标题：Hyperspectral image unmixing for classification and recognition: An overview
作者：Nie, Mingyu ;Liu, Zhi ;Xu, Hui ;Xiao, Xiaoyan ;Su, Fangqi ;Chang, Jun ;Li, Xiaomei
作者机构：[Nie, Mingyu ;Liu, Zhi ;Xu, Hui ;Su, Fangqi ;Chang, Jun ] School of Information Science and Engineering, Shandong University, China;[Li, Xiaomei ] Dep 更多
来源：International Journal of Signal Processing, Image Processing and Pattern Recognition
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. © 2015 SERSC.