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

作者：Zhao Chun-hui^*; Tian Ming-hua; Qi Bin; Wang Yu-lei

来源：Journal of Central South University, 2016, 23(8): 1990-2000.

DOI：10.1007/s11771-016-3256-0

• variation pixels
• hyperspectral
• simplex
• variation index
• local entropy
• kernel spatial attraction

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 alpha. 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 alpha when dealing with no prior knowledge hyperspectral data.

• 出版日期2016-8
• 单位哈尔滨工程大学