、准确的优点。

Abstract

In order to solve the distribution and content of each substance in hyperspectral images, we introduce the hyperspectral classification into the endmember extraction to propose a new endmember extraction method. Firstly, the number of endmembers is determined by the virtual dimension. And the thought of hyperspectral unsupervised classification is performed by K-means clustering algorithm which classifies each pixel into classifications. Then the pixel with the largest spectral value is extracted from each kind of class. According to the theory of simplex, the pixels of hyperspectral image are used to form a simplex in the high-dimensional space, and the vertexes of the largest simplex are the endmembers which are extracted. The simulation and real data have shown that this method of endmember extraction has the advantages of high efficiency and accuracy compared with the traditional method.

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中图分类号:O433.4

DOI:10.3788/lop55.093004

所属栏目:光谱学

收稿日期:2018-02-27

修改稿日期:2018-04-03

网络出版日期:2018-04-08

作者单位    点击查看

严阳:陆军工程大学石家庄校区电子与光学工程系, 河北 石家庄 050003
华文深:陆军工程大学石家庄校区电子与光学工程系, 河北 石家庄 050003
崔子浩:陆军工程大学石家庄校区电子与光学工程系, 河北 石家庄 050003
伍锡山:陆军工程大学石家庄校区电子与光学工程系, 河北 石家庄 050003
刘恂:陆军工程大学石家庄校区电子与光学工程系, 河北 石家庄 050003

联系人作者:华文深(huawensh@126.com); 严阳(554735438@qq.com);

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引用该论文

Yan Yang,Hua Wenshen,Cui Zihao,Wu Xishan,Liu Xun. Classification and Volume for Hyperspectral Endmember Extraction[J]. Laser & Optoelectronics Progress, 2018, 55(9): 093004

严阳,华文深,崔子浩,伍锡山,刘恂. 高光谱分类体积的端元提取[J]. 激光与光电子学进展, 2018, 55(9): 093004

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