An initially robust minimum simplex volume-based method for linear hyperspectral unmixing

doi:10.1080/01431161.2024.2305628

2024-02-02

PRE

Yanyan Li *

Tao Tan

DOI：10.1080/01431161.2024.2305628

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摘要

En 中文

Initialization plays an important role in the accuracy of endmember extraction algorithms (EEAs) in linear hyperspectral unmixing (LHU). Random initialization can lead to varying endmembers generated by EEAs. To address this challenge, an initialization strategy has been introduced, encompassing vertex component analysis (VCA), automatic target generation process (ATGP), among others. These techniques significantly contribute to enhancing the accuracy of EEAs. However, complex initialization is sometimes less preferable, prompting the unexplored question of whether there exists an EEA robust to initialization. This paper focuses on analyzing this issue within the context of minimum simplex volume-based (MV) methods, which have received considerable attention in the past two decades due to their robustness against the absence of pure pixels. MV methods typically formulate LHU as an optimization problem, most of which includes a non-convex volume term. Additionally, many MV methods use VCA as an initialization strategy. Firstly, this paper demonstrates that the variable splitting augmented Lagrangian approach (SISAL), as a representative non-convex MV method, heavily depends on initialization. To our knowledge, the impact of initialization for MV methods has not been thoroughly analyzed before. Furthermore, this paper proposes an initially robust MV method by introducing a new convex MV term. Numerical experiments conducted on simulated and real datasets demonstrate its outstanding performance in accuracy and robustness to initialization. Throughout the experiments the proposed method proves to be the most stable, which is crucial in real scene where the ground truth is unknown beforehand.

Keyword:

Linear Hyperspectral Unmixing

Endmember extraction algorithms (EEAs)

endmember initialization algorithm (EIA)

minimum simplex volume (MV)

convex

adaptive moment method (Adam)

the log-absolute-determinant compound function (LADF)

the nonnegative matrix factorization-based minimum simplex volume method (NMF-MV), principal component analysis (PCA)