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Relative Radiometric Normalization of Hyperion Hyperspectral Images Through Automatic Extraction of Pseudo-Invariant Features for Change Detection  

Kim, Dae-Sung (서울대학교 건설환경공학부)
Kim, Yong-Il (서울대학교 건설환경공학부)
Publication Information
Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography / v.26, no.2, 2008 , pp. 129-137 More about this Journal
Abstract
This study focuses on the radiometric normalization, which is one of the pre-processing steps to apply the change detection technique fur hyperspectral images. The PIFs which had radiometric consistency under the time interval were automatically extracted by applying spectral angle, and used as sample pixels for linear regression of the radiometric normalization. We also dealt with the problem about the number of PIFs for linear regression with iteratively quantitative methods. The results were assessed in comparison with image regression, histogram matching, and FLAASH. In conclusion, we show that linear regression method with PIFs can carry out the efficient result for radiometric normalization.
Keywords
Change Detection; Radiometric Normalization; Hyperion Hyperspectral Image; Pseudo-Invariant Features(PIF); Linear Regression;
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Times Cited By KSCI : 2  (Citation Analysis)
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