超越连接点：基于密集特征一致性的卫星影像区域网平差
Beyond Tie Points: Satellite Image Block Adjustment based on Dense Feature Consistency
用预训练特征提取器提取密集特征和视差感知置信图，以置信度加权特征一致性替代稀疏连接点进行区域网平差，为遥感影像几何校正提供了不依赖显式连接点的可复用优化框架。
原文依据 A pretrained feature extractor is employed to extract robust dense features and a parallax-aware confidence map from each image.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论由于卫星影像的立体几何较弱，平面区域网平差（PBA）是校正卫星影像几何畸变的主流技术，该技术将高程视为已知约束并主要优化平面坐标。现有PBA方法主要依赖显式连接点，受高程不准确（如高层建筑附近）引起的视差和不可逆误差累积的影响，严重降低了平差精度。本文提出一种“超越连接点”的卫星影像平差范式。采用预训练特征提取器从每幅影像中提取稳健的密集特征和视差感知置信图。然后，基于置信度加权特征一致性，通过网格化由粗到细的优化框架直接求解平差参数。在覆盖北京、广州和圣何塞的多视角卫星影像数据集上进行的实验表明，所提方法在精度和鲁棒性上均显著优于传统方法，与传统PBA相比平均误差降低高达75.43%。
英文原文摘要 1144 字符
Owing to the weak stereo geometry of satellite images, Planar Block Adjustment (PBA) is a predominant technique for correcting geometric distortions in satellite images, which treats elevation as a known constraint and primarily optimizes planar coordinates. Existing PBA methods mainly rely on explicit tie points, suffering from parallax caused by inaccurate elevation (e.g., near high buildings) and irreversible error accumulation, which severely degrades adjustment accuracy. In this paper, a "Beyond Tie Points" paradigm for satellite image adjustment is proposed. A pretrained feature extractor is employed to extract robust dense features and a parallax-aware confidence map from each image. A gridded coarse-to-fine optimization framework then directly solves for the adjustment parameters basing on confidence-weighted feature consistency. Experiments conducted on multiview satellite image datasets covering Beijing, Guangzhou and San Jose demonstrate that the proposed method is significantly superior to traditional approaches in both accuracy and robustness, reducing the average error by up to 75.43% compared to traditional PBA.