{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T12:07:11Z","timestamp":1781525231867,"version":"3.54.1"},"reference-count":78,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,7,17]],"date-time":"2021-07-17T00:00:00Z","timestamp":1626480000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFC0823002"],"award-info":[{"award-number":["2018YFC0823002"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100016692","name":"Key Research and Development Program of Ningxia","doi-asserted-by":"publisher","award":["2019BFG02009"],"award-info":[{"award-number":["2019BFG02009"]}],"id":[{"id":"10.13039\/100016692","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Nature Science Foundation of China","award":["61801019"],"award-info":[{"award-number":["61801019"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>A deep understanding of our visual world is more than an isolated perception on a series of objects, and the relationships between them also contain rich semantic information. Especially for those satellite remote sensing images, the span is so large that the various objects are always of different sizes and complex spatial compositions. Therefore, the recognition of semantic relations is conducive to strengthen the understanding of remote sensing scenes. In this paper, we propose a novel multi-scale semantic fusion network (MSFN). In this framework, dilated convolution is introduced into a graph convolutional network (GCN) based on an attentional mechanism to fuse and refine multi-scale semantic context, which is crucial to strengthen the cognitive ability of our model Besides, based on the mapping between visual features and semantic embeddings, we design a sparse relationship extraction module to remove meaningless connections among entities and improve the efficiency of scene graph generation. Meanwhile, to further promote the research of scene understanding in remote sensing field, this paper also proposes a remote sensing scene graph dataset (RSSGD). We carry out extensive experiments and the results show that our model significantly outperforms previous methods on scene graph generation. In addition, RSSGD effectively bridges the huge semantic gap between low-level perception and high-level cognition of remote sensing images.<\/jats:p>","DOI":"10.3390\/ijgi10070488","type":"journal-article","created":{"date-parts":[[2021,7,18]],"date-time":"2021-07-18T21:16:48Z","timestamp":1626643008000},"page":"488","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Semantic Relation Model and Dataset for Remote Sensing Scene Understanding"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5453-7389","authenticated-orcid":false,"given":"Peng","family":"Li","sequence":"first","affiliation":[{"name":"School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China"},{"name":"Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3456-5259","authenticated-orcid":false,"given":"Dezheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China"},{"name":"Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7228-7838","authenticated-orcid":false,"given":"Aziguli","family":"Wulamu","sequence":"additional","affiliation":[{"name":"School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China"},{"name":"Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7909-9012","authenticated-orcid":false,"given":"Xin","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China"},{"name":"Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing 100083, China"},{"name":"Surgery Simulation Research Laboratory, University of Alberta, Edmonton, AB T6G 2E1, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0519-169X","authenticated-orcid":false,"given":"Peng","family":"Chen","sequence":"additional","affiliation":[{"name":"FINTECH Innovation Division, Postal Savings Bank of China, Beijing 100808, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"813","DOI":"10.1016\/j.neucom.2016.05.061","article-title":"Local structure learning in high resolution remote sensing image retrieval","volume":"207","author":"Du","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1085","DOI":"10.1109\/TGRS.2016.2619384","article-title":"Multiple Kernel Sparse Representation for Airborne LiDAR Data Classification","volume":"55","author":"Gu","year":"2017","journal-title":"IEEE Trans. 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