Remotely sensed change detection based on artificial neural networks
- Author
- LONG DAI, X1 ; KHORRAM, S1
[1] Center for Earth Observation, North Carolina State University, Raleigh, North Carolina 27695-7106, United States - Source
Photogrammetric engineering and remote sensing. 1999, Vol 65, Num 10, pp 1187-1194 ; Illustration , Table ; ref : 36 ref
- CODEN
- PERSDV
- ISSN
- 0099-1112
- Scientific domain
- Agronomy, agriculture, phytopathology; Ecology; Geology; Geophysics; Oceanography
- Publisher
- American Society for Photogrammetry and Remote Sensing, Bethesda, MD
- Publication country
- United States
- Document type
- Article
- Language
- English
- Keyword (fr)
- Algorithme Classification Détection LANDSAT Maximum vraisemblance Précision Réseau neuronal Thematic Mapper Télédétection spatiale Caroline du Nord Amérique du Nord Etats Unis
- Keyword (en)
- algorithms classification detection Landsat maximum likelihood accuracy neural networks Thematic Mapper Space remote sensing North Carolina North America United States
- Keyword (es)
- Algoritmo Clasificación Precisión Red neuronal Teledetección espacial Carolina del norte America del norte Estados Unidos
- 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
- 1953266
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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