Endmember Extraction From Hyperspectral Imagery Based on Probabilistic Tensor Moments
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comunitat-uji-handle2:10234/43662
comunitat-uji-handle3:10234/43643
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INVESTIGACIONMetadatos
Título
Endmember Extraction From Hyperspectral Imagery Based on Probabilistic Tensor MomentsFecha de publicación
2020-01-13Editor
Institute of Electrical and Electronics EngineersISSN
1545-598XCita bibliográfica
FERNANDEZ-BELTRAN, Ruben; PLA, Filiberto; PLAZA, Antonio. Endmember Extraction From Hyperspectral Imagery Based on Probabilistic Tensor Moments. IEEE Geoscience and Remote Sensing Letters, 2020.Tipo de documento
info:eu-repo/semantics/articleVersión
info:eu-repo/semantics/acceptedVersionPalabras clave / Materias
Resumen
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 ... [+]
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. [-]
Publicado en
IEEE Geoscience and Remote Sensing Letters ( Volume: 17, Issue: 12, Dec. 2020)Proyecto de investigación
APOSTD/2017/007, ESP2016-79503-C2-2-P, RTI2018-098651-B-C54, TIN2015-63646-C5-5-R, Junta de Extremadura/GR18060, H2020 EOXPOSURE/Project 734541Derechos de acceso
© 2020 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
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