Special Section on High-Performance Computing in Applied Remote Sensing: Part 3

Hyperspectral band selection based on parallel particle swarm optimization and impurity function band prioritization schemes

[+] Author Affiliations
Yang-Lang Chang

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

Jin-Nan Liu

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

Yen-Lin Chen

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

Wen-Yen Chang

National Dong Hwa University, Department of Natural Resources and Environmental Studies, No. 1, Sec. 2, Da Hsueh Road, Shoufeng, Hualien, Taiwan 97401

Tung-Ju Hsieh

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

Bormin Huang

University of Wisconsin-Madison, Cooperative Institute for Meteorological Satellite, Studies Space Science and Engineering Center, Madison, Wisconsin 53706

J. Appl. Remote Sens. 8(1), 084798 (Aug 13, 2014). doi:10.1117/1.JRS.8.084798
History: Received May 6, 2014; Revised May 20, 2014; Accepted May 22, 2014
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Abstract.  In recent years, satellite imaging technologies have resulted in an increased number of bands acquired by hyperspectral sensors, greatly advancing the field of remote sensing. Accordingly, owing to the increasing number of bands, band selection in hyperspectral imagery for dimension reduction is important. This paper presents a framework for band selection in hyperspectral imagery that uses two techniques, referred to as particle swarm optimization (PSO) band selection and the impurity function band prioritization (IFBP) method. With the PSO band selection algorithm, highly correlated bands of hyperspectral imagery can first be grouped into modules to coarsely reduce high-dimensional datasets. Then, these highly correlated band modules are analyzed with the IFBP method to finely select the most important feature bands from the hyperspectral imagery dataset. However, PSO band selection is a time-consuming procedure when the number of hyperspectral bands is very large. Hence, this paper proposes a parallel computing version of PSO, namely parallel PSO (PPSO), using a modern graphics processing unit (GPU) architecture with NVIDIA’s compute unified device architecture technology to improve the computational speed of PSO processes. The natural parallelism of the proposed PPSO lies in the fact that each particle can be regarded as an independent agent. Parallel computation benefits the algorithm by providing each agent with a parallel processor. The intrinsic parallel characteristics embedded in PPSO are, therefore, suitable for parallel computation. The effectiveness of the proposed PPSO is evaluated through the use of airborne visible/infrared imaging spectrometer hyperspectral images. The performance of PPSO is validated using the supervised K-nearest neighbor classifier. The experimental results demonstrate that the proposed PPSO/IFBP band selection method can not only improve computational speed, but also offer a satisfactory classification performance.

© 2014 Society of Photo-Optical Instrumentation Engineers

Citation

Yang-Lang Chang ; Jin-Nan Liu ; Yen-Lin Chen ; Wen-Yen Chang ; Tung-Ju Hsieh, et al.
"Hyperspectral band selection based on parallel particle swarm optimization and impurity function band prioritization schemes", J. Appl. Remote Sens. 8(1), 084798 (Aug 13, 2014). ; http://dx.doi.org/10.1117/1.JRS.8.084798


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