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dc.contributor.authorWang, J
dc.contributor.authorZhou, J
dc.contributor.authorHuang, W
dc.date.accessioned2020-03-05T03:49:31Z
dc.date.available2020-03-05T03:49:31Z
dc.date.issued2019
dc.identifier.issn1939-1404
dc.identifier.doi10.1109/JSTARS.2019.2955097
dc.identifier.urihttp://hdl.handle.net/10072/392120
dc.description.abstractHyperspectral remote sensing sensors have the ability to capture a wide range of spectrum of ground objects with hundreds to thousands of bands. The obtained hyperspectral images contain more detailed spectral information than conventional panchromatic or color images. However, redundant and noisy data could also be introduced into the images and may impair further processing of the data. Removing the redundancy and noisy bands becomes one of the most important preprocessing steps of hyperspectral imaging. As a feature selection method, an attention mechanism has been successfully used in computer vision and natural language processing to enable algorithms to concentrate on the most significant data. In this article, we propose a band attention network for hyperspectral image classification. This network can automatically learn to attend to the desired band set that maximizes the classification accuracy. With careful design, the band attention framework can undertake both band weighting and band selection tasks. When working in the band weighting mode, the proposed band attention framework can learn a band weighting vector that models the relationship between all the bands. For band selection, our network can be adapted as two different types of band selection networks that select the significant bands and discard the useless bands. In both tasks, the attention and classification can be learned end to end. The experimental results prove the effectiveness and advantages of the proposed work.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers
dc.publisher.placeUnited States
dc.relation.ispartofpagefrom4712
dc.relation.ispartofpageto4727
dc.relation.ispartofissue12
dc.relation.ispartofjournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
dc.relation.ispartofvolume12
dc.subject.fieldofresearchArtificial intelligence
dc.subject.fieldofresearchPhysical geography and environmental geoscience
dc.subject.fieldofresearchGeomatic engineering
dc.subject.fieldofresearchcode4602
dc.subject.fieldofresearchcode3709
dc.subject.fieldofresearchcode4013
dc.titleAttend in Bands: Hyperspectral Band Weighting and Selection for Image Classification
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationWang, J; Zhou, J; Huang, W, Attend in Bands: Hyperspectral Band Weighting and Selection for Image Classification, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12 (12), pp. 4712-4727
dc.date.updated2020-03-05T00:37:34Z
gro.hasfulltextNo Full Text
gro.griffith.authorZhou, Jun


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