EMT-HEE: An Evolutionary Multi-Tasking Method for Hyperspectral Endmember Extraction

Le résumé fourni par la source

Endmember extraction (EE) plays an essential role in the unmixing of hyperspectral images, and many EE algorithms have been proposed. Among them, evolutionary algorithm (EA) based EE algorithms attract much attention due to the EA's powerful global search ability. However, hyperspectral EE problem itself is a constrained and sparse large-scale optimization problem, and it is difficult to search the optimal solutions efficiently. In this paper, to address this problem, we tackle this complex optimization problem from the view of evolutionary multi-tasking. Specifically, an evolutionary multi-tasking method for hyperspectral EE, termed EMT-HEE, is suggested, where two related tasks cooperate with each other to achieve endmembers with higher quality. In EMT-HEE, the original hyperspectral EE problem is regarded as the main task, whose aim is to obtain the final accurate endmembers. Meanwhile, an unconstrained task is constructed to assist the main task, and is used to explore the sparse large-scale search space thoroughly and avoid the local optima. To implement the evolutionary multi-tasking idea, two populations ($MP$and$AP$) are evolved for the two tasks, respectively. During the evolving, a learning based solution generation strategy is suggested for the main task, which can produce high-quality solutions. Besides, a global search strategy with dynamic ranges is developed for the assisting task, which generates the solutions with more diversity. To make full use of the advantage of each task, a pair of knowledge transferring strategy (including the “assisting-to-main repairing strategy” and the “main-to-assisting enhancing strategy”) is proposed, which improves the performance of each task greatly. The competitiveness of EMT-HEE is validated on different hyperspectral datasets, and EMT-HEE can extract more accurate endmembers than the state-of-the-art EE algorithms.

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