Plan, Decouple, Assimilate: Physics-Aware Object Insertion in Remote Sensing Imagery

Object insertion has emerged as a promising augmentation paradigm for the label scarcity and long-tailed distributions in remote sensing, generating training samples by synthesizing target instances onto real backgrounds. However, existing methods suffer from three critical issues: (i) semantic placement inconsistency, (ii) radiometric inconsistency with illumination and atmospheric conditions, and (iii) textural discontinuity. To address these, we propose a physics-aware method, "Plan, Decouple, Assimilate" (PDA), for generating high-fidelity training samples. In the planning stage, the Planning (P) module automatically generates geometrically valid bounding boxes. In the generation stage, a dual-module design synthesizes the target instance: the Decoupling (D) module employs Asymmetric Spectral Adaptation to disentangle structural identity from environmental illumination, while the Assimilation (A) module uses Neighborhood-Aware Texture Assimilation to harmonize the local manifold. By integrating these modules, PDA enforces multi-level consistency from global geometry to local micro-textures. Extensive experiments verify that PDA outperforms state-of-the-art methods in generative quality, reducing whole-image FID by 15.7% over the strongest baseline, and substantially improves downstream detection, boosting average mAP50 by +17.07 points over the real data.