Scene graph generation (SGG) in satellite imagery (SAI) benefits promoting
intelligent understanding of geospatial scenarios from perception to cognition.
In SAI, objects exhibit great variations in scales and aspect ratios, and there
exist rich relationships between objects (even between spatially disjoint
objects), which makes it necessary to holistically conduct SGG in large-size
very-high-resolution (VHR) SAI. However, the lack of SGG datasets with
large-size VHR SAI has constrained the advancement of SGG in SAI. Due to the
complexity of large-size VHR SAI, mining triplets <subject, relationship,
object> in large-size VHR SAI heavily relies on long-range contextual
reasoning. Consequently, SGG models designed for small-size natural imagery are
not directly applicable to large-size VHR SAI. To address the scarcity of
datasets, this paper constructs a large-scale dataset for SGG in large-size VHR
SAI with image sizes ranging from 512 x 768 to 27,860 x 31,096 pixels, named
RSG, encompassing over 210,000 objects and more than 400,000 triplets. To
realize SGG in large-size VHR SAI, we propose a context-aware cascade cognition
(CAC) framework to understand SAI at three levels: object detection (OBD), pair
pruning and relationship prediction. As a fundamental prerequisite for SGG in
large-size SAI, a holistic multi-class object detection network (HOD-Net) that
can flexibly integrate multi-scale contexts is proposed. With the consideration
that there exist a huge amount of object pairs in large-size SAI but only a
minority of object pairs contain meaningful relationships, we design a pair
proposal generation (PPG) network via adversarial reconstruction to select
high-value pairs. Furthermore, a relationship prediction network with
context-aware messaging (RPCM) is proposed to predict the relationship types of
these pairs.

该研究论文提出了一个面向大尺寸卫星图像的场景图生成方法，构建了一个大规模的 SGG 数据集，通过上下文感知级联认知框架实现了场景图的检测、筛选和关系预测。