GCCG-RSI: Ground LiDAR and Image-Guided Geometry-Constrained Controllable Generation for Remote Sensing Image

Remote sensing image analysis is crucial for many research fields, yet acquiring frequent high-quality remote sensing imagery is not always feasible due to prohibitive costs and logistical efforts. As a solution, ground-to-satellite cross-view image generation has emerged as a promising approach for synthesizing remote sensing images from readily available ground sensor data. However, existing methods face two critical limitations that bottleneck their performance, including instability in object structural attributes in ground views and reduced image fidelity and consistency due to environmental occlusions. To address these challenges, this paper proposes a geometrically constrained controllable generation model specifically tailored for remote sensing image generation, called GCCG-RSI. To overcome the limitation of structural instability, GCCG-RSI introduces LiDAR ranging accuracy to constrain the geometric shapes of the generated image. To mitigate occlusion-induced fidelity issues, GCCG-RSI employs an attention mechanism to derive a unified fused representation that integrates texture and spatial structure information. The representation is utilized as a conditional control signal to guide the diffusion model in accurately synthesizing remote sensing imagery. Experimental results demonstrate that, compared with state-of-the-art methods, GCCG-RSI infers remote sensing images with superior realism and fidelity using ground-view images and point clouds with limited perspective. Overall, the proposed method provides an effective image preprocessing approach that contributes to significantly narrowing the domain discrepancy between ground and satellite images, thereby facilitating the execution of downstream tasks.