STAR: A First-Ever Dataset and a Large-Scale Benchmark for Scene Graph Generation in Large-Size Satellite Imagery

• Published in: IEEE transactions on pattern analysis and machine intelligence Vol. 47; no. 3; pp. 1832 - 1849
• Main Authors: Li, Yansheng, Wang, Linlin, Wang, Tingzhu, Yang, Xue, Luo, Junwei, Wang, Qi, Deng, Youming, Wang, Wenbin, Sun, Xian, Li, Haifeng, Dang, Bo, Zhang, Yongjun, Yu, Yi, Yan, Junchi
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2025
• Subjects: Annotations; Benchmark testing; Bridges; Cognition; Complexity theory; Large-size satellite imagery; Marine vehicles; Object detection; relationship prediction; Satellite images; scene graph generation benchmark; Stars; Visualization
• ISSN: 0162-8828; 1939-3539; 2160-9292; 1939-3539
• DOI: 10.1109/TPAMI.2024.3508072

Scene graph generation (SGG) in satellite imagery (SAI) benefits promoting 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 attractive to holistically conduct SGG in large-size very-high-resolution (VHR) SAI. However, there lack such SGG datasets. Due to the complexity of large-size SAI, mining triplets <inline-formula><tex-math notation="LaTeX">< </tex-math> <mml:math><mml:mo><</mml:mo></mml:math><inline-graphic xlink:href="li-ieq1-3508072.gif"/> </inline-formula>subject, relationship, object<inline-formula><tex-math notation="LaTeX">> </tex-math> <mml:math><mml:mo>></mml:mo></mml:math><inline-graphic xlink:href="li-ieq2-3508072.gif"/> </inline-formula> heavily relies on long-range contextual reasoning. Consequently, SGG models designed for small-size natural imagery are not directly applicable to large-size SAI. This paper constructs a large-scale dataset for SGG in large-size VHR SAI with image sizes ranging from 512 × 768 to 27 860 × 31 096 pixels, named STAR (Scene graph generaTion in lArge-size satellite imageRy), encompassing over 210K objects and over 400K triplets. To realize SGG in large-size SAI, we propose a context-aware cascade cognition (CAC) framework to understand SAI regarding object detection (OBD), pair pruning and relationship prediction for SGG. We also release a SAI-oriented SGG toolkit with about 30 OBD and 10 SGG methods which need further adaptation by our devised modules on our challenging STAR dataset.