Radiometric normalization of multitemporal satellite images using weighted principal component analysis

The radiometric normalization of multitemporal satellite is a fundamental and important process for land cover change detection. In previous studies, ground reference data or pseudo-invariant features (PIFs) were used in the radiometric normalization. However, ground reference data are difficult to acquire and the selection of PIFs is generally sensitive to cloud covers. In this paper, an approach based on weighted principal component analysis is proposed for PIFs selection, which can withstand the disturbance of cloud covers. In the experiments, qualitative analyses of image sequences acquired by Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor and quantitative analyses of image sequences with various cloud contamination conditions and landscapes are conducted to evaluate the proposed method. The experimental results show that the proposed radiometric normalization has the ability to deal with images that contain various clouds.