Dual-decoupling inter-correction multitemporal framework for high-, medium-, and low-resolution optical remote sensing image reconstruction

Abstract

Reconstructing missing information due to cloud occlusion is an effective means of enhancing the utilization of low-, medium-, and high-resolution optical remote sensing images. However, singletemporal-based methods have limitations regarding the demand for cloud-free reference data and the applicability of specific datadriven models to real-world scenarios. It is more unable to realize mutitemporal reconstruction. To address this, we propose the Dual-Decoupling Inter-correction Multitemporal Reconstruction network (DDIM-RecNet), a unified framework designed for single- and multitemporal cloud occlusion reconstruction of low-, medium-, and high-resolution images. DDIM-RecNet innovatively decouples remote sensing images into ground object and imaging environment components using dedicated inter-correction modules, coupled with targeted loss functions. Additionally, an imaging environment enhancement module ensures spatial consistency between reconstructed and original regions. Compared with classical models, such as U-Net, RFR-Net, STGAN, PSTCR, BSN, GLDF-RecNet, and IDF-CR, DDIM-RecNet achieved excellent visual reconstruction results and the best quantitative evaluation indicators under Gaofen-1 (2 m), Sentinel-2 (10 m), Landsat-8 (30 m) single/multitemporal images. Taking Gaofen-1 (2 m) as an example, compared with the suboptimal model, the clarity of the DDIM-RecNet model in the three bands was improved by 0.44, 0.70, and 0.85 respectively under singletemporal reconstruction; the clarity of DDIM-RecNet was improved by 0.55, 0.43, and 0.35 respectively under mutitemporal cloud occlusion.