{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T01:12:44Z","timestamp":1769389964390,"version":"3.49.0"},"reference-count":74,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,1,14]],"date-time":"2021-01-14T00:00:00Z","timestamp":1610582400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 61675036"],"award-info":[{"award-number":["No. 61675036"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"13th Five-year Plan Equipment Pre-research","award":["No. 6140415020312"],"award-info":[{"award-number":["No. 6140415020312"]}]},{"name":"Chinese Academy of Sciences Key Laboratory of Beam Control","award":["No. 2017LBC006"],"award-info":[{"award-number":["No. 2017LBC006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p> Deep learning technology has been extensively explored by existing methods to improve the performance of target detection in remote sensing images, due to its powerful feature extraction and representation abilities. However, these methods usually focus on the interior features of the target, but ignore the exterior semantic information around the target, especially the object-level relationship. Consequently, these methods fail to detect and recognize targets in the complex background where multiple objects crowd together. To handle this problem, a diversified context information fusion framework based on convolutional neural network (DCIFF-CNN) is proposed in this paper, which employs the structured object-level relationship to improve the target detection and recognition in complex backgrounds. The DCIFF-CNN is composed of two successive sub-networks, i.e., a multi-scale local context region proposal network (MLC-RPN) and an object-level relationship context target detection network (ORC-TDN). The MLC-RPN relies on the fine-grained details of objects to generate candidate regions in the remote sensing image. Then, the ORC-TDN utilizes the spatial context information of objects to detect and recognize targets by integrating an attentional message integrated module (AMIM) and an object relational structured graph (ORSG). The AMIM is integrated into the feed-forward CNN to highlight the useful object-level context information, while the ORSG builds the relations between a set of objects by processing their appearance features and geometric features. Finally, the target detection method based on DCIFF-CNN effectively represents the interior and exterior information of the target by exploiting both the multiscale local context information and the object-level relationships. Extensive experiments are conducted, and experimental results demonstrate that the proposed DCIFF-CNN method improves the target detection and recognition accuracy in complex backgrounds, showing superiority to other state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/rs13020281","type":"journal-article","created":{"date-parts":[[2021,1,15]],"date-time":"2021-01-15T01:33:29Z","timestamp":1610674409000},"page":"281","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Structured Object-Level Relational Reasoning CNN-Based Target Detection Algorithm in a Remote Sensing Image"],"prefix":"10.3390","volume":"13","author":[{"given":"Bei","family":"Cheng","sequence":"first","affiliation":[{"name":"College of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China"}]},{"given":"Zhengzhou","family":"Li","sequence":"additional","affiliation":[{"name":"College of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China"},{"name":"Key Laboratory of Beam Control, Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China"}]},{"given":"Bitong","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China"}]},{"given":"Xu","family":"Yao","sequence":"additional","affiliation":[{"name":"College of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China"}]},{"given":"Zhiquan","family":"Ding","sequence":"additional","affiliation":[{"name":"Sichuan Institute of Aerospace Electronic Equipment, Chengdu 610100, China"}]},{"given":"Tianqi","family":"Qin","sequence":"additional","affiliation":[{"name":"Sichuan Institute of Aerospace Electronic Equipment, Chengdu 610100, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"947","DOI":"10.1109\/LGRS.2018.2889247","article-title":"A Sample Update-Based Convolutional Neural Network Framework for Object Detection in Large-Area Remote Sensing Images","volume":"16","author":"Hu","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1293","DOI":"10.1109\/LGRS.2017.2708722","article-title":"M-FCN: Effective Fully Convolutional Network-Based Airplane Detection Framework","volume":"14","author":"Yang","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zhu, M., Xu, Y., Ma, S., Li, S., Ma, H., and Han, Y. (2019). Effective Airplane Detection in Remote Sensing Images Based on Multilayer Feature Fusion and Improved Nonmaximal Suppression Algorithm. Remote Sens., 11.","DOI":"10.3390\/rs11091062"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2152","DOI":"10.1109\/TIP.2011.2172798","article-title":"Vehicle detection in aerial surveillance using dynamic Bayesian networks","volume":"21","author":"Cheng","year":"2012","journal-title":"IEEE Trans. Image Process."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4069","DOI":"10.1109\/JSTARS.2014.2308301","article-title":"Detection of Buildings in Multispectral Very High Spatial Resolution Images Using the Percentage Occupancy Hit-or-Miss Transform","volume":"7","author":"Stankov","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1109\/LGRS.2018.2803259","article-title":"Very High Resolution Object-Based Land Use\u2013Land Cover Urban Classification Using Extreme Gradient Boosting","volume":"15","author":"Georganos","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2692","DOI":"10.1109\/JSTARS.2018.2804440","article-title":"Monitoring of Urban Impervious Surfaces Using Time Series of High-Resolution Remote Sensing Images in Rapidly Urbanized Areas: A Case Study of Shenzhen","volume":"11","author":"Zhang","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.isprsjprs.2016.03.014","article-title":"A survey on object detection in optical remote sensing images","volume":"117","author":"Cheng","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1538","DOI":"10.1109\/JSTARS.2012.2199085","article-title":"Semi-Automated Road Detection From High Resolution Satellite Images by Directional Morphological Enhancement and Segmentation Techniques","volume":"5","author":"Chaudhuri","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1109\/LGRS.2012.2193552","article-title":"Building Detection in Very High Spatial Resolution Multispectral Images Using the Hit-or-Miss Transform","volume":"10","author":"Stankov","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.isprsjprs.2013.09.014","article-title":"Geographic Object-Based Image Analysis\u2014Towards a new paradigm","volume":"87","author":"Blaschke","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"9705","DOI":"10.3390\/rs70809705","article-title":"Identification of Forested Landslides Using LiDar Data, Object-based Image Analysis, and Machine Learning Algorithms","volume":"7","author":"Li","year":"2015","journal-title":"Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.isprsjprs.2015.01.013","article-title":"Water flow based geometric active deformable model for road network","volume":"102","author":"Leninisha","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1701","DOI":"10.1109\/TGRS.2012.2207123","article-title":"Automated Detection of Arbitrarily Shaped Buildings in Complex Environments From Monocular VHR Optical Satellite Imagery","volume":"51","author":"Ok","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.isprsjprs.2013.09.004","article-title":"Automated detection of buildings from single VHR multispectral images using shadow information and graph cuts","volume":"86","author":"Ok","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1080\/01431161.2012.705443","article-title":"Automatic landslide detection from remote-sensing imagery using a scene classification method based on BoVW and pLSA","volume":"34","author":"Cheng","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4238","DOI":"10.1109\/TGRS.2015.2393857","article-title":"Effective and Efficient Midlevel Visual Elements-Oriented Land-Use Classification Using VHR Remote Sensing Images","volume":"53","author":"Cheng","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","unstructured":"Cheng, G., Han, J., Zhou, P., and Guo, L. (2014, January 13\u201318). Scalable multi-class geospatial object detection in high-spatial-resolution remote sensing images. Proceedings of the 2014 IEEE Geoscience and Remote Sensing Symposium, Quebec City, QC, Canada."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"886","DOI":"10.1109\/LGRS.2012.2183337","article-title":"Automatic Target Detection in High-Resolution Remote Sensing Images Using a Contour-Based Spatial Model","volume":"9","author":"Yu","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"6508","DOI":"10.1109\/TGRS.2013.2296782","article-title":"VHR Object Detection Based on Structural Feature Extraction and Query Expansion","volume":"52","author":"Bai","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1109\/LGRS.2014.2358994","article-title":"Weakly Supervised Learning for Target Detection in Remote Sensing Images","volume":"12","author":"Dingwen","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.isprsjprs.2013.08.001","article-title":"Object detection in remote sensing imagery using a discriminatively trained mixture model","volume":"85","author":"Cheng","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1109\/LGRS.2010.2051792","article-title":"Airport Detection from Large IKONOS Images Using Clustered SIFT Keypoints and Region Information","volume":"8","author":"Tao","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.isprsjprs.2008.09.005","article-title":"Classification-based vehicle detection in high-resolution satellite images","volume":"64","author":"Eikvil","year":"2009","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1109\/LGRS.2012.2210189","article-title":"Texture-Based Airport Runway Detection","volume":"10","author":"Aytekin","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1939","DOI":"10.1109\/TGRS.2012.2236683","article-title":"Semi-Supervised Novelty Detection Using SVM Entire Solution Path","volume":"51","author":"Tuia","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3906","DOI":"10.1109\/TGRS.2011.2136381","article-title":"Use of Salient Features for the Design of a Multistage Framework to Extract Roads From High-Resolution Multispectral Satellite Images","volume":"49","author":"Das","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1109\/LGRS.2011.2180695","article-title":"A Visual Search Inspired Computational Model for Ship Detection in Optical Satellite Images","volume":"9","author":"Fukun","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_29","first-page":"4099","article-title":"Local Manifold Learning-Based k-Nearest-Neighbor for Hyperspectral Image Classification","volume":"48","author":"Ma","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1804","DOI":"10.1109\/TGRS.2008.916090","article-title":"Nearest Neighbor Classification of Remote Sensing Images with the Maximal Margin Principle","volume":"46","author":"Blanzieri","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2327","DOI":"10.1109\/JSTARS.2013.2242846","article-title":"Airborne Vehicle Detection in Dense Urban Areas Using HoG Features and Disparity Maps","volume":"6","author":"Tuermer","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4511","DOI":"10.1109\/TGRS.2013.2282355","article-title":"Ship Detection in High-Resolution Optical Imagery Based on Anomaly Detector and Local Shape Feature","volume":"52","author":"Zhenwei","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2795","DOI":"10.1109\/TGRS.2010.2043109","article-title":"Vehicle Detection in Very High Resolution Satellite Images of City Areas","volume":"48","author":"Leitloff","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1109\/JSTARS.2010.2053521","article-title":"Building Detection from One Orthophoto and High-Resolution InSAR Data Using Conditional Random Fields","volume":"4","author":"Wegner","year":"2011","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"4483","DOI":"10.1109\/TGRS.2015.2400462","article-title":"Robust Rooftop Extraction from Visible Band Images Using Higher Order CRF","volume":"53","author":"Li","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1227","DOI":"10.1109\/TGRS.2013.2248738","article-title":"Bi-Temporal Texton Forest for Land Cover Transition Detection on Remotely Sensed Imagery","volume":"52","author":"Lei","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.isprsjprs.2015.02.006","article-title":"Multilayer Markov Random Field models for change detection in optical remote sensing images","volume":"107","author":"Benedek","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1830","DOI":"10.1109\/JSTARS.2015.2416255","article-title":"Target Detection Based on Random Forest Metric Learning","volume":"8","author":"Dong","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201312). Fast R-CNN. Proceedings of the IEEE Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_41","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster R-CNN-Towards Real-Time Object Detection with Region Proposal Networks. Proceedings of the 28th International Conference on Neural Information Processing Systems, MIT Press."},{"key":"ref_42","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. Computer Vision and Pattern Recognition, IEEE Press."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A.C. (2016, January 11\u201314). SSD: Single Shot Multi Box Detector. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_44","unstructured":"Dai, J., Li, Y., He, K., and Sun, J. (2016). R-FCN:Object Detection via Region-based Fully Convolutional Networks. Computer Vision and Pattern Recognition, IEEE Press."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1109\/LGRS.2018.2872355","article-title":"Multiscale Visual Attention Networks for Object Detection in VHR Remote Sensing Images","volume":"16","author":"Wang","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"7405","DOI":"10.1109\/TGRS.2016.2601622","article-title":"Learning Rotation-Invariant Convolutional Neural Networks for Object Detection in VHR Optical Remote Sensing Images","volume":"54","author":"Cheng","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"3377","DOI":"10.1109\/TGRS.2019.2954328","article-title":"FMSSD: Feature-Merged Single-Shot Detection for Multiscale Objects in Large-Scale Remote Sensing Imagery","volume":"58","author":"Wang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2020.04.019","article-title":"HyNet: Hyper-scale object detection network framework for multiple spatial resolution remote sensing imagery","volume":"166","author":"Zheng","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"3325","DOI":"10.1109\/TGRS.2014.2374218","article-title":"Object Detection in Optical Remote Sensing Images Based on Weakly Supervised Learning and High-Level Feature Learning","volume":"53","author":"Han","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1109\/TPAMI.2014.2343217","article-title":"Contextualizing Object Detection and Classification","volume":"37","author":"Chen","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Choi, M.J., Lim, J.J., Torralba, A., and Willsky, A.S. (2010, January 13\u201318). Exploiting Hierarchical Context on a Large Database of Object Categories. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5540221"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Liu, Y., Wang, R., Shan, S., and Chen, X. (2018). Structure Inference Net: Object Detection Using Scene-Level Context and Instance-Level Relationships. Conference on Computer Vision and Pattern Recognition, IEEE Press.","DOI":"10.1109\/CVPR.2018.00730"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Bell, S., Zitnick, C.L., Bala, K., and Girshick, R. (2015, January 7\u201312). Inside-Outside Net-Detecting Objectsin Contextwith Skip Poolingand Recurrent Neural Networks. Proceedings of the IEEE Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2016.314"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"712","DOI":"10.1016\/j.cviu.2010.02.004","article-title":"Context based object categorization: A critical survey","volume":"114","author":"Galleguillos","year":"2010","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Shrivastava, A., and Gupta, A. (2016, January 11\u201314). Contextual Priming and Feedback for Faster R-CNN. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_20"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Mottaghi, R., Chen, X., Liu, X., Cho, N.-G., Lee, S.-W., Fidler, S., Urtasun, R., and Yuille, A. (2014, January 23\u201328). The Role of Context for Object Detection and Semantic Segmentation in the Wild. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.119"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Zeng, X., Ouyang, W., Yang, B., Yan, J., and Wang, X. (2016, January 11\u201314). Gated Bi-directional CNN for Object Detection. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46478-7_22"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"944","DOI":"10.1109\/TMM.2016.2642789","article-title":"Attentive Contexts for Object Detection","volume":"19","author":"Li","year":"2017","journal-title":"IEEE Trans. Multimed."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1109\/TIP.2019.2933728","article-title":"Image Representations with Spatial Object-to-Object Relations for RGB-D Scene Recognition","volume":"29","author":"Song","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Cho, K., van Merrienboer, B., and Bahdanau, D. (2014, January 25). On the Properties of Neural Machine Translation: Encoder\u2013Decoder approaches. Proceedings of the Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation (SSST-8), Doha, Qatar.","DOI":"10.3115\/v1\/W14-4012"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Xu, D., Zhu, Y., Chozy, C.B., and Fei-Fei, L. (2018, January 18\u201322). Scene Graph Generation by Iterative Message Passing. Proceedings of the IEEE Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2017.330"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Deng, Z., Vahdat, A., Hu, H., and Mori, G. (2016, January 27\u201330). Structure Inference Machines: Recurrent Neural Networks for Analyzing Relations in Group Activity Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.516"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Hu, H., Zhou, G.-T., Deng, Z., Liao, Z., and Mori, G. (2016, January 27\u201330). Learning Structured Inference Neural Networks with Label Relations. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA,.","DOI":"10.1109\/CVPR.2016.323"},{"key":"ref_64","unstructured":"Seo, Y., Defferrard, M.E., Vandergheynst, P., and Bresson, X. Structured Sequence Modeling with Graph Convolutional Recurrent Networks, Proceedings of the International Conference on Learning Representations."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Marino, K., Salakhutdinov, R., and Gupta, A. (2017, January 21\u201326). The More You Know: Using Knowledge Graphs for Image Classification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.10"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Fan, Q., Zhuo, W., Tang, C.-K., and Tai, Y.-W. (2020, January 14\u201319). Few-Shot Object Detection with Attention-RPN and Multi-Relation Detector. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00407"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1109\/TIP.2018.2865280","article-title":"Attention CoupleNet: Fully Convolutional Attention Coupling Network for Object Detection","volume":"28","author":"Zhu","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"2972","DOI":"10.1109\/TCSVT.2018.2875449","article-title":"Multi-Scale Attention Deep Neural Network for Fast Accurate Object Detection","volume":"29","author":"Song","year":"2019","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_69","unstructured":"Sanghyun, W., Park, J., Lee, J.-Y., and Kweon, I.S. (2018, January 8\u201314). CBAM Convolutional Block Attention Module. Proceedings of the European Conference on Computer Vision, Munich, Germany."},{"key":"ref_70","unstructured":"Simonyan, K., and Zisserman, A. (2015, January 7\u201312). Very Deep Convolutional Networks for Large-Scale Image Recognition. In Computer Vision and Pattern Recognition. Proceedings of the Computer Vision and Pattern Recognition, Boston, MA, USA."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.isprsjprs.2014.10.002","article-title":"Multi-class geospatial object detection and geographic image classification based on collection of part detectors","volume":"98","author":"Cheng","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, Faster, Stronger. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_73","unstructured":"Bochkovskiy, A., Wang, C.-Y., and Liao, H.-J.M. (2020, January 13\u201319). YOLOv4 Optimal Speed and Accuracy of Object Detection. Proceedings of the Computer Vision and Pattern Recognition, Seattle, WA, USA."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Cai, Z., Fan, Q., Feris, R.S., and Vasconcelos, N. (2016, January 8\u201316). A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46493-0_22"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/2\/281\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:11:21Z","timestamp":1760159481000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/2\/281"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,14]]},"references-count":74,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2021,1]]}},"alternative-id":["rs13020281"],"URL":"https:\/\/doi.org\/10.3390\/rs13020281","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,14]]}}}