Spectral-Spatial Hyperspectral Image Segmentation Using Subspace Multinomial Logistic Regression and Markov Random Fields
- Author
- JUN LI1 ; BIOUCAS-DIAS, Jose M2 ; PLAZA, Antonio1
[1] Hyperspectral Computing Laboratory. Department of Technology of Computers and Communications, University of Extremadura, 10071 Caceres, Spain
[2] Instituto de Telecomunicações and the Instituto Superior Técnico, Technical University of Lisbon, 1049-001 Lisbon, Portugal - Source
IEEE transactions on geoscience and remote sensing. 2012, Vol 50, Num 3, pp 809-823, 15 p ; ref : 51 ref
- CODEN
- IGRSD2
- ISSN
- 0196-2892
- Scientific domain
- Agronomy, agriculture, phytopathology; Ecology; Geology; Geophysics; Telecommunications
- Publisher
- Institute of Electrical and Electronics Engineers, New York, NY
- Publication country
- United States
- Document type
- Article
- Language
- English
- Author keyword
- Hyperspectral image segmentation Markov random field (MRF) multinomial logistic regression (MLR) subspace projection method
- Keyword (fr)
- Algorithme Bruit Bâti Classification Expansion Imagerie Optimisation Performance Pixel Probabilité Projection Régression Segmentation
- Keyword (en)
- algorithms noise frame structure classification expansion imagery optimization performances Pixel probability projection regression segmentation
- Keyword (es)
- Algoritmo Clasificación Expansión Imaginería Optimización Pixel Probabilidad Proyección Regresión
- Classification
- Pascal
- 001 Exact sciences and technology / 001E Earth, ocean, space / 001E01 Earth sciences / 001E01M Internal geophysics / 001E01M04 Applied geophysics
- Discipline
- Earth sciences
- Origin
- Inist-CNRS
- Database
- PASCAL
- INIST identifier
- 25565132
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
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DOI
10.1109/TGRS.2011.2162649 
https://dx.doi.org/10.1109/TGRS.2011.2162649
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