Assessing and Comparing the Performance of Endmember Extraction Methods in Multiple Change Detection Using Hyperspectral Data

Abstract

Abstract. Endmember extraction is a process to identify the hidden pure source signals from the mixture. Endmember finding has become increasingly important in hyperspectral data exploitation because endmembers can be used to specify unknown particular spectral classes. This paper evaluates the change detection problem in bi-temporal hyperspectral remote sensing images using the unmixing process. A complete spectral unmixing process contains estimating the number of endmembers, endmember extraction and abundance estimation. Endmember extraction is a vital step in spectral unmixing of hyperspectral images. Hyperspectral change detection by unmixing has the potential to provide subpixel information from hyperspectral images. In this study, four methods including Simplex Identification via variable Splitting and Augmented Lagrangian (SISAL), N-finder algorithm (N-FINDR), Vertex Component Analysis (VCA), and Fast algorithm for linearly Unmixing (FUN) are used to produce multiple change detection maps. This paper explores and compares the performance of these methods in multiple change detection. The empirical results reveal the superiority of the FUN method in providing multiple change map with an overall accuracy of 87% and a kappa coefficient of 0.70.

Towards the Concurrent Execution of Multiple Hyperspectral Imaging Applications by Means of Computationally Simple Operations

“…In this regard, if background spectral signatures are obtained in previous flights, changes in the land surface could be detected as anomalous entities by measuring the differences among abundances between bitemporal HSIs [57,58]. On the contrary, when background pixels are not known a priori, pixels extracted from each image block, M k , can be seen as local endmembers.…”

Examining long-term natural vegetation dynamics in the Aral Sea Basin applying the linear spectral mixture model

“…There are many methods to select image endmembers. But the disadvantage of the PPI algorithm is that the selected vector has a high degree of arbitrariness; the performance of the N-FINDR algorithm is largely related to the nature of the initially selected endmember ( Zhong, Zhao & Zhang, 2014 ; Jafarzadeh & Hasanlou, 2019 ); and the nature of the AMEE algorithm also depends on the relationship between the spatial characteristics of the structure element and the spectral distribution in the scene ( Li et al, 2011 ). Generally speaking, all kinds of endmember extraction algorithms are still in the exploratory stage, and each algorithm has its own advantages and disadvantages, which needs further improvement.…”

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