Spectral Content Characterization for Efficient Image Detection Algorithm Design
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Research Article Spectral Content Characterization for Efficient Image Detection Algorithm Design Kyoung-Su Park,1 Sangjin Hong,1 Peom Park,2, 3 and We-Duke Cho4 1 Mobile
Systems Design Laboratory, Department of Electrical and Computer Engineering, Stony Brook University – SUNY, Stony Brook, NY 11794-2350, USA 2 Department of Industrial and Information Systems Engineering, Ajou University, Suwon-Si 442-749, South Korea 3 Humintec Co. Ltd., Suwon-Si 443-749, South Korea 4 Department of Electronics Engineering, College of Information Technology, Ajou University, Suwon-Si 442-749, South Korea Received 8 August 2006; Revised 25 January 2007; Accepted 30 January 2007 Recommended by C.-C. Jay Kuo This paper presents spectral characterization for efficient image detection using hyperspectral processing techniques. We investigate the relationship between the number of used bands and the performance of the detection process in order to find the optimal number of band reductions. The band reduction significantly reduces computation and implementation complexity of the algorithms. Specifically, we define and characterize the contribution coefficient for each band. Based on the coefficients, we heuristically select the required minimum bands for the detection process. We have shown that the small number of bands is efficient for effective detection. The proposed algorithm is suitable for low-complexity and real-time applications. Copyright © 2007 Kyoung-Su Park et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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INTRODUCTION
The hyperspectral imaging systems have found various civilian and military applications. The high efficiency and flexibility of hyperspectral sensors provide a powerful measurement technology currently being demonstrated with modern airborne and spaceborne hyperspectral systems. The hyperspectral sensor typically gets one hundred to several hundreds of bands for exact spectral classification. The property of the hyperspectral sensor is similar to that of the sensor used in advanced digital cameras. The hyperspectral sensor is capable of covering infrared and/or ultraviolet radiation as well as visible light using the enormous number of bands; a typical digital camera sensor covers only visible light using three bands which are called RGB. The hyperspectral processing technology is gradually incorporated into modern civil and military remote sensing systems along with other sensors such as imaging radar and laser systems [1]. Hyperspectral processing requires an extremely large amount of input data for the spectral classification. Moreover, the computational requirement for processing input is significant. There are many approaches for analyzing hyperspectral data. Hardware clusters may be a feasible solution
because they are used to achieve high performance, high availability, or horizontal scaling. Cluster technology can also be used for hig
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