8 December 2004 Enhancements of target detection using atmospheric correction preprocessing techniques in hyperspectral remote sensing
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Abstract
This paper reports the result of a study on how atmospheric correction techniques (ACT) enhance target detection in hyperspectral remote sensing, using different sets of real data. Based on the data employed in this study, it has been shown that ACT can reduce the masking effect of the atmosphere and effectively improving spectral contrast. By using the standard Kmeans cluster based unsupervised classifier, it has been shown that the accuracy of the classification obtained from the atmospheric corrected data is almost an order of magnitude better than that achieved using the radiance data. This enhancement is entirely due to the improved separability of the classes in the atmospherically corrected data. Moreover, it has been found that intrinsic information concerning the nature of the imaged surface can be retrieved from the atmospherically corrected data. This has been done to within an error of 5% by using a model based atmospheric correction package ATCOR.
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Peter W. T. Yuen, Peter W. T. Yuen, Gary J. Bishop, Gary J. Bishop, "Enhancements of target detection using atmospheric correction preprocessing techniques in hyperspectral remote sensing", Proc. SPIE 5613, Military Remote Sensing, (8 December 2004); doi: 10.1117/12.578688; https://doi.org/10.1117/12.578688
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