{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T08:42:46Z","timestamp":1769503366283,"version":"3.49.0"},"reference-count":45,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2020,6,4]],"date-time":"2020-06-04T00:00:00Z","timestamp":1591228800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The detection of objects in very high-resolution (VHR) remote sensing images has become increasingly popular with the enhancement of remote sensing technologies. High-resolution images from aircrafts or satellites contain highly detailed and mixed backgrounds that decrease the success of object detection in remote sensing images. In this study, a model that performs weighted ensemble object detection using optimized coefficients is proposed. This model uses the outputs of three different object detection models trained on the same dataset. The model\u2019s structure takes two or more object detection methods as its input and provides an output with an optimized coefficient-weighted ensemble. The Northwestern Polytechnical University Very High Resolution 10 (NWPU-VHR10) and Remote Sensing Object Detection (RSOD) datasets were used to measure the object detection success of the proposed model. Our experiments reveal that the proposed model improved the Mean Average Precision (mAP) performance by 0.78%\u201316.5% compared to stand-alone models and presents better mean average precision than other state-of-the-art methods (3.55% higher on the NWPU-VHR-10 dataset and 1.49% higher when using the RSOD dataset).<\/jats:p>","DOI":"10.3390\/ijgi9060370","type":"journal-article","created":{"date-parts":[[2020,6,5]],"date-time":"2020-06-05T03:32:21Z","timestamp":1591327941000},"page":"370","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Weighted Ensemble Object Detection with Optimized Coefficients for Remote Sensing Images"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3704-267X","authenticated-orcid":false,"given":"Atakan","family":"K\u00f6rez","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Faculty of Technology, Gazi University, Ankara 06560, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8762-5091","authenticated-orcid":false,"given":"Necaattin","family":"Bar\u0131\u015f\u00e7\u0131","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Faculty of Technology, Gazi University, Ankara 06560, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8669-823X","authenticated-orcid":false,"given":"Ayd\u0131n","family":"\u00c7etin","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Faculty of Technology, Gazi University, Ankara 06560, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"U\u00e7man","family":"Erg\u00fcn","sequence":"additional","affiliation":[{"name":"Biomedical Engineering Department, Afyon Kocatepe University, Afyon 03300, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"128837","DOI":"10.1109\/ACCESS.2019.2939201","article-title":"A Survey of Deep Learning-Based Object Detection","volume":"7","author":"Jiao","year":"2019","journal-title":"IEEE Access"},{"key":"ref_2","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":"Peicheng","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","first-page":"309","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_4","unstructured":"Wu, X., Hong, D., Chanussot, J., Xu, Y., Tao, R., and Wang, Y. (2019). Fourier-Based Rotation-Invariant Feature Boosting: An Efficient Framework for Geospatial Object Detection. IEEE Geosci. Remote Sens. Lett., 1\u20135."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"8534","DOI":"10.1109\/TGRS.2019.2921396","article-title":"Sig-NMS-Based Faster R-CNN Combining Transfer Learning for Small Target Detection in VHR Optical Remote Sensing Imagery","volume":"57","author":"Dong","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1109\/TGRS.2019.2930246","article-title":"Context-Aware Convolutional Neural Network for Object Detection in VHR Remote Sensing Imagery","volume":"58","author":"Gong","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"144691","DOI":"10.1109\/ACCESS.2019.2943346","article-title":"Multi-Class Objects Detection Method in Remote Sensing Image Based on Direct Feedback Control for Convolutional Neural Network","volume":"7","author":"Cheng","year":"2019","journal-title":"IEEE Access"},{"key":"ref_8","first-page":"3377","article-title":"FMSSD: Feature-Merged Single-Shot Detection for Multiscale Objects in Large-Scale Remote Sensing Imagery","volume":"38","author":"Wang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"20818","DOI":"10.1109\/ACCESS.2019.2960931","article-title":"Rotation-Invariant Feature Learning for Object Detection in VHR Optical Remote Sensing Images by Double-Net","volume":"8","author":"Zhang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_10","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_11","doi-asserted-by":"crossref","first-page":"2337","DOI":"10.1109\/TGRS.2017.2778300","article-title":"Rotation-Insensitive and Context-Augmented Object Detection in Remote Sensing Images","volume":"56","author":"Li","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","first-page":"1","article-title":"Geospatial Object Detection via Deconvolutional Region Proposal Network","volume":"12","author":"Wang","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Obs. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"K\u00f6rez, A., and Bar\u0131\u015f\u00e7\u0131, N. (2019). Object Detection with Low Capacity GPU Systems Using Improved Faster R-CNN. Appl. Sci., 10.","DOI":"10.3390\/app10010083"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Qiu, H., Li, H., Wu, Q., Meng, F., Ngan, K.N., and Shi, H. (2019). A2RMNet: Adaptively Aspect Ratio Multi-Scale Network for Object Detection in Remote Sensing Images. Remote Sens., 11.","DOI":"10.3390\/rs11131594"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Xu, Z., Xu, X., Wang, L., Yang, R., and Pu, F. (2017). Deformable ConvNet with Aspect Ratio Constrained NMS for Object Detection in Remote Sensing Imagery. Remote Sens., 9.","DOI":"10.3390\/rs9121312"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhou, K., Zhang, Z., Gao, C., and Liu, J. (2020). Rotated Feature Network for Multiorientation Object Detection of Remote-Sensing Images. IEEE Geosci. Remote Sens. Lett., 1\u20135.","DOI":"10.1109\/LGRS.2020.2965629"},{"key":"ref_17","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_18","doi-asserted-by":"crossref","first-page":"3978","DOI":"10.1109\/TGRS.2007.907109","article-title":"A Multiple Conditional Random Fields Ensemble Model for Urban Area Detection in Remote Sensing Optical Images","volume":"45","author":"Zhong","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1109\/LGRS.2011.2182032","article-title":"Ensemble Methodology Using Multistage Learning for Improved Detection of Harmful Algal Blooms","volume":"9","author":"Gokaraju","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1109\/LGRS.2013.2245855","article-title":"Ensemble of Multilayer Perceptrons for Change Detection in Remotely Sensed Images","volume":"11","author":"Roy","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1109\/LGRS.2017.2786241","article-title":"Aerial Scene Classification via Multilevel Fusion Based on Deep Convolutional Neural Networks","volume":"15","author":"Yu","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"732","DOI":"10.1109\/LGRS.2018.2880136","article-title":"Deep Network Ensembles for Aerial Scene Classification","volume":"16","author":"Dede","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"61797","DOI":"10.1109\/ACCESS.2019.2915985","article-title":"Insulator Fault Detection in Aerial Images Based on Ensemble Learning with Multi-level Perception","volume":"7","author":"Jiang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C., and Berg, A. (2016). SSD: Single Shot MultiBox Detector. Eur. Conf. Comput. Vis. (ECCV), 21\u201337.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_25","first-page":"318","article-title":"Focal loss for object detection","volume":"42","author":"Lin","year":"2017","journal-title":"IEEE Int. Conf. Comput. Vis."},{"key":"ref_26","first-page":"91","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume":"39","author":"Ren","year":"2015","journal-title":"Proc. Adv. Neural Inform. Process. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., and Wei, Y. (2017, January 22\u201329). Deformable convolutional networks. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.89"},{"key":"ref_28","unstructured":"Tsung-Yi, L., Dollar, P., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature Pyramid Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA."},{"key":"ref_29","unstructured":"Qiao, S., Wang, H., Liu, C., Shen, W., and Yuille, A. (2019). Weight Standardization. Arxiv Prepr."},{"key":"ref_30","unstructured":"Ruszczy\u0144ski, A. (2006). Nonlinear Optimization, Princeton University Press."},{"key":"ref_31","unstructured":"Lehmann, E.L., and Casella, G. (1998). Theory of Point Estimation, Springer Texts in Statics. [2nd ed.]."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Liu, L., and \u00d6zsu, M.T. (2009). Encyclopedia of Database Systems, Springer.","DOI":"10.1007\/978-0-387-39940-9"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009). ImageNet: A Large-Scale Hierarchical Image Database. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 165\u2013171.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_34","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":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1109\/LGRS.2018.2882778","article-title":"Detection of Multiclass Objects in Optical Remote Sensing Images","volume":"16","author":"Liu","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"5512","DOI":"10.1109\/TGRS.2019.2899955","article-title":"R2 -CNN: Fast Tiny Object Detection in Large-Scale Remote Sensing Images","volume":"57","author":"Pang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","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_38","doi-asserted-by":"crossref","first-page":"8445","DOI":"10.1109\/TGRS.2019.2921111","article-title":"Detecting Small Objects in Urban Settings Using SlimNet Model","volume":"57","author":"Yang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1109\/TGRS.2019.2935177","article-title":"Gated and Axis-Concentrated Localization Network for Remote Sensing Object Detection","volume":"58","author":"Lu","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","first-page":"37","article-title":"Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness and Correlation","volume":"2","author":"Powers","year":"2011","journal-title":"J. Mach. Learn. Technol."},{"key":"ref_41","unstructured":"(2019, July 15). NWPU-VHR10 Dataset. Available online: http:\/\/www.escience.cn\/people\/gongcheng\/NWPU-VHR-10.html."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1080\/01431161.2014.999881","article-title":"Elliptic Fourier transformation-based histograms of oriented gradients for rotationally invariant object detection in remote-sensing images","volume":"36","author":"Xiao","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_43","unstructured":"(2020, January 07). Vaihingen Dataset. Available online: http:\/\/www2.isprs.org\/commissions\/comm3\/wg4\/2d-sem-label-vaihingen.html."},{"key":"ref_44","unstructured":"(2020, March 07). China Official Web Mapping Service, Available online: www.tianditu.gov.cn."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Buslaev, A., Parinov, A., Khvedchenya, E., Iglovikov, V., and Kalinin, A. (2020). Albumentations: Fast and Flexible Image Augmentations. Information, 11.","DOI":"10.3390\/info11020125"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/6\/370\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:35:32Z","timestamp":1760175332000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/6\/370"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,4]]},"references-count":45,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2020,6]]}},"alternative-id":["ijgi9060370"],"URL":"https:\/\/doi.org\/10.3390\/ijgi9060370","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6,4]]}}}