A variation pixels identification method based on kernel spatial attraction model and local entropy for robust endmember extraction

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• 作者：Chun-hui Zhao 赵春晖 ; Ming-hua Tian 田明华 ; Bin Qi 齐滨…
• 关键词：Key wordsvariation pixels ; hyperspectral ; simplex ; variation index ; local entropy ; kernel spatial attraction
• 刊名：Journal of Central South University
• 出版年：2016
• 出版时间：August 2016
• 年：2016
• 卷：23
• 期：8
• 页码：1990-2000
• 全文大小：2,582 KB
• 刊物类别：Engineering
• 刊物主题：Engineering, general
  Metallic Materials
  Chinese Library of Science
  
• 出版者：Central South University, co-published with Springer
• ISSN：2227-5223
• 卷排序：23

文摘

A variation pixels identification method was proposed aiming at depressing the effect of variation pixels, which dilates the theoretical hyperspectral data simplex and misguides volume evaluation of the simplex. With integration of both spatial and spectral information, this method quantitatively defines a variation index for every pixel. The variation index is proportional to pixels local entropy but inversely proportional to pixels kernel spatial attraction. The number of pixels removed was modulated by an artificial threshold factor a. Two real hyperspectral data sets were employed to examine the endmember extraction results. The reconstruction errors of preprocessing data as opposed to the result of original data were compared. The experimental results show that the number of distinct endmembers extracted has increased and the reconstruction error is greatly reduced. 100% is an optional value for the threshold factor a when dealing with no prior knowledge hyperspectral data.