Reducing the Effect of the Endmembers’ Spectral Variability by Selecting the Optimal Spectral Bands
AbstractVariable environmental conditions cause different spectral responses of scene endmembers. Ignoring these variations affects the accuracy of fractional abundances obtained from linear spectral unmixing. On the other hand, the correlation between the bands of hyperspectral data is not considered by conventional methods developed for dealing with spectral variability. In this paper, a novel approach is proposed to simultaneously mitigate spectral variability and reduce correlation among different endmembers in hyperspectral datasets. The idea of the proposed method is to utilize the angular discrepancy of bands in the Prototype Space (PS), which is constructed using the endmembers of the image. Using the concepts of PS, in which each band is treated as a space point, we proposed a method to identify independent bands according to their angles. The proposed method comprised two main steps. In the first step, which aims to alleviate the spectral variability issue, image bands are prioritized based on their standard deviations computed over some sets of endmembers. Independent bands are then recognized in the prototype space, employing the angles between the prioritized bands. Finally, the unmixing process is done using the selected bands. In addition, the paper presents a technique to form a spectral library of endmembers’ variability (sets of endmembers). The proposed method extracts endmembers sets directly from the image data via a modified version of unsupervised spatial–spectral preprocessing. The performance of the proposed method was evaluated by five simulated images and three real hyperspectral datasets. The experiments show that the proposed method—using both groups of spectral variability reduction methods and independent band selection methods—produces better results compared to the conventional methods of each group. The improvement in the performance of the proposed method is observed in terms of more appropriate bands being selected and more accurate fractional abundance values being estimated. View Full-Text
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Ghaffari, O.; Zoej, M.J.V.; Mokhtarzade, M. Reducing the Effect of the Endmembers’ Spectral Variability by Selecting the Optimal Spectral Bands. Remote Sens. 2017, 9, 884.
Ghaffari O, Zoej MJV, Mokhtarzade M. Reducing the Effect of the Endmembers’ Spectral Variability by Selecting the Optimal Spectral Bands. Remote Sensing. 2017; 9(9):884.Chicago/Turabian Style
Ghaffari, Omid; Zoej, Mohammad Javad Valadan; Mokhtarzade, Mehdi. 2017. "Reducing the Effect of the Endmembers’ Spectral Variability by Selecting the Optimal Spectral Bands." Remote Sens. 9, no. 9: 884.