Radiometric optimization of airborne hyperspectral imagery for large-scale geological applications

Recent developments in hyperspectral remote sensing have heightened the need for large-scale quantitative applications. As an important part of preprocessing, radiation correction and optimization are of great significance for subsequent quantitative analysis. The radiometry of hyperspectral images is influenced by many factors. For airborne hyperspectral data, the Bidirectional Reflectance Distribution Function (BRDF) has the greatest effect on radiation. This effect, which mainly depends on the sun-view geometry, will lead to an across-track illumination gradient in the image and seriously affect the radiometric consistencies of the regional image mosaics. This contribution describes an improved empirical method for radiometric optimization of multi-strip airborne hyperspectral images. Also, a cubic fitting equation and a statistical matching algorithm are used to generate the seamless image mosaics and remove the radiation inconsistency caused by the viewing and incident angle. As a case, The airborne hyperspectral images from the Lop Nor area of Xinjiang Province were processed and used for geological mapping. Results suggest that the method we proposed can effectively improve the efficiency and accuracy of regional geological mapping through radiometric optimization.