PDA++：遥感中的场对齐规划与场景自适应插入
PDA++: Field-Aligned Planning and Scene-Adaptive Insertion in Remote Sensing
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中文总结 AI 辅助
PDA++提出统一的环境感知目标插入框架，通过规划、解耦、同化三阶段实现遥感图像中逼真目标插入，显著提升少样本识别与检测性能。
中文摘要 AI 辅助
遥感识别常常受到稀有目标观测稀少和标注成本高昂的制约，这使得在少样本和长尾场景中，逼真的合成增强尤为有价值。目标插入提供了一种高效的方式，在保持真实背景场景的同时增加目标多样性，但在俯视图像中进行逼真插入要求生成的目标能够与其周围环境连贯地适应。为此，我们提出了PDA++，一个统一的环境感知目标插入框架，其组织为规划、解耦和同化三个阶段。规划阶段通过一个结合几何间隙与结构和尺度感知线索的可供性场来确定场景兼容的姿态。解耦阶段引入一个姿态条件背景，提供精确的空间引导以及目标-场景上下文，使参考目标在适应目标观测的同时保持其身份。这种构造还自然地提供像素级掩码用于分割增强。同化阶段通过最优传输对齐多尺度纹理分布，进一步改善局部连贯性。在光学基准上，PDA++实现了6.28的整体图像FID，并将平均少样本识别mAP50提高了17.69个百分点，相对于真实数据基线对应28.8%的相对增益。在SAR图像上，它将船舶检测提高了4.10个mAP50点，并在跨数据集迁移和无定形目标插入下保持有效。代码可在以下URL获取。
英文摘要
Remote sensing recognition is often constrained by scarce observations of rare targets and costly annotations, making realistic synthetic augmentation particularly valuable for few-shot and long-tailed scenarios. Object insertion provides an efficient way to increase target diversity while preserving authentic background scenes, but realistic insertion in overhead imagery requires the generated target to adapt coherently to its surrounding environment. To this end, we propose PDA++, a unified environment-aware object insertion framework organized as Plan, Decouple, and Assimilate. Planning determines scene-compatible poses through an affordance field that combines geometric clearance with structure- and scale-aware cues. Decoupling introduces a pose-conditioned background that provides precise spatial guidance together with target-scene context, allowing the reference object to preserve its identity while adapting to the target observation. This construction also naturally provides pixel-level masks for segmentation augmentation. Assimilation further improves local coherence by aligning multi-scale texture distributions through optimal transport. On the optical benchmark, PDA++ achieves a whole-image FID of 6.28 and improves average few-shot recognition mAP50 by 17.69 points, corresponding to a 28.8% relative gain over the real-data baseline. On SAR imagery, it improves ship detection by 4.10 mAP50 points and remains effective under cross-dataset transfer and amorphous-target insertion. Code is available at https://github.com/lisheyu972/PDA_PLUS.
发表机构
- Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）
- University of Chinese Academy of Sciences（中国科学院大学）
机构由 AI 辅助整理，请以论文原文为准。
