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Proceedings Paper

Remote sensing image classification based on sparse component analysis
Author(s): Tingting Cao; Xianchuan Yu; Chunping Yang
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Paper Abstract

The classification of remote sensing images is a key issue and hot topic in remote sensing image processing domain. Considering that the classification result of classical principle component analysis (PCA) is not satisfying when the spectra of different ground objects are related, a new classification method based on sparse component analysis (SCA) is presented. The proposed method utilizes the sparse characteristic to extract the source signals, and does not demand the sources be independent. The experimental result of TM image shows that compared to the PCA method the overall classification precision of the SCA method enhances approximately 15%, which indicates that the classification result of the SCA method is more reliable and more accurate.

Paper Details

Date Published: 30 October 2009
PDF: 10 pages
Proc. SPIE 7494, MIPPR 2009: Multispectral Image Acquisition and Processing, 74942O (30 October 2009); doi: 10.1117/12.833529
Show Author Affiliations
Tingting Cao, Beijing Normal Univ. (China)
Xianchuan Yu, Beijing Normal Univ. (China)
Chunping Yang, Beijing Normal Univ. (China)

Published in SPIE Proceedings Vol. 7494:
MIPPR 2009: Multispectral Image Acquisition and Processing
Faxiong Zhang; Faxiong Zhang, Editor(s)

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