Simulated annealing and greedy modular eigenspaces band selection approach for hyperspectral imagery

Yang-Lang Chang; Jyh-Perng Fang; Hsuan Ren; Wen-Yew Liang; Wei-Lieh Hsu; Jen-Chieh Yang

doi:10.1117/12.739228

25 September 2007 Simulated annealing and greedy modular eigenspaces band selection approach for hyperspectral imagery

Yang-Lang Chang, Jyh-Perng Fang, Hsuan Ren, Wen-Yew Liang, Wei-Lieh Hsu, Jen-Chieh Yang

Proceedings Volume 6756, Chemical and Biological Sensors for Industrial and Environmental Monitoring III; 67560C (2007) https://doi.org/10.1117/12.739228
Event: Optics East, 2007, Boston, MA, United States

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.

Citation Download Citation

Yang-Lang Chang, Jyh-Perng Fang, Hsuan Ren, Wen-Yew Liang, Wei-Lieh Hsu, and Jen-Chieh Yang "Simulated annealing and greedy modular eigenspaces band selection approach for hyperspectral imagery", Proc. SPIE 6756, Chemical and Biological Sensors for Industrial and Environmental Monitoring III, 67560C (25 September 2007); https://doi.org/10.1117/12.739228

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
10 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Hyperspectral imaging

Feature extraction

Algorithm development

Algorithms

Distance measurement

Annealing

Principal component analysis

Show All Keywords

Keywords/Phrases

Search In:

Publication Years