An Active Learning Approach to Hyperspectral Data Classification
- Author
- RAJAN, Suju1 ; GHOSH, Joydeep1 ; CRAWFORD, Melba M2
[1] Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX 78712, United States
[2] Schools of Civil and Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, United States - Source
IEEE transactions on geoscience and remote sensing. 2008, Vol 46, Num 4, pp 1231-1242, 12 p ; 2 ; ref : 40 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
- Active learning hierarchical classifier multitemporal data semisupervised classifiers spatially separate data
- Keyword (fr)
- Classification Méthodologie Occupation sol Performance Signature spectrale Traitement donnée Télédétection Apprentissage Classificateur Télédétection hyperspectrale
- Keyword (en)
- classification methodology land cover performances spectral signature data processing remote sensing
- Keyword (es)
- Clasificación Metodología Tratamiento datos Detección a distancia
- 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
- 20246678
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.2007.910220 
https://dx.doi.org/10.1109/TGRS.2007.910220
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