Automated Haze Removal and Radiometric Normalization for Electro-Optical Imagery Preprocessing

Abstract

Two precedures are presented for image preprocessing, automated haze removal and the relative radiometric normalization of multitemporal optical images, to act as the data support for land cover change detection and image analysis. The developed algorithm for haze removal involves processes of feature and texture analysis of the multiresolution spatial frequency distribution of pixel brightness information content of a scene. The image contaminated by haze is decomposed into layers of different resolutions of spatial distribution frequencies. The radiometric characteristics of the corresponding layers are estimated and analyzed with topology based multiresolution spatial analysis technology. Based on the analysis, the haze component is then separated from the remaining spatial frequency components representing spectral information of actual land cover types in the scene, and a spectrally corrected image with “haze-off” characteristics is obtained. Then a method is used for radiometric normalization between multitemporal images of the same area. Case study using several different type images of Qingdao City in China proves the effectiveness of this technique except for those regions too hazy.