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dc.contributor.authorDing, X
dc.contributor.authorZhang, S
dc.contributor.authorLi, H
dc.contributor.authorWu, P
dc.contributor.authorDale, P
dc.contributor.authorLiu, L
dc.contributor.authorCheng, S
dc.date.accessioned2020-03-06T04:19:01Z
dc.date.available2020-03-06T04:19:01Z
dc.date.issued2020
dc.identifier.issn0143-1161
dc.identifier.doi10.1080/01431161.2019.1655810
dc.identifier.urihttp://hdl.handle.net/10072/392155
dc.description.abstractWith hundreds of spectral bands, the rise of the issue of dimensionality in the classification of hyperspectral images is usually inevitable. In this paper, a restrictive polymorphic ant colony algorithm (RPACA) based band selection algorithm (RPACA-BS) was proposed to reduce the dimensionality of hyperspectral images. In the proposed algorithm, both local and global searches were conducted considering band similarity. Moreover, the problem of falling into local optima, due to the selection of similar band subsets although travelling different paths, was solved by varying the pheromone matrix between ants moving in opposite directions. The performance of the proposed RPACA-BS algorithm was evaluated using three public datasets (the Indian Pines, Pavia University and Botswana datasets) based on average overall classification accuracy (OA) and CPU processing time. The experimental results showed that average OA of RPACA-BS was up to 89.80%, 94.96% and 92.17% for the Indian Pines, Pavia University and Botswana dataset, respectively, which was higher than that of the benchmarks, including the ant colony algorithm-based band selection algorithm (ACA-BS), polymorphic ant colony algorithm-based band selection algorithm (PACA-BS) and other band selection methods (e.g. the ant lion optimizer-based band selection algorithm). Meanwhile, the time consumed by RPACA-BS and PACA-BS were slightly lower than that of ACA-BS but obviously lower than that of other benchmarks. The proposed RPACA-BS method is thus able to effectively enhance the search abilities and efficiencies of the ACA-BS and PACA-BS algorithms to handle the complex band selection issue for hyperspectral remotely sensed images.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherInforma UK Limited
dc.relation.ispartofpagefrom1093
dc.relation.ispartofpageto1117
dc.relation.ispartofissue3
dc.relation.ispartofjournalInternational Journal of Remote Sensing
dc.relation.ispartofvolume41
dc.subject.fieldofresearchPhysical Geography and Environmental Geoscience
dc.subject.fieldofresearchGeomatic Engineering
dc.subject.fieldofresearchcode0406
dc.subject.fieldofresearchcode0909
dc.titleA restrictive polymorphic ant colony algorithm for the optimal band selection of hyperspectral remote sensing images
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationDing, X; Zhang, S; Li, H; Wu, P; Dale, P; Liu, L; Cheng, S, A restrictive polymorphic ant colony algorithm for the optimal band selection of hyperspectral remote sensing images, International Journal of Remote Sensing, 2020, 41 (3), pp. 1093-1117
dc.date.updated2020-03-06T04:17:21Z
gro.hasfulltextNo Full Text
gro.griffith.authorDale, Patricia E.


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