Treffer: Spectral derivative feature coding for hyperspectral signature analysis
Title:
Spectral derivative feature coding for hyperspectral signature analysis
Source:
Pattern recognition. 42(3):395-408
Publisher Information:
Kidlington: Elsevier, 2009.
Publication Year:
2009
Physical Description:
print, 11 ref
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Telecommunications, Télécommunications, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Telecommunications et theorie de l'information, Telecommunications and information theory, Théorie de l'information, du signal et des communications, Information, signal and communications theory, Théorie du signal et des communications, Signal and communications theory, Signal, bruit, Signal, noise, Représentation du signal. Analyse spectrale, Signal representation. Spectral analysis, Codage, codes, Coding, codes, Traitement du signal, Signal processing, Divers, Miscellaneous, Analyse contenu, Content analysis, Análisis contenido, Analyse signal, Signal analysis, Análisis de señal, Analyse signature, Signature analysis, Análisis firma, Analyse spectre, Spectrum analysis, Análisis espectro, Analyse texture, Texture analysis, Análisis textura, Codage binaire, Binary coding, Codificación binaria, Discrimination, Discriminación, Evaluation performance, Performance evaluation, Evaluación prestación, Extraction caractéristique, Feature extraction, Traitement signal, Signal processing, Procesamiento señal, Spectral analysis manager (SPAM), Spectral derivative feature coding (SDFC), Spectral feature-based binary coding (SFBC)
Document Type:
Fachzeitschrift
Article
File Description:
text
Language:
English
Author Affiliations:
Remote Sensing Signal and Image Processing Laboratory, Department of Computer Science and Electrical Engineering. University of Maryland Baltimore County, Baltimore, MD 21250, United States
Department of Electrical Engineering, National Chung Hsing University, Taichung, Tawain, Province of China
Environmental Restoration and Disaster Reduction Research Center, National Chung Hsing University, Taichung, Tawain, Province of China
Department of Medical Research, China Medical University Hospital, Taichung, Tawain, Province of China
Department of Radiology, China Medical University Hospital, Taichung, Tawain, Province of China
Department of Electrical Engineering, National Chung Hsing University, Taichung, Tawain, Province of China
Environmental Restoration and Disaster Reduction Research Center, National Chung Hsing University, Taichung, Tawain, Province of China
Department of Medical Research, China Medical University Hospital, Taichung, Tawain, Province of China
Department of Radiology, China Medical University Hospital, Taichung, Tawain, Province of China
ISSN:
0031-3203
Rights:
Copyright 2009 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:
Telecommunications and information theory
Accession Number:
edscal.21243091
Database:
PASCAL Archive
Weitere Informationen
This paper presents a new approach to hyperspectral signature analysis, called spectral derivative feature coding (SDFC). It is derived from texture features used in texture classification to dictate gradient changes among adjacent bands in characterizing spectral variations so as to improve better spectral discrimination and classification. In order to evaluate its performance, two known binary coding methods, spectral analysis manager (SPAM) and spectral feature-based binary coding (SFBC) are used to conduct comparative analysis. Experimental results demonstrate that the proposed SDFC performs more effectively in capturing spectral characteristics than do SPAM and SFBC.