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Endmember Extraction From Hyperspectral Imagery Based on Probabilistic Tensor Moments
dc.contributor.author | Fernandez-Beltran, Ruben | |
dc.contributor.author | Pla, Filiberto | |
dc.contributor.author | Plaza, Antonio | |
dc.date.accessioned | 2020-12-11T10:52:52Z | |
dc.date.available | 2020-12-11T10:52:52Z | |
dc.date.issued | 2020-01-13 | |
dc.identifier.citation | FERNANDEZ-BELTRAN, Ruben; PLA, Filiberto; PLAZA, Antonio. Endmember Extraction From Hyperspectral Imagery Based on Probabilistic Tensor Moments. IEEE Geoscience and Remote Sensing Letters, 2020. | ca_CA |
dc.identifier.issn | 1545-598X | |
dc.identifier.uri | http://hdl.handle.net/10234/190853 | |
dc.description.abstract | This letter presents a novel hyperspectral endmember extraction approach that integrates a tensor-based decomposition scheme with a probabilistic framework in order to take advantage of both technologies when uncovering the signatures of pure spectral constituents in the scene. On the one hand, statistical unmixing models are generally able to provide accurate endmember estimates by means of rather complex optimization algorithms. On the other hand, tensor decomposition techniques are very effective factorization tools which are often constrained by the lack of physical interpretation within the remote sensing field. In this context, this letter develops a new hybrid endmember extraction approach based on the decomposition of the probabilistic tensor moments of the hyperspectral data. Initially, the input image reflectance values are modeled as a collection of multinomial distributions provided by a family of Dirichlet generalized functions. Then, the unmixing process is effectively conducted by the tensor decomposition of the thirdorder probabilistic tensor moments of the multivariate data. Our experiments, conducted over four hyperspectral data sets, reveal that the proposed approach is able to provide efficient and competitive results when compared to different state-of-the-art endmember extraction methods. | ca_CA |
dc.format.extent | 13 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | Institute of Electrical and Electronics Engineers | ca_CA |
dc.relation.isPartOf | IEEE Geoscience and Remote Sensing Letters ( Volume: 17, Issue: 12, Dec. 2020) | ca_CA |
dc.rights | © 2020 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information. | ca_CA |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | * |
dc.source | https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=8859 | ca_CA |
dc.subject | endmember extraction | ca_CA |
dc.subject | hyperspectral unmixing | ca_CA |
dc.subject | statistical models | ca_CA |
dc.subject | tensor decomposition | ca_CA |
dc.subject | biological system modeling | ca_CA |
dc.subject | matrix decomposition | ca_CA |
dc.subject | hyperspectral imaging | ca_CA |
dc.subject | probabilistic logic | ca_CA |
dc.subject | tensors | ca_CA |
dc.title | Endmember Extraction From Hyperspectral Imagery Based on Probabilistic Tensor Moments | ca_CA |
dc.type | info:eu-repo/semantics/article | ca_CA |
dc.identifier.doi | https://doi.org/10.1109/LGRS.2019.2963114 | |
dc.relation.projectID | APOSTD/2017/007, ESP2016-79503-C2-2-P, RTI2018-098651-B-C54, TIN2015-63646-C5-5-R, Junta de Extremadura/GR18060, H2020 EOXPOSURE/Project 734541 | ca_CA |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_CA |
dc.type.version | info:eu-repo/semantics/acceptedVersion | ca_CA |
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