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Invariant Attribute Profiles: A Spatial-Frequency Joint Feature Extractor for Hyperspectral Image Classification

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dc.contributor Háskóli Íslands
dc.contributor University of Iceland
dc.contributor.author Hong, Danfeng
dc.contributor.author Wu, Xin
dc.contributor.author Ghamisi, Pedram
dc.contributor.author Chanussot, Jocelyn
dc.contributor.author Yokoya, Naoto
dc.contributor.author Zhu, Xiao Xiang
dc.date.accessioned 2021-01-11T13:29:10Z
dc.date.available 2021-01-11T13:29:10Z
dc.date.issued 2020-06
dc.identifier.citation Hong, D., Wu, X., Ghamisi, P., Chanussot, J., Yokoya, N., Zhu, X.X., 2020. Invariant Attribute Profiles: A Spatial-Frequency Joint Feature Extractor for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing 58, 3791–3808. doi:10.1109/tgrs.2019.2957251
dc.identifier.issn 0196-2892
dc.identifier.issn 1558-0644 (eISSN)
dc.identifier.uri https://hdl.handle.net/20.500.11815/2342
dc.description Publisher's version (útgefin grein)
dc.description.abstract So far, a large number of advanced techniques have been developed to enhance and extract the spatially semantic information in hyperspectral image processing and analysis. However, locally semantic change, such as scene composition, relative position between objects, spectral variability caused by illumination, atmospheric effects, and material mixture, has been less frequently investigated in modeling spatial information. Consequently, identifying the same materials from spatially different scenes or positions can be difficult. In this article, we propose a solution to address this issue by locally extracting invariant features from hyperspectral imagery (HSI) in both spatial and frequency domains, using a method called invariant attribute profiles (IAPs). IAPs extract the spatial invariant features by exploiting isotropic filter banks or convolutional kernels on HSI and spatial aggregation techniques (e.g., superpixel segmentation) in the Cartesian coordinate system. Furthermore, they model invariant behaviors (e.g., shift, rotation) by the means of a continuous histogram of oriented gradients constructed in a Fourier polar coordinate. This yields a combinatorial representation of spatial-frequency invariant features with application to HSI classification. Extensive experiments conducted on three promising hyperspectral data sets (Houston2013 and Houston2018) to demonstrate the superiority and effectiveness of the proposed IAP method in comparison with several state-of-the-art profile-related techniques.
dc.description.sponsorship This work was supported in part by the German Research Foundation (DFG) under Grant ZH 498/7-2, in part by the Helmholtz Association through the Framework of the Young Investigators Group SiPEO under Grant VH-NG-1018, and in part by the European Research Council (ERC) through the European Union’s Horizon 2020 Research and Innovation Programme (Acronym: So2Sat) under Grant ERC-2016-StG-714087. The work of Naoto Yokoya was supported by the Japan Society for the Promotion of Science under Grant KAKENHI 18K18067. (Corresponding author: Xiao Xiang Zhu.) Danfeng Hong and Xiao Xiang Zhu are with the Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), 82234 Weßling, Germany, and also with the Signal Processing in Earth Observation (SiPEO), Technical University of Munich (TUM), 80333 Munich, Germany (e-mail: danfeng.hong@dlr.de; xiaoxiang.zhu@dlr.de).
dc.format.extent 3791-3808
dc.language.iso en
dc.publisher Institute of Electrical and Electronics Engineers (IEEE)
dc.relation info:eu-repo/grantAgreement/EC/H2020/714087
dc.relation.ispartofseries IEEE Transactions on Geoscience and Remote Sensing;58(6)
dc.rights info:eu-repo/semantics/openAccess
dc.subject Attribute profile (AP)
dc.subject Feature extraction
dc.subject Fourier
dc.subject Frequency
dc.subject Hyperspectral image
dc.subject Invariant
dc.subject Remote sensing
dc.subject Spatial information modeling
dc.subject Spatial-spectral classification
dc.subject Fjarkönnun
dc.subject Myndgreining (upplýsingatækni)
dc.title Invariant Attribute Profiles: A Spatial-Frequency Joint Feature Extractor for Hyperspectral Image Classification
dc.type info:eu-repo/semantics/article
dcterms.license This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
dc.description.version Peer Reviewed
dc.identifier.journal IEEE Transactions on Geoscience and Remote Sensing
dc.identifier.doi 10.1109/TGRS.2019.2957251
dc.relation.url http://xplorestaging.ieee.org/ielx7/36/9097824/08961105.pdf?arnumber=8961105
dc.contributor.department Rafmagns- og tölvuverkfræðideild (HÍ)
dc.contributor.department Faculty of Electrical and Computer Engineering (UI)
dc.contributor.school Verkfræði- og náttúruvísindasvið (HÍ)
dc.contributor.school School of Engineering and Natural Sciences (UI)

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