Browse > Article

Optical System Design and Image Processing for Hyperspectral Imaging Systems  

Heo, A-Young (Department of Electrical Engineering, KAIST)
Choi, Seung-Won (Department of Electrical Engineering, KAIST)
Lee, Jae-Hoon (Department of Electrical Engineering, KAIST)
Kim, Tae-Hyeong (Department of Physics, KAIST)
Park, Dong-Jo (Department of Electrical Engineering, KAIST)
Publication Information
Journal of the Korea Institute of Military Science and Technology / v.13, no.2, 2010 , pp. 328-335 More about this Journal
Abstract
A hyperspectral imaging spectrometer has shown significant advantages in performance over other existing ones for remote sensing applications. It can collect hundreds of narrow, adjacent spectral bands for each image, which provides a wealth of information on unique spectral characteristics of objects. We have developed a compact hyperspectral imaging system that successively shows high spatial and spectral resolutions and fast data processing performance. In this paper, we present an overview of the hyperspectral imaging system including the strucure of geometrical optics and several image processing schemes such as wavelength calibration and noise reduction for image data on Visible and Near-Infrared(VNIR) and Shortwave-Infrared(SWIR) band.
Keywords
Spectrometer; Optical Design; Hyperspectral Imaging Systems; VNIR & SWIR Images;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Z. Pan. G. Healay, M. Prased, and B. Tromberg, "Face Recognition in Hyperspectral Images", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25, No. 12, pp. 1552-1560, 2003.   DOI   ScienceOn
2 G. Shaw and H. K. Burke, "Spectral Imaging for Remote Sensing", Lincoln Laboratory Journal, Vol. 14, No. 1, pp. 3-28, 2003.
3 허아영, 최승원, 이재훈 박동조, "초분광 분해기파장 미세 보정 및 영상의 응용", 한국군사과학기술학회 종합학술대회, pp. 1047-1050, 2009. 8.
4 M. R. Descour and E. L. Dereniak, "Nonscanning No-moving-parts Imaging Spectrometer", Proceedings of SPIE, Vol. 2480, pp. 48-64, 1995.
5 G. Healey and D. Slater, "Models and Methods for Automated Material Identification in Hyperspectral Imagery Acquired Under Unknown Illumination and Atmospheric Conditions", IEEE Transactions on Geoscience and Remote Sensing, Vol. 37, No. 6, pp. 2706-2717, 1999.   DOI   ScienceOn
6 D. Manolakis and G. Shaw, "Detection Algorithms for Hyperspectral Imaging Applications", IEEE Signal Processing Magazine, Vol. 19, No. 1, pp. 29-43, 2002.   DOI   ScienceOn
7 A. A. Green, M. Berman, P. Switzer, and M. D. Craig, "A Transformation for Ordering Multispectral Data in Term of Image Quality with Implications for Noise Removal", IEEE Transactions on Geoscience and Remote Sensing, Vol. 26, No. 1, pp. 65-74, 1988.   DOI   ScienceOn
8 E. M. Edwin, "Mitigating the Effects of Bad and Noisy Detectors on Hyperspectral Data", IEEE International Geoscience and Romote Sensing Symposium, Vol. 1, pp. 80-82, 2003.
9 김준태, 허아영, 박동조, "초분광 영상의 노이즈 제거 및 영상 처리 기법 연구", 제20회 영상 처리 및 이해에 관한 워크샵, 2008.
10 C. Cordon, "A Generalization of the Maximum Noise Fraction Transform", IEEE Transactions on Geoscience and Remote Sensing, Vol. 38, No. 1, pp. 608-610, 2000.   DOI   ScienceOn