Endmember Selection With Adaptive Double Prior Model

Selecting a correct endmember subset from a large number of endmembers plays an important role in improving the performance of spectral unmixing. Sparse unmixing (SU) pro-vides a useful tool to solve this endmember selection problem. However, due to the complexity of endmembers in the spectral library and noise in hyperspectral data, this problem is ill-posed and remains a significant challenge. To address this, prior infor-mation can be employed to mitigate the problem. Existing algo-rithms, however, often overlook the possibility that the prior information may be incomplete or inaccurate, and consider only with a single prior model. To this end, this paper proposes an adaptive method designed to handle incorrect prior information and balance the tradeoff between the contributions of two sets of priors. In contrast to the conventional models, our method dy-namically adjusts optimal decision boundaries by introducing decision-making parameters (DMPs), thereby obtaining a better solution. Optimal weight conditions and robustness analysis are also presented to prove the performance of our method. Fur-thermore, experiments conducted on both simulated and real datasets demonstrate that the proposed method delivers robust and reliable performance, highlighting its effectiveness in ad-dressing the unmixing challenges. The code of our method is available at https://github.com/spdelphi/Double-Prior.