Special Section on Satellite Data Compression

Simulated annealing band selection approach for hyperspectral imagery

[+] Author Affiliations
Yang-Lang Chang, Jyh-Perng Fang

National Taipei University of Technology, Department of Electrical Engineering, No. 1, Sec. 3, Chung-Hsiao E. Road, Taipei, 10608 Taiwan

Wei-Lieh Hsu

Lunghwa University of Science and Technology, Department of Computer Information and Network Engineering, No. 300,Sec. 1, Wanshou Road, Guishan, Taoyuan County, 33306 Taiwan

Lena Chang

National Taiwan Ocean University, Department of Communications and Guidance Engineering, No. 2 Pei-Ning Road, Keelung, 20224 Taiwan

Wen-Yen Chang

National Science Council, Department of Natural Sciences, No. 106, Sec. 2, Heping E. Road, Taipei, Taiwan 10622

J. Appl. Remote Sens. 4(1), 041767 (September 27, 2010). doi:10.1117/1.3502611
History: Received December 16, 2009; Revised August 24, 2010; Accepted September 17, 2010; September 27, 2010; Online September 27, 2010
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In hyperspectral imagery, greedy modular eigenspace (GME) was developed by clustering highly correlated bands into a smaller subset based on the greedy algorithm. Unfortunately, GME is hard to find the optimal set by greedy scheme except by exhaustive iteration. The long execution time has been the major drawback in practice. Accordingly, finding the optimal (or near-optimal) solution is very expensive. Instead of adopting the band-subset-selection paradigm underlying this approach, we introduce a simulated annealing band selection (SABS) approach, which takes sets of non-correlated bands for high-dimensional remote sensing images based on a heuristic optimization algorithm, to overcome this disadvantage. It utilizes the inherent separability of different classes embedded in high-dimensional data sets to reduce dimensionality and formulate the optimal or near-optimal GME feature. Our proposed SABS scheme has a number of merits. Unlike traditional principal component analysis, it avoids the bias problems that arise from transforming the information into linear combinations of bands. SABS can not only speed up the procedure to simultaneously select the most significant features according to the simulated annealing optimization scheme to find GME sets, but also further extend the convergence abilities in the solution space based on simulated annealing method to reach the global optimal or near-optimal solution and escape from local minima. The effectiveness of the proposed SABS is evaluated by NASA MODIS/ASTER (MASTER) airborne simulator data sets and airborne synthetic aperture radar images for land cover classification during the Pacrim II campaign. The performance of our proposed SABS is validated by supervised k-nearest neighbor classifier. The experimental results show that SABS is an effective technique of band subset selection and can be used as an alternative to the existing dimensionality reduction method.

© 2010 Society of Photo-Optical Instrumentation Engineers


Yang-Lang Chang ; Jyh-Perng Fang ; Wei-Lieh Hsu ; Lena Chang and Wen-Yen Chang
"Simulated annealing band selection approach for hyperspectral imagery", J. Appl. Remote Sens. 4(1), 041767 (September 27, 2010). ; http://dx.doi.org/10.1117/1.3502611



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