{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:37:29Z","timestamp":1760146649049,"version":"build-2065373602"},"reference-count":52,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T00:00:00Z","timestamp":1732665600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In the realm of remote sensing image object detection compression distillation, establishing an efficient method for instance feature knowledge transfer between teacher and student models holds paramount importance. To this end, this paper introduces an innovative deep structured instance graph distillation method that endeavors to delve into the underlying information between instance features, thereby optimizing detection performance. Specifically, our proposed method incorporates feature instances and their relations into a graph-based structure (SIG). In this graph, feature instances serve as the nodes, while the relations between them serve as the edges. This structure enables us to capture both the individual significance of each feature instance and their collective influence within the context. Furthermore, in the experiment, we found that the index of some dense and small-target objects did not improve much because the edge assembly generated by a large number of background feature nodes in the SIG module inhibited the loss. To address the perennial imbalance between foreground and background features, we introduce an adaptive background feature mining strategy. Through carefully calibrated weights, this strategy effectively extracts and integrates background information, thereby minimizing noise interference in detection results and augmenting the expressive capacity of foreground features. We achieved state-of-the-art results on both the challenging DIOR and DOTA datasets, with the two-stage Oriented RCNN-based student Resnet18 model achieving a 73.23 mAP on the DOTA benchmark, close to the teacher Resnet101\u2019s 76.16. In addition, on the DIOR dataset, the student Resnet18 based on the two-stage Faster RCNN achieved 70.13 mAP, higher than the baseline 66.31, and the student Resnet50 achieved 72.28, higher than the teacher\u2019s 72.25.<\/jats:p>","DOI":"10.3390\/rs16234443","type":"journal-article","created":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T08:17:42Z","timestamp":1732695462000},"page":"4443","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["SIGKD: A Structured Instance Graph Distillation Method for Efficient Object Detection in Remote Sensing Images"],"prefix":"10.3390","volume":"16","author":[{"given":"Fangzhou","family":"Liu","sequence":"first","affiliation":[{"name":"The Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Key Laboratory of Spatial Information Processing and Application System Technology, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Key Laboratory of Target Cognition and Application Technology (TCAT), Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101408, China"}]},{"given":"Wenzhe","family":"Zhao","sequence":"additional","affiliation":[{"name":"The Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Key Laboratory of Spatial Information Processing and Application System Technology, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Key Laboratory of Target Cognition and Application Technology (TCAT), Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101408, China"}]},{"given":"Haoxiang","family":"Qi","sequence":"additional","affiliation":[{"name":"The Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Key Laboratory of Spatial Information Processing and Application System Technology, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Key Laboratory of Target Cognition and Application Technology (TCAT), Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101408, China"}]},{"given":"Guangyao","family":"Zhou","sequence":"additional","affiliation":[{"name":"The Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Key Laboratory of Spatial Information Processing and Application System Technology, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Key Laboratory of Target Cognition and Application Technology (TCAT), Chinese Academy of Sciences, Beijing 100190, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6154","DOI":"10.1109\/TGRS.2020.3023928","article-title":"SRAF-Net: Shape Robust Anchor-Free Network for Garbage Dumps in Remote Sensing Imagery","volume":"59","author":"Sun","year":"2021","journal-title":"IEEE Trans. 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