Share Email Print

Proceedings Paper

Spectral features recognition based on data mining algorithms
Author(s): Peijun Du; Hongjun Su; Wei Zhang
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

In order to discover those significant spectral features that are of effectiveness to target identification, some Data Mining algorithms were used to the data sets from USGS spectral library and OMIS hyperspectral remote sensing image. The candidate feature sets were generated by traditional spectral feature extraction approaches at first, and then clustering, statistical analysis and decision tree were used to characterized feature recognition and target identification model design. Derivative spectrum has the superiority of enhancing the characteristic spectral features in contrast with other algorithms. The recognition decision tree based on the knowledge and rules can identify and discriminate targets using the discovered spectral features. The experiment showed that the proposed characterized spectral features recognition approach based on Data Mining algorithm was suitable to hyperspectral remote sensing information processing.

Paper Details

Date Published: 8 August 2007
PDF: 11 pages
Proc. SPIE 6752, Geoinformatics 2007: Remotely Sensed Data and Information, 675204 (8 August 2007); doi: 10.1117/12.760106
Show Author Affiliations
Peijun Du, China Univ. of Mining and Technology (China)
Hongjun Su, Nanjing Normal Univ. (China)
Wei Zhang, China Univ. of Mining and Technology (China)

Published in SPIE Proceedings Vol. 6752:
Geoinformatics 2007: Remotely Sensed Data and Information
Weimin Ju; Shuhe Zhao, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?