In remote sensing single-image dehazing (RSSID), adjacency effects and the multi-scale characteristics of the land surface–atmosphere system highlight the importance of a network’s effective receptive field (ERF) and its ability to capture multi-scale features. Although multi-scale hybrid models combining convolutional neural networks and Transformers show promise, the quadratic complexity of Transformer complicates the balance between ERF and efficiency. Recently, Mamba achieved global ERF with linear complexity and excelled in modeling long-range dependencies, yet its design for sequential data and channel redundancy limits its direct applicability to RSSID. To overcome these challenges and improve performance in RSSID, we present a novel Mamba-based dehazing network, U-shaped Dual Attention Vision Mamba Network (UDAVM-Net) for Satellite RSSID, which integrates multi-path scanning and incorporates dual attention mechanisms to better capture non-uniform haze features while reducing redundancy. The core module, Residual Vision Mamba Blocks (RVMBs), are stacked within a U-Net architecture to enhance multi-scale feature learning. Furthermore, to enhance the model’s applicability to real-world remote sensing data, we abandoned overly simplified haze image degradation models commonly used in existing works, instead adopting an atmospheric radiative transfer model combined with a cloud distortion model to construct a submeter-resolution satellite RSSID dataset. Experimental results demonstrate that UDAVM-Net consistently outperforms competing methods on the StateHaze1K dataset, our newly proposed dataset, and real-world remote sensing images, underscoring its effectiveness in diverse scenarios.

Remote sensing image fusion technology integrates observational data from multiple satellite platforms to leverage the complementary advantages of the different types of remote sensing images. High-quality fused remote sensing images provide detailed information on surface radiation, climate, and environmental conditions, thereby supporting governmental policies on environmental changes. Improving the quality and quantitative accuracy of fused images is a crucial trend in remote sensing image fusion research. This study investigates the impact of atmospheric correction and five widely applied fusion techniques on remote sensing image fusion. By constructing four fusion frameworks, it evaluates how the choice of fusion method, the implementation of atmospheric correction, the synchronization of atmospheric parameters, and the timing of atmospheric correction influence the outcomes of remote sensing image fusion. Aerial flights using remote sensors were conducted to acquire atmospheric parameter distribution images that are strictly synchronous with the remote sensing images. Comprehensive and systematic evaluations of the fused remote sensing images were performed. Experiments show that for the remote sensing images used, selecting the appropriate fusion method can improve the spatial detail evaluation metrics of the fused images by up to 2.739 times, with the smallest deviation from true reflectance reaching 35.02%. Incorporating synchronous atmospheric parameter distribution images can enhance the spatial detail evaluation metrics by up to 2.03 times, with the smallest deviation from true reflectance reaching 5.4%. This indicates that choosing an appropriate fusion method and performing imaging-based synchronous atmospheric correction before fusion can maximize the enhancement of spatial details and spectral quantification in fused images.

Atmospheric correction of high-spatial-resolution (HSR) optical satellite imagery is strongly affected by the adjacency effect (AE). Conventional Atmospheric Point Spread Function (APSF)-based AE correction methods are often based on simple local averaging or distance-weighted background approximations. These are often insufficient for highly heterogeneous HSR scenes and become computationally expensive when the AE’s influence range spans far more pixels. To address these issues, this study proposes a fast AE correction algorithm with adaptive local surface constraints. The method first introduces a surface-atmosphere coupling correction based on effective reflectance. It then constructs downward- and upward-weighting kernels and incorporates local reflectance constraints into the estimation of environmental reflectance to better characterize AE intensity in HSR scenes. Since environmental reflectance estimation is retained in a kernel-weighted form, the infinite-domain integration is reformulated as a finite-window computation with truncation compensation and accelerated via fast Fourier transform (FFT) convolution, followed by a few iterations for reflectance retrieval. Validation with GaoFen-2 (GF-2) panchromatic imagery shows that, at an aerosol optical depth of about 0.4, the proposed method achieves the best performance among the compared methods, with a mean absolute error (MAE) below 0.006 relative to in situ measurements, sharpness and contrast increases of approximately $99.0\%$ and $97.9\%$, respectively, and a National Imagery Interpretability Rating Scale (NIIRS) increase of more than 0.3. For a $1024\times 1024$ image with a 501-pixel AE window diameter, the running time is below 4 s, substantially lower than that of previous APSF-based AE correction methods. The FFT implementation also avoids the quadratic dependence on window size in direct spatial convolution. Additional experiments on multiple GF-2 and Gao Fen Duo Mo scenes show that the proposed method provides stable AE correction and achieves higher image quality and visual interpretability than the compared methods in HSR imagery.