文章基本信息

标题：Hyperspectral Image Unmixing for Classification and Recognition: An Overview
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作者：Mingyu Nie ; Zhi Liu ; Hui Xu 等
期刊名称：International Journal of Signal Processing, Image Processing and Pattern Recognition
印刷版ISSN：2005-4254
出版年度：2015
卷号：8
期号：12
页码：223-236
DOI：10.14257/ijsip.2015.8.12.22
出版社：SERSC
摘要：The limited resolution of image sensors and the complex diversity of nature, cause mixed pixel problems in hyperspectral technology. Such problems are common, and increase the complexity of hyperspectral image processing. Hyperspectral unmixing is crucial for hyperspectral image classification and recognition. In unmixing, the image signatures are represented as a linear combination of the basic materials. Unmixing is the process of decomposing a mixed pixel into constituent materials, and calculating the corresponding fractional abundance. If pure materials (end members) are present in an image, unmixing can be divided into two steps, namely, end member extraction and abundance decomposition. On the other hand, if there is no pure material, researchers have devised and investigated unsupervised and semi-supervised spectral unmixing technology. This article presents an overview of the state-of-the-art methods of hyperspectral unmixing and their extensions.
关键词：hyperspectral; unmixing; end member extraction; unsupervised; NMF; ; semi-supervised