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dc.contributor.authorLi, Huapeng
dc.contributor.authorZhang, Shuqing
dc.contributor.authorZhang, Ce
dc.contributor.authorLi, Ping
dc.contributor.authorCropp, Roger
dc.date.accessioned2018-07-02T22:39:41Z
dc.date.available2018-07-02T22:39:41Z
dc.date.issued2017
dc.identifier.issn0143-1161
dc.identifier.doi10.1080/01431161.2017.1368102
dc.identifier.urihttp://hdl.handle.net/10072/377626
dc.description.abstractThe rapid development of earth observation technology has produced large quantities of remote-sensing data. Unsupervised classification (i.e. clustering) of remote-sensing images, an important means to acquire land-use/cover information, has become increasingly in demand due to its simplicity and ease of application. Traditional methods, such as k-means, struggle to solve this NP-hard (Non-deterministic Polynomial hard) image classification problem. Particle swarm optimization (PSO), always achieving better result than k-means, has recently been applied to unsupervised image classification. However, PSO was also found to be easily trapped on local optima. This article proposes a novel unsupervised Levy flight particle swarm optimization (ULPSO) method for image classification with balanced exploitation and exploration capabilities. It benefits from a new searching strategy: the worst particle in the swarm is targeted and its position is updated with Levy flight at each iteration. The effectiveness of the proposed method was tested with three types of remote-sensing imagery (Landsat Thematic Mapper (TM), Flightline C1 (FLC), and QuickBird) that are distinct in terms of spatial and spectral resolution and landscape. Our results showed that ULPSO is able to achieve significantly better and more stable classification results than k-means and the other two intelligent methods based on genetic algorithm (GA) and particle swarm optimization (PSO) over all of the experiments. ULPSO is, therefore, recommended as an effective alternative for unsupervised remote-sensing image classification.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherTaylor & Francis
dc.relation.ispartofpagefrom6970
dc.relation.ispartofpageto6992
dc.relation.ispartofissue23
dc.relation.ispartofjournalInternational Journal of Remote Sensing
dc.relation.ispartofvolume38
dc.subject.fieldofresearchPhotogrammetry and Remote Sensing
dc.subject.fieldofresearchPhysical Geography and Environmental Geoscience
dc.subject.fieldofresearchGeomatic Engineering
dc.subject.fieldofresearchcode090905
dc.subject.fieldofresearchcode0406
dc.subject.fieldofresearchcode0909
dc.titleA novel unsupervised Levy flight particle swarm optimization (ULPSO) method for multispectral remote-sensing image classification
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© 2017 Taylor & Francis (Routledge). This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Remote Sensing on 24 August 2017, available online: https://doi.org/10.1080/01431161.2017.1368102
gro.hasfulltextFull Text
gro.griffith.authorCropp, Roger A.


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