Multi-fidelity evolutionary multitasking optimization for hyperspectral endmember extraction

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Endmember extraction plays an indispensable role in hyperspectral image processing, which is also an important step to decompose the mixed pixels in spectral unmixing. Most of the existing methods based on the principle of convex geometry and intelligent optimization algorithms have achieved an excellent result, but they also suffer from the influence of outliers and expensive computation respectively. Furthermore, it is difficult to determine the number of endmembers in a hyperspectral image without any prior conditions. In order to alleviate these problems, we propose a multi-fidelity evolutionary multitasking optimization framework for hyperspectral endmember extraction in this article. In the proposed framework, multiple tasks to extract different numbers of endmembers are uniformly coded into a single population, aiming to process these similar tasks simultaneously with the implicit genetic transfer. Accordingly, our proposed algorithm is capable of extracting the best endmember set within a certain range of the number of endmembers without any prior knowledge. In addition, an ensemble surrogate model is firstly built to approximate the high-fidelity fitness with the low-fidelity fitness in endmember extraction, which greatly alleviates the time-consuming problem of individual evaluation. The experimental results on the simulated and the real hyperspectral data demonstrate the effectiveness of the proposed framework. The accuracy and the efficiency of endmember extraction are greatly improved compared with other classic endmember extraction methods.