Joint block adjustment and variational optimization for global and local radiometric normalization toward multiple remote sensing image mosaicking

Multi-temporal optical remote sensing images acquired from cross-sensor platforms often show significant radiometric differences, posing challenges when mosaicking images. These challenges include inconsistent global radiometric tones, unsmooth local radiometric transitions, and visible seamlines. In this paper, to address these challenges, we propose a two-stage approach for global and local radiometric normalization (RN) using joint block adjustment and variational optimization. In the first stage, a block adjustment based global RN (BAGRN) model is established to simultaneously perform global RN on all the images, eliminating global radiometric differences and achieving overall radiometric tonal consistency. In the second stage, a variational optimization based local RN (VOLRN) model is introduced to address the remaining local radiometric differences after global RN. The VOLRN model applies local RN to all the image blocks within a unified energy function and imposes the $l_1$ norm constraint on the data fidelity term, providing the model with a more flexible local RN capability to radiometrically normalize the intersection and transition areas of the images. Therefore, the local radiometric discontinuities and edge artifacts can be eliminated, resulting in natural and smooth local radiometric transitions. The experimental results obtained on five challenging datasets of cross-sensor and multi-temporal remote sensing images demonstrate that the proposed approach excels in both visual quality and quantitative metrics. The proposed approach effectively eliminates global and local radiometric differences, preserves image gradients well, and has high processing efficiency. As a result, it outperforms the state-of-the-art RN approaches.