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.
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