Share Email Print

Proceedings Paper

Hyperspectral and SAR imagery data fusion with positive Boolean function
Author(s): Yang-Lang Chang; Chia-Tang Chen; Chin-Chuan Han; Kuo-Chin Fan; K. S. Chen; Jeng-Horng Chang
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

High-dimensional spectral imageries obtained from multispectral, hyperspectral or even ultraspectral bands generally provide complementary characteristics and analyzable information. Synthesis of these data sets into a composite image containing such complementary attributes in accurate registration and congruence would provide truly connected information about land covers for the remote sensing community. In this paper, a novel feature selection algorithm applied to the greedy modular eigenspaces (GME) is proposed to explore a multi-class classification technique using data fused from data gathered by the MODIS/ASTER airborne simulator (MASTER) and the Airborne Synthetic Aperture Radar (AIRSAR) during the Pacrim II campaign. The proposed approach, based on a synergistic use of these fused data, represents an effective and flexible utility for land cover classifications in earth remote sensing. An optimal positive Boolean function (PBF) based multi-classifier is built by using the labeled samples of these data as the classifier parameters in a supervised training stage. It utilizes the positive and negative sample learning ability of minimum classification error criteria to improve the classification accuracy. It is proved that the proposed method improves the precision of image classification significantly.

Paper Details

Date Published: 23 September 2003
PDF: 12 pages
Proc. SPIE 5093, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX, (23 September 2003); doi: 10.1117/12.487468
Show Author Affiliations
Yang-Lang Chang, St. John's and St. Mary's Institute of Technology (Taiwan)
Chia-Tang Chen, National Central Univ. (Taiwan)
Chin-Chuan Han, Chung Hua Univ. (Taiwan)
Kuo-Chin Fan, National Central Univ. (Taiwan)
K. S. Chen, National Central Univ. (Taiwan)
Jeng-Horng Chang, Huafan Univ. (Taiwan)

Published in SPIE Proceedings Vol. 5093:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX
Sylvia S. Shen; Paul E. Lewis, 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?