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Proceedings Paper

Simulated annealing and greedy modular eigenspaces band selection approach for hyperspectral imagery
Author(s): Yang-Lang Chang; Jyh-Perng Fang; Hsuan Ren; Wen-Yew Liang; Wei-Lieh Hsu; Jen-Chieh Yang
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Paper Abstract

For hyperspectral imagery, simulated annealing (SA) and greedy modular eigenspaces (GME) have been successfully developed to cluster highly correlated hyperspectral bands into a smaller subset of band modules. This paper introduces a novel band selection technique of combining these two approaches, called the SA and GME band selection (SGBS), for hyperspectral imagery. The SGBS selects sets of non-correlated hyperspectral bands for hyperspectral images based on heuristic and greedy algorithms, utilizes the inherent separability of different classes in hyperspectral images to reduce dimensionality, and further generates a unique clustered eigenspace (CE) feature set effectively. The proposed SGBS features can 1) avoid the bias problems of transforming the information into linear combinations of bands as does the traditional principal components analysis, 2) evince improved discriminatory properties, crucial to subsequent classification compared with conventional band selection techniques, 3) provide a fast procedure to simultaneously select the most significant features by merging SA and GME schemes, and 4) select each band by a simple logical operation, called the CE feature scale uniformity transformation (CE/FSUT), to include different classes into the most common feature modules of the hyperspectral bands. The performance of the proposed SGBS method was evaluated by MODIS/ASTER airborne simulator (MASTER) images for land cover classification during the Pacific Rim II campaign. Encouraging experimental results showed that the proposed SGBS approach is effective and can be used as an alternative to the existing band selection algorithms.

Paper Details

Date Published: 25 September 2007
PDF: 10 pages
Proc. SPIE 6756, Chemical and Biological Sensors for Industrial and Environmental Monitoring III, 67560C (25 September 2007); doi: 10.1117/12.739228
Show Author Affiliations
Yang-Lang Chang, National Taipei Univ. of Technology (Taiwan)
Jyh-Perng Fang, National Taipei Univ. of Technology (Taiwan)
Hsuan Ren, National Central Univ. (Taiwan)
Wen-Yew Liang, National Taipei Univ. of Technology (Taiwan)
Wei-Lieh Hsu, Lunghwa Univ. of Science and Technology (Taiwan)
Jen-Chieh Yang, National Taipei Univ. of Technology (Taiwan)


Published in SPIE Proceedings Vol. 6756:
Chemical and Biological Sensors for Industrial and Environmental Monitoring III
Kenneth J. Ewing; James B. Gillespie; Pamela M. Chu; William J. Marinelli, Editor(s)

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