Abstract
Evolutionary algorithm (EA) with good search capability has been successfully extended to a mainstream band selection (BS) technique for hyperspectral images (HSIs). However, most of the existing methods still face two challenges: 1) falling into local optimum due to the single search strategy; 2) ignoring the problem of potential duplicate bands. To address these issues, this paper proposes an effective unsupervised BS method by using a diversified multi-objective particle swarm optimization (PSO) algorithm, called DPSO-BS. First, a new unsupervised BS model is designed, which applies the information entropy and structural similarity measure as two optimization objectives. Then, two complementary PSO search strategies are proposed to solve the above constructed BS model. In addition, a self-repair mechanism is designed to correct the offending solutions with duplicate bands. Experimental results on three HSI datasets demonstrate that DPSO-BS outperforms several state-of-the-art BS methods.
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Acknowledgment
This work was supported by the National Natural Science Foundation of China (NSFC) under Grants 62002230, 61836005 and 62272315; in part by the Natural Science Foundation of Guangdong Province under grant 2023A1515011296; in part by the Guangdong Regional Joint Foundation Key Project under Grant 2022B1515120076, and in part by the Shenzhen Science and Technology Program under Grants JCYJ20220531101411027 and JCYJ20190808164211203.
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Zhang, Y., Li, L., Xiao, Z., Lin, Q., Ming, Z. (2023). A Diversified Multi-objective Particle Swarm Optimization Algorithm for Unsupervised Band Selection of Hyperspectral Images. In: Tan, Y., Shi, Y., Luo, W. (eds) Advances in Swarm Intelligence. ICSI 2023. Lecture Notes in Computer Science, vol 13968. Springer, Cham. https://doi.org/10.1007/978-3-031-36622-2_38
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