Browse > Article
http://dx.doi.org/10.7780/kjrs.2018.34.4.7

Vicarious Calibration-based Robust Spectrum Measurement for Spectral Libraries Using a Hyperspectral Imaging System  

Chi, Junhwa (Unit of Arctic Sea-Ice Prediction, Korea Polar Research Institute)
Publication Information
Korean Journal of Remote Sensing / v.34, no.4, 2018 , pp. 649-659 More about this Journal
Abstract
The aim of this study is to develop a protocol for obtaining spectral signals that are robust to varying lighting conditions, which are often found in the Polar regions, for creating a spectral library specific to those regions. Because hyperspectral image (HSI)-derived spectra are collected on the same scale as images, they can be directly associated with image data. However, it is challenging to find precise and robust spectra that can be used for a spectral library from images taken under different lighting conditions. Hence, this study proposes a new radiometric calibration protocol that incorporates radiometric targets with a traditional vicarious calibration approach to solve issues in image-based spectrum measurements. HSIs obtained by the proposed method under different illumination levels are visually uniform and do not include any artifacts such as stripes or random noise. The extracted spectra capture spectral characteristics such as reflectance curve shapes and absorption features better than those that have not been calibrated. The results are also validated quantitatively. The calibrated spectra are shown to be very robust to varying lighting conditions and hence are suitable for a spectral library specific to the Polar regions.
Keywords
Hyperspectral imaging; Illumination robustness; Imaging spectroscopy; Spectral library; Vicarious calibration;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Chi, J. and M. M. Crawford, 2014. Spectral unmixing based crop residue estimation using hyperspectral remote sensing data: A case study at Purdue University, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(6): 2531-2539.   DOI
2 Clark,R. N., 1993. Spectroscopy of rocks andminerals, and principles of spectroscopy, Manual of Remote Sensing, 3: 3-58.
3 Clark, R. N., G. A. Swayze, A. J. Gallagher, T.V.V. King, and W. M. Calvin, 1993. The US Geological Survey, Digital Spectral Library: Version 1 (0.2 to 3.0 um), Geological Survey, US.
4 Dinguirard, M. and P. N. Slater, 1999. Calibration of space-multispectral imaging sensors: A review, Remote Sensing of Environment, 68(3): 194-205.   DOI
5 Adams, J. B., M. O. Smith, and P. E. Johnson, 1986. Spectral mixture modeling-Anew analysis of rock and soil types at the Viking Lander-1 Site, Journal of Geophysical Research: Solid Earth, 91(B8): 8098-8112.   DOI
6 Baldridge, M., S. J. Hook, C. I. Grove, and G. Rivera, 2009. The ASTER spectral library version 2.0, Remote Sensing of Environment,113(4):711-715.   DOI
7 Goswami, S. and K. Matharasi, 2015. Development of a web-based vegetation spectral library (VSL) for remote sensing research and applications, Peer J PrePrints, 3: e915v1.
8 Green, R. O., B. E. Pavri, and T. G. Chrien, 2003. Onorbit radiometric and spectral calibration characteristics of EO-1 Hyperion derived with an underflight of AVIRIS and in situ measurements at Salar de Arizaro, Argentina, IEEE Transactions on Geoscience and Remote Sensing, 41(6): 1194-1203.   DOI
9 Habib, A., W. Xiong, F. He, H. L. Yang, and M. M. Crawford, 2017. Improving orthorectification of UAV-Based push-broom scanner imagery using derived orthophotosfrom frame cameras, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(1): 262-276.   DOI
10 Habib, A., Y. Han, W. Xiong, F. He, Z. Zhang, and M. M. Crawford, 2016. Automated orthorectification of UAV- based hyperspectral data over an agricultural field using frame RGB imagery, Remote Sensing, 8(10): 796.   DOI
11 Hruska, R.,J. Mitchell, M.Anderson, and N. F. Glenn, 2012. Radiometric and geometric analysis of hyperspectral imagery acquired from an unmanned aerial vehicle, Remote Sensing, 4(9): 2736-2752.   DOI
12 Karpouzli, E. and T. Malthus, 2003.The empirical line method for the atmospheric correction of IKONOS imagery, International Journal of Remote Sensing, 24(5): 1143-1150.   DOI
13 Keshava, N. and J. F. Mustard, 2002. Spectral unmixing, IEEE Signal Processing Magazine, 19(1): 44-57.   DOI
14 Keshava, N., 2004. Distance metrics and band selection in hyperspectral processing with applications to material identification and spectral libraries, IEEE Transactions on Geoscience and Remote Sensing, 42(7): 1552-1565.   DOI
15 Lewis, J. P., 1995. Fast template matching, Proc. of Vision Interface 95, Quebec City, Canada, May 15-19, pp.120-123.
16 Segl, K., L. Guanter, H. Kaufmann, J. Schubert, S. Kaiser,B. Sang, and S. Hofer, 2010. Simulation of spatial sensor characteristics in the context of the EnMAP Hyperspectral Mission, IEEE Transactions on Geoscience and Remote Sensing, 48(7): 3046-3054.   DOI
17 Slater, P. N., S. F. Biggar, K. J. Thome, D. I. Gellman, and P. R. Spyak, 1996. Vicarious radiometric calibrations of EOS sensors, Journal of Atmospheric and Oceanic Technology, 13(2): 349-359.   DOI
18 Smith, G. M. and E. J. Milton, 1999. The use of the empirical line method to calibrate remotely sensed data to reflectance, International Journal of Remote Sensing, 20(13): 2653-2662.   DOI
19 Solomon,J. and B. Rock, 1985. Imaging spectrometry for earth remote sensing, Science, 228(4704): 1147-1153.   DOI
20 Thorne, K., B. Markharn, and P. S. Barker, 1997. Radiometric calibration of Landsat, Photogrammetric Engineering & Remote Sensing, 63(7): 853-858.
21 Martin, M. E. and J. D. Aber, 1997. High spectral resolution remote sensing of forest canopy lignin, nitrogen, and ecosystem processes, Ecological Applications, 7(2): 431-443..   DOI