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Proceedings Paper

An adaptive algorithm of relative radiometric normalization based on feature corner
Author(s): Xiaolian Deng; Changyao Wang; Mingguo Wei
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Paper Abstract

An adaptive algorithm of relative radiometric normalization for multi-temporal remote sensing images based on feature corner was introduced in this paper. The main purpose of this research was to explore an automatic and robust algorithm of relative radiometric normalization to minimize imaging differences of multi-temporal satellite images. The main idea was to construct statistical regression model of relative radiometric normalization by extracting steady ground point correspondences. The algorithm's detailed processes were as follows: First, a method of image matching was applied to recognize steady ground point correspondences of multi-temporal remote sensing images. Second, a statistical regression model of relative radiometric normalization was constructed to calibrate imaging difference of multi-temporal remote sensing images. By the experiments, we could see that the total RSME of reference image and corrected image was reduced apparently in comparison with the total RSME of reference image and uncalibrated image, and the hue of corrected image was looked more like that of reference image. We could conclude that this modified algorithm was more accurate and efficient than traditional algorithm, its adaptive characteristic made it easy to be integrated, and it had more feasibility and applicable value.

Paper Details

Date Published: 10 November 2007
PDF: 6 pages
Proc. SPIE 6795, Second International Conference on Space Information Technology, 67952D (10 November 2007); doi: 10.1117/12.774216
Show Author Affiliations
Xiaolian Deng, China Three Gorges Univ. (China)
Changyao Wang, Institute of Remote Sensing Applications (China)
Mingguo Wei, China Three Gorges Univ. (China)


Published in SPIE Proceedings Vol. 6795:
Second International Conference on Space Information Technology

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