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Article: Combining t-Distributed Stochastic Neighbor Embedding with Convolutional Neural Networks for Hyperspectral Image Classification

TitleCombining t-Distributed Stochastic Neighbor Embedding with Convolutional Neural Networks for Hyperspectral Image Classification
Authors
Keywordst-distributed stochastic neighbor embedding (t-SNE)
hyperspectral image (HSI) classification
Assembly fusion
dimensionality reduction
convolutional neural network (CNN)
Issue Date2020
Citation
IEEE Geoscience and Remote Sensing Letters, 2020, v. 17, n. 8, p. 1368-1372 How to Cite?
AbstractHyperspectral images (HSIs), featured by high spectral resolution over a wide range of electromagnetic spectra, have been widely used to characterize materials with subtle differences in the spectral domain. However, a large number of bands and an insufficient number of sample pixels for each class are challenging for traditional machine learning-based classifiers. As alternative tools for feature extraction, neural networks have received extensive attention. This letter proposes to combine t-distributed stochastic neighbor embedding (t-SNE) with a convolutional neural network (CNN) for HSI classification. Our framework is designed to automatically capture the potential assembly features, which are extracted from both the dimension-reduced CNN (DR-CNN) and the multiscale-CNN. Experimental results show that the proposed classification framework outperforms several state-of-the-art techniques for three real data sets.
Persistent Identifierhttp://hdl.handle.net/10722/298346
ISSN
2020 Impact Factor: 3.966
2020 SCImago Journal Rankings: 1.372
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorGao, Lianru-
dc.contributor.authorGu, Daixin-
dc.contributor.authorZhuang, Lina-
dc.contributor.authorRen, Jinchang-
dc.contributor.authorYang, Dong-
dc.contributor.authorZhang, Bing-
dc.date.accessioned2021-04-08T03:08:12Z-
dc.date.available2021-04-08T03:08:12Z-
dc.date.issued2020-
dc.identifier.citationIEEE Geoscience and Remote Sensing Letters, 2020, v. 17, n. 8, p. 1368-1372-
dc.identifier.issn1545-598X-
dc.identifier.urihttp://hdl.handle.net/10722/298346-
dc.description.abstractHyperspectral images (HSIs), featured by high spectral resolution over a wide range of electromagnetic spectra, have been widely used to characterize materials with subtle differences in the spectral domain. However, a large number of bands and an insufficient number of sample pixels for each class are challenging for traditional machine learning-based classifiers. As alternative tools for feature extraction, neural networks have received extensive attention. This letter proposes to combine t-distributed stochastic neighbor embedding (t-SNE) with a convolutional neural network (CNN) for HSI classification. Our framework is designed to automatically capture the potential assembly features, which are extracted from both the dimension-reduced CNN (DR-CNN) and the multiscale-CNN. Experimental results show that the proposed classification framework outperforms several state-of-the-art techniques for three real data sets.-
dc.languageeng-
dc.relation.ispartofIEEE Geoscience and Remote Sensing Letters-
dc.subjectt-distributed stochastic neighbor embedding (t-SNE)-
dc.subjecthyperspectral image (HSI) classification-
dc.subjectAssembly fusion-
dc.subjectdimensionality reduction-
dc.subjectconvolutional neural network (CNN)-
dc.titleCombining t-Distributed Stochastic Neighbor Embedding with Convolutional Neural Networks for Hyperspectral Image Classification-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/LGRS.2019.2945122-
dc.identifier.scopuseid_2-s2.0-85082307350-
dc.identifier.volume17-
dc.identifier.issue8-
dc.identifier.spage1368-
dc.identifier.epage1372-
dc.identifier.eissn1558-0571-
dc.identifier.isiWOS:000552271800016-
dc.identifier.issnl1545-598X-

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