Treffer: Vertex component analysis: A fast algorithm to extract endmembers spectra from hyperspectral data
Title:
Vertex component analysis: A fast algorithm to extract endmembers spectra from hyperspectral data
Authors:
Source:
Pattern recognition and image analysis (Puerto de Andratx, 4-6 June 2003)Lecture notes in computer science. :626-635
Publisher Information:
Berlin: Springer, 2003.
Publication Year:
2003
Physical Description:
print, 28 ref
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Telecommunications, Télécommunications, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Intelligence artificielle, Artificial intelligence, Reconnaissance des formes. Traitement numérique des images. Géométrie algorithmique, Pattern recognition. Digital image processing. Computational geometry, Algorithme rapide, Fast algorithm, Algoritmo rápido, Analyse algorithme, Algorithm analysis, Análisis algoritmo, Analyse spectrale, Spectral analysis, Análisis espectral, Capteur imagerie hyperspectral, Hyperspectral imaging sensor, Sensor hiperespectral de formación de imágenes, Complexité algorithme, Algorithm complexity, Complejidad algoritmo, Corps flottant, Floating body, Cuerpo flotante, Donnée observation, Observation data, Dato observación, Mesure sans contact, Non contact measurement, Medida sin contacto, Mélange, Mixture, Mezcla, Pixel, Signature spectrale, Spectral signature, Signatura espectral, Taille échantillon, Sample size, Tamaño muestra, Télédétection, Remote sensing, Teledetección, Télémesure, Telemetry, Telemedida, Vertex, Vértice
Document Type:
Konferenz
Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Instituto Superior de Engenharia de Lisboa and Instituto de Telecomunicações, R. Conselheiro Emídio Navarro N 1, edifício 5, 1949-014 Lisboa, Portugal
Instituto Superior Técnico and Instituto de Telecomunicações, Av. Rovisco Pais, Torre Norte, Piso 10, 1049-001 Lisboa, Portugal
Instituto Superior Técnico and Instituto de Telecomunicações, Av. Rovisco Pais, Torre Norte, Piso 10, 1049-001 Lisboa, Portugal
ISSN:
0302-9743
Rights:
Copyright 2004 INIST-CNRS
CC BY 4.0
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
CC BY 4.0
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
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.15491848
Database:
PASCAL Archive
Weitere Informationen
Linear spectral mixture analysis, or linear unmixing, has proven to be a useful tool in hyperspectral remote sensing applications. It aims at estimating the number of reference substances, also called endmembers, their spectral signature and abundance fractions, using only the observed data (mixed pixels). This paper presents new method that performs unsupervised endmember extraction from hyperspectral data. The algorithm exploits a simple geometric fact: endmembers are vertices of a simplex. The algorithm complexity, measured in floating points operations, is O(n), where n is the sample size. The effectiveness of the proposed scheme is illustrated using simulated data.