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Automatic Cross-calibration of Multispectral Imagery with Airborne Hyperspectral Imagery Using Spectral Mixture Analysis
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 Title & Authors
Automatic Cross-calibration of Multispectral Imagery with Airborne Hyperspectral Imagery Using Spectral Mixture Analysis
Yeji, Kim; Jaewan, Choi; Anjin, Chang; Yongil, Kim;
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 Abstract
The analysis of remote sensing data depends on sensor specifications that provide accurate and consistent measurements. However, it is not easy to establish confidence and consistency in data that are analyzed by different sensors using various radiometric scales. For this reason, the cross-calibration method is used to calibrate remote sensing data with reference image data. In this study, we used an airborne hyperspectral image in order to calibrate a multispectral image. We presented an automatic cross-calibration method to calibrate a multispectral image using hyperspectral data and spectral mixture analysis. The spectral characteristics of the multispectral image were adjusted by linear regression analysis. Optimal endmember sets between two images were estimated by spectral mixture analysis for the linear regression analysis, and bands of hyperspectral image were aggregated based on the spectral response function of the two images. The results were evaluated by comparing the Root Mean Square Error (RMSE), the Spectral Angle Mapper (SAM), and average percentage differences. The results of this study showed that the proposed method corrected the spectral information in the multispectral data by using hyperspectral data, and its performance was similar to the manual cross-calibration. The proposed method demonstrated the possibility of automatic cross-calibration based on spectral mixture analysis.
 Keywords
Cross-calibration;Spectral Mixture Analysis;Spectral Unmixing;Hyperspectral Image;Multispectral Image;
 Language
English
 Cited by
 References
1.
Berry, M.W., Browne, M., Langville, A.N., Pauca, V.P., and Plemmons, R.J. (2007), Algorithms and applications for approximate nonnegative matrix factorization, Computational Statistics and Data Analysis, Vol. 52, No. 1, pp. 155-173. crossref(new window)

2.
BlackBridge (2012), Spectral response curves of the RapidEye sensor, BlackBridge, German, http://blackbridge.com/rapideye/upload/Spectral_Response_Curves.pdf (last date accessed: 8 June 2015).

3.
Brook, A. and Dor, E.B. (2011), Supervised vicarious calibration (SVC) of hyperspectral remote-sensing data, Remote Sensing of Environment, Vol. 115, No. 6, 1543-1555. crossref(new window)

4.
Chander, G., Mishra, N., Helder, D.L., Aaron, D.B., Angal, A., Choi, T., Xiong, X., and Doelling, D.R. (2013), Applications of spectral band adjustment factors (SBAF) for cross-calibration, IEEE Transactions on Geoscience and Remote Sensing, Vol. 51, No. 3, 1267-1281. crossref(new window)

5.
Chi, J. (2013), Validation of the radiometric characteristics of Landsat 8 (LDCM) OLI Sensor using band aggregation technique of EO-1 Hyperion hyperspectral imagery, Korean Journal of Remote Sensing, Vol. 29, No. 4, pp. 399-406. (in Korean with English abstract) crossref(new window)

6.
Franke, J., Roberts, D.A., Halligan, K., and Menz, G. (2009), Hierarchical multiple endmember spectral mixture analysis (MESMA) of hyperspectral imagery for urban environments, Remote Sensing of Environment, Vol. 113, No. 8, pp. 1712-1723. crossref(new window)

7.
Heinz, D.C. and Chang, C.I. (2001), Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery, IEEE Transactions on Geoscience and Remote Sensing, Vol. 39, No. 3, pp. 529-545. crossref(new window)

8.
Keshava, N. (2003), A survey of spectral unmixing algorithms. Lincoln Laboratory Journal, Vol. 14, No. 1, pp. 55-78.

9.
Lee, D.D. and Seung, H.S. (1999), Learning the parts of objects by non-negative matrix factorization, Nature, Vol. 401, No. 6755, pp. 788-791. crossref(new window)

10.
Nascimento, J.M.P. and Bioucas-Dias, J.M. (2005), Vertex component analysis: a fast algorithm to unmix hyperspectral data, IEEE Transactions on Geoscience and Remote Sensing, Vol. 43 No. 4, pp. 898-910. crossref(new window)

11.
Paatero, P. and Tapper, U. (1994), Positive matrix factorization: a non-negative factor model with optimal utilization of error estimates of data values, Environmetrics, Vol. 5, No. 2, pp. 111-126. crossref(new window)

12.
Raksuntorn, N. and Du, Q. (2010), Nonlinear spectral mixture analysis for hyperspectral imagery in an unknown environment, IEEE Geoscience and Remote Sensing Letters, Vol. 7, No. 4, pp. 836-840. crossref(new window)

13.
Röder, A., Kuemmerle, T., and Hill, J. (2005), Extension of retrospective datasets using multiple sensors. an approach to radiometric intercalibration of Landsat TM and MSS data, Remote Sensing of Environment, Vol. 95, No. 2, pp. 195-210. crossref(new window)

14.
Song, C. (2004), Cross-sensor calibration between Ikonos and Landsat ETM+ for spectral mixture analysis, IEEE Geoscience and Remote Sensing Letters, Vol. 1, No. 4, pp. 272-276. crossref(new window)

15.
Stathaki, T. (2008), Image Fusion: Algorithms and Applications 1st Edition, Academic Press, New York, N.Y.

16.
Teillet, P.M., Fedose evs, G., Thome, K.J., and Barker, J.L. (2007), Impacts of spectral band difference effects on radiometric cross-calibration between satellite sensors in the solar-reflective spectral domain, Remote Sensing of Environment, Vol. 110, No. 3, pp. 393-409 crossref(new window)

17.
Yokoya, N., Mayumi, N., and Iwasaki, A. (2013), Cross-calibration for data fusion of EO-1/Hyperion and Terra/ASTER, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 6, No. 2, pp. 419-426. crossref(new window)

18.
Yokoya, N., Yairi, T., and Iwasaki, A. (2012), Coupled nonnegative matrix factorization unmixing for hyperspectral and multispectral data fusion, IEEE Transactions on Geoscience and Remote Sensing, Vol. 50, No. 2, pp. 528-537. crossref(new window)