Relative Radiometric Normalization for Multitemporal Remote Sensing Images by Hierarchical Regression

The existing relative radiometric normalization methods are insufficient to define the invariant pixels automatically, and the conventional methods do not perform well when the multitemporal images contain a lot of changes. Two types of changes should be particularly considered: one is caused by significant spectral differences due to change of ground objects, and the other is the pixels in the regions of misalignment caused by displacement due to differences in acquisition view angles and geometrical distortions. To automatically extract invariant pixels and reduce the influence of the changes, a hierarchical regression method is proposed to reduce the radiation difference for multitemporal images, which consists of extraction of the pseudo-invariant features (PIFs) and optimization of normalization parameters. A weighted regression based on spectral difference is proposed to automatically extract the PIFs, which can also suppress the negative effect of the first type of changes. In addition, a robust regression with gradient dependence is performed on the extracted PIFs to build the final relationship between the target image and the reference image, which can be robust for the second type of changes. Experimental results demonstrate that the proposed method has a better performance to normalize the target image.