A Fast Adjacency Effect Correction Algorithm for High-Spatial-Resolution Optical Satellite Imagery with Adaptive Local Surface Constraints

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 1024×1024 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.