Unsupervised Band Selection for Hyperspectral Image Classification: Particle Swarm Optimization via Cross-Domain Knowledge Transfer
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Le résumé fourni par la source
Band selection (BS) is a key method in Hyperspectral image (HSI) classification that helps to reduce the computational burden and improve the class separability. However, with the emerging of unmanned aerial vehicle (UAV)-borne HSI datasets, their attributes such as high spatial and spectral resolution as well as large-scale samples pose serious challenges to the existing BS methods, making them inefficient. In addition, the efficient utilization of the prior knowledge from the data collected by fixed UAV-borne sensors in different regions is often easily overlooked. In view of these issues, this paper proposes a neural network-assisted particle swarm optimization (PSO) algorithm for cross-domain BS of UAV-borne HSIs. First, a knowledge learning strategy is designed for the source domain, which applies a neural network model to learn the useful prior knowledge in labeled source domain data. Then, a network-assisted PSO algorithm is introduced to search for the optimal subset of bands in the target domain under the guidance of the valid prior knowledge captured from the source domain by the network model. Moreover, a similarity-based grouping strategy is designed to group similar bands and then select bands from each group with the aims of reducing the redundant information in the subset of bands. Finally, experimental results on three common UAV-borne HSI datasets show that our proposed method can efficiently handle UAV-borne HSI data with large samples, as it is able to find a subset of bands with higher quality compared to several state-of-the-art BS methods.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Unsupervised Band Selection for Hyperspectral Image Classification: Particle Swarm Optimization via Cross-Domain Knowledge Transfer
- Date Crossref
- 01/04/2026
- Éditeur
- Institute of Electrical and Electronics Engineers (IEEE)
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Shenzhen University pays non établi dans la noticeUniversité ou école supérieure
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Shenzhen Technology University pays non établi dans la noticeUniversité ou école supérieure
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Tsinghua University Department of Automation pays non établi dans la noticeUniversité ou école supérieure
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Carleton University pays non établi dans la noticeUniversité ou école supérieure
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Shenzhen MSU-BIT University Artificial Intelligence Research Institute pays non établi dans la noticeUniversité ou école supérieure
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College of Computer Science and Software Engineering pays non établi dans la noticeUniversité ou école supérieure
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College of Big Data and Internet pays non établi dans la noticeUniversité ou école supérieure
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School of Information Technology pays non établi dans la noticeUniversité ou école supérieure
Shenzhen University, Shenzhen Technology University et Department of Automation — Tsinghua University, avec 5 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.