Color Calibration of Remote Sensing Imagery Based on Orthogonal Space Transformation

Abstract:

Due to different atmospheric conditions, seasonal changes in vegetation characteristics and other reasons, the remote sensing images captured at different time may be quite different in color, brightness and so on. In this paper, coupled with statistics classification, three common orthogonal space transformations were used to calibrate the color difference respectively. Compared with conventional methods such as the overlapping region correction and histogram matching, the results show that orthogonal transform could achieve better correction effects. The lαβ transform gets the best corrected result among three orthogonal space transform methods.

• Periodical: Applied Mechanics and Materials (Volumes 20-23)
• Pages: 1315-1322
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.20-23.1315
• Online since: January 2010
• Authors: Xu Chen, Ya Ping Zhang
• Keywords: Color Calibration, Orthogonal Space Transformation, Remote Sensing Image

References

[1] X. Chen, H. Ling, Z.P. Qiang: Color Calibration of Remote Sensing Imagery Based on Automatic Classification and Color Space Transformation, Journal of Remote Sensing, in press.

[2] M.J. Canty, A.A. Nielsen, M Schmidt: Automatic Radiometric Normalization of Multitemporal Satellite Imagery. Remote Sensing of Environment, VOL. 91(2004), pp.441-451.

DOI: 10.1016/j.rse.2003.10.024

[3] Y. Du, J. Cihlar, J. Beaubien, R. Latifovic: Radiometric Normalization, Compositing, and Quality Control for Satellite High Resolution Image Mosaics over Large Areas. IEEE Transactions on Geoscience and Remote Sensing, VOL. 39(2001), pp.623-634.

DOI: 10.1109/36.911119

[4] Y. Du, P.M. Teillet, J. Cihlar: Radiometric normalization of Multitemporal High-Resolution Satellite Images with Quality Control for Land Cover Change Detection. Remote Sensing of Environment, VOL. 82(2002), pp.123-134.

DOI: 10.1016/s0034-4257(02)00029-9

[5] S.L. Furby, N.A. Campbell: Calibrating Images from Different Dates to Like-Value, Digital Counts. Remote Sensing of Environment, VOL. 77(2001), pp.186-196.

DOI: 10.1016/s0034-4257(01)00205-x

[6] I. Olthof, C. Butson, R. Fraser: Signature Extension through Space for Northern Landcover Classification: A Comparison of Radiometric Correction Methods. Remote Sensing of Environment, VOL. 95(2005), pp.290-302.

DOI: 10.1016/j.rse.2004.12.015

[7] I. Olthof, D. Pouliot, R. Fernandes, R. Latifovic: Landsat-7 ETM+ Radiometric Normalization Comparison for Northern Mapping Applications. Remote Sensing of Environment, VOL. 95(2005), pp.388-398.

DOI: 10.1016/j.rse.2004.06.024

[8] X.X. Chen, L. Vierling, D. Deering: A Simple and Effective Radiometric Correction Method to Improve Landscape Change Detection across Sensors and across Time. Remote Sensing of Environment, VOL. 98(2005), pp.63-79.

DOI: 10.1016/j.rse.2005.05.021

[9] P.Q. Zhang, X.C. Yu, Z. Liu, J.S. Li, G. WAN: A Study on Relative Radiometric Correction of Multitemporal Remote Sensing Images. Journal of Remote Sensing, VOL. 10(2006), pp.339-344. in Chinese.

[10] J. Shlens: A Tutorial on Principal Component Analysis. (2005) http: /www. snl. salk. edu/~shlens/pub/notes/pca. pdf.

[11] K.N. Plataniontis, A.N. Venetsanopoulos: Color image processing and applications. Heidelberg: Springer. (2000).

[12] D.L. Ruderman, T.W. Cronin, C.C. Chiao: Statistics of Cone Responses to Natural Images: Implications for Visual Coding. Optical Society of America, VOL. 15(1998), p.2036-2045v.

DOI: 10.1364/josaa.15.002036

[13] A. Hertzmann, C.E. Jacobs, N. Oliver, B. Curless, and D.H. Salesin: Image Analogies. ACM SIGGRAPH, (2001), pp.327-340.

DOI: 10.1145/383259.383295