L₁ Sparsity-Constrained Archetypal Analysis Algorithm for Hyperspectral Unmixing

Xu, Min; Yang, Zhiru; Ren, Guangbo; Shen, Hui; Liu, Shanwei; Liu, Wei; Ye, Chuanlong

doi:10.1109/lgrs.2022.3164054

Cited by 7 publications

(1 citation statement)

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“…Recently, archetypal analysis has been successfully harnessed for blind unmixing in [14]. In [15], ℓ 1 sparsity-constrained archetypal analysis was proposed for blind unmixing where the sparsity was enforced on the abundances. In this paper, we propose solving sparse unmixing using archetypal analysis (SUnAA).…”

Section: Introductionmentioning

confidence: 99%

SUnAA: Sparse Unmixing Using Archetypal Analysis

Rasti

Zouaoui

Mairal

et al. 2023

IEEE Geosci. Remote Sensing Lett.

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This paper introduces a new sparse unmixing technique using archetypal analysis (SUnAA). First, we design a new model based on archetypal analysis. We assume that the endmembers of interest are a convex combination of endmembers provided by a spectral library and that the number of endmembers of interest is known. Then, we propose a minimization problem. Unlike most conventional sparse unmixing methods, here the minimization problem is non-convex. We minimize the optimization objective iteratively using an active set algorithm. Our method is robust to the initialization and only requires the number of endmembers of interest. SUnAA is evaluated using two simulated datasets for which results confirm its better performance over other conventional and advanced techniques in terms of signal-to-reconstruction error. SUnAA is also applied to Cuprite dataset and the results are compared visually with the available geological map provided for this dataset. The qualitative assessment demonstrates the successful estimation of the minerals abundances and significantly improves the detection of dominant minerals compared to the conventional regression-based sparse unmixing methods. The Python implementation of SUnAA can be found at: https://github.com/BehnoodRasti/SUnAA.

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Section: Introductionmentioning

confidence: 99%