Chen J, Zhou X, Zhang Y, et al. Message-Passing-Driven Triplet Representation for Geo-Object Relational Inference in HRSI[J]. IEEE Geoscience and Remote Sensing Letters, 2021.[DOI: 10.1109/LGRS.2020.3038569]
发布时间：2021-06-18
点击次数：
摘要：A high-resolution remote sensing image (HRSI) scene typically contains multiple geo-objects, and geospatial relations among these geo-objects are obvious. As the important information conveyed by HRSI, the intelligent expression of geospatial relation is helpful in understanding HRSI scenes. Previous HRSI semantic understanding was mainly based on image captions that only generate one sentence to describe image content, thereby resulting in insufficient understanding of the scene. Thus, the present letter proposes an approach to represent geospatial relations in an HRSI scene with structured form of łanglesubject, geospatial relation, object⟩. A geospatial relation triplet representation data set that contains visual and semantic information, such as category, location, and geospatial relations of the geo-objects, is constructed first. An ``object-relation'' message-passing mechanism is adopted to enhance the information exchange between the geo-objects and geospatial relations to predict triplets accurately. The experimental results show that the proposed method can effectively predict the geospatial relation in a HRSI scene.
论文类型：期刊论文
是否译文：否
收录刊物：SCI
发布期刊链接：https://ieeexplore.ieee.org/document/9325509
上一条： Chen J, Huang H, Peng J, et al. Contextual Information-Preserved Architecture Learning for Remote-Sensing Scene Classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021.[DOI: 10.1109/TGRS.2021.3063287]
下一条： 陈杰,戴欣宜,周兴,孙庚,邓敏.2021.双LSTM驱动的高分遥感影像地物目标空间关系语义描述.遥感学报,25(5):1085-1094.[DOI:10.11834/jrs.20210340]