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Titolo:
A FAST 2-STAGE CLASSIFICATION METHOD FOR HIGH-DIMENSIONAL REMOTE-SENSING DATA
Autore:
TU TN; CHEN CH; WU JL; CHANG CI;
Indirizzi:
CHUNG CHEN INST TECHNOL,DEPT ELECT ENGN TAYUAN 33509 TAIWAN NATL CHENG KUNG UNIV,DEPT ELECT ENGN TAINAN 70101 TAIWAN UNIV MARYLAND BALTIMORE CTY,DEPT COMP SCI & ELECT ENGN,REMOTE SENSINGSIGNAL & IMAGE PROC LAB BALTIMORE MD 21228
Titolo Testata:
IEEE transactions on geoscience and remote sensing
fascicolo: 1, volume: 36, anno: 1998,
pagine: 182 - 191
SICI:
0196-2892(1998)36:1<182:AF2CMF>2.0.ZU;2-O
Fonte:
ISI
Lingua:
ENG
Soggetto:
MAXIMUM-LIKELIHOOD CLASSIFICATION;
Keywords:
BAND SELECTION (BS); CANONICAL ANALYSIS (CA); PRINCIPAL COMPONENTS ANALYSIS (PCA); RECURSIVE ML CLASSIFIER (MLC); WINOGRADS IDENTITY;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Science Citation Index Expanded
Science Citation Index Expanded
Science Citation Index Expanded
Citazioni:
26
Recensione:
Indirizzi per estratti:
Citazione:
T.N. Tu et al., "A FAST 2-STAGE CLASSIFICATION METHOD FOR HIGH-DIMENSIONAL REMOTE-SENSING DATA", IEEE transactions on geoscience and remote sensing, 36(1), 1998, pp. 182-191

Abstract

Classification for high-dimensional remotely sensed data generally requires a large set of data samples and enormous processing time, particularly for hyperspectral image data, Hat this paper, we present a fast two-stage classification method composed of a band selection (BS) algorithm with feature extraction/selection (FSE) followed by a recursive maximum likelihood classifier (MLC). The first stage is to develop aBS algorithm coupled with FSE for data dimensionality reduction. The second siege is to design a fast recursive MLC (RMLC) so as to achievecomputational efficiency, The experimental results shelf that the proposed recursive MLC, in conjunction with BS and FSE, reduces computingtime significantly by a factor ranging from 30 to 145, as compared tothe conventional MLC.

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Documento generato il 22/10/20 alle ore 06:50:49