{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T19:39:50Z","timestamp":1786045190424,"version":"3.56.0"},"reference-count":40,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2020,9,25]],"date-time":"2020-09-25T00:00:00Z","timestamp":1600992000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001665","name":"Agence Nationale de la Recherche","doi-asserted-by":"publisher","award":["ANR-17-ASTR-0016"],"award-info":[{"award-number":["ANR-17-ASTR-0016"]}],"id":[{"id":"10.13039\/501100001665","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This article tackles the problem of detecting small objects in satellite or aerial remote sensing images by relying on super-resolution to increase image spatial resolution, thus the size and details of objects to be detected. We show how to improve the super-resolution framework starting from the learning of a generative adversarial network (GAN) based on residual blocks and then its integration into a cycle model. Furthermore, by adding to the framework an auxiliary network tailored for object detection, we considerably improve the learning and the quality of our final super-resolution architecture, and more importantly increase the object detection performance. Besides the improvement dedicated to the network architecture, we also focus on the training of super-resolution on target objects, leading to an object-focused approach. Furthermore, the proposed strategies do not depend on the choice of a baseline super-resolution framework, hence could be adopted for current and future state-of-the-art models. Our experimental study on small vehicle detection in remote sensing data conducted on both aerial and satellite images (i.e., ISPRS Potsdam and xView datasets) confirms the effectiveness of the improved super-resolution methods to assist with the small object detection tasks.<\/jats:p>","DOI":"10.3390\/rs12193152","type":"journal-article","created":{"date-parts":[[2020,9,28]],"date-time":"2020-09-28T08:02:58Z","timestamp":1601280178000},"page":"3152","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":128,"title":["Small Object Detection in Remote Sensing Images Based on Super-Resolution with Auxiliary Generative Adversarial Networks"],"prefix":"10.3390","volume":"12","author":[{"given":"Luc","family":"Courtrai","sequence":"first","affiliation":[{"name":"IRISA, Universit\u00e9 Bretagne Sud, UMR 6074, 56000 Vannes, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0266-767X","authenticated-orcid":false,"given":"Minh-Tan","family":"Pham","sequence":"additional","affiliation":[{"name":"IRISA, Universit\u00e9 Bretagne Sud, UMR 6074, 56000 Vannes, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2384-8202","authenticated-orcid":false,"given":"S\u00e9bastien","family":"Lef\u00e8vre","sequence":"additional","affiliation":[{"name":"IRISA, Universit\u00e9 Bretagne Sud, UMR 6074, 56000 Vannes, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,25]]},"reference":[{"key":"ref_1","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015, January 7\u201312). Faster R-CNN: Towards real-time object detection with region proposal networks. Proceedings of the Advances in Neural Information Processing System, Montreal, QC, Cananda."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 8\u201316). SSD: Single shot multibox detector. Proceedings of the European Conference on Computer Vision (ECCV), Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201426). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_5","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., and Le, Q.V. (2020, January 13\u201319). Efficientdet: Scalable and efficient object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref_7","unstructured":"Zou, Z., Shi, Z., Guo, Y., and Ye, J. (2019). Object detection in 20 years: A survey. arXiv."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"106838","DOI":"10.1109\/ACCESS.2019.2932731","article-title":"An improved faster R-CNN for small object detection","volume":"7","author":"Cao","year":"2019","journal-title":"IEEE Access"},{"key":"ref_9","unstructured":"Cao, G., Xie, X., Yang, W., Liao, Q., Shi, G., and Wu, J. (2018, January 14\u201316). Feature-fused SSD: Fast detection for small objects. Proceedings of the Ninth International Conference on Graphic and Image Processing (ICGIP 2017), Qingdao, China."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhang, S., Wen, L., Bian, X., Lei, Z., and Li, S.Z. (2018, January 18\u201322). Single-shot refinement neural network for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00442"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"951","DOI":"10.2991\/ijcis.11.1.72","article-title":"Scan: Semantic context aware network for accurate small object detection","volume":"11","author":"Guan","year":"2018","journal-title":"Int. J. Comput. Intell. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Tong, K., Wu, Y., and Zhou, F. (2020). Recent advances in small object detection based on deep learning: A review. Image Vis. Comput., 103910.","DOI":"10.1016\/j.imavis.2020.103910"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhang, W., Wang, S., Thachan, S., Chen, J., and Qian, Y. (2018, January 22\u201327). Deconv R-CNN for small object detection on remote sensing images. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8517436"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Yan, J., Wang, H., Yan, M., Diao, W., Sun, X., and Li, H. (2019). IoU-adaptive deformable R-CNN: Make full use of IoU for multi-class object detection in remote sensing imagery. Remote Sens., 11.","DOI":"10.3390\/rs11030286"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Liu, M., Wang, X., Zhou, A., Fu, X., Ma, Y., and Piao, C. (2020). UAV-YOLO: Small Object Detection on Unmanned Aerial Vehicle Perspective. Sensors, 20.","DOI":"10.3390\/s20082238"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Pham, M.T., Courtrai, L., Friguet, C., Lef\u00e8vre, S., and Baussard, A. (2020). YOLO-Fine: One-Stage Detector of Small Objects Under Various Backgrounds in Remote Sensing Images. Remote Sens., 12.","DOI":"10.3390\/rs12152501"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Froidevaux, A., Julier, A., Lifschitz, A., Pham, M.T., Dambreville, R., Lef\u00e8vre, S., and Lassalle, P. (2020, January 19\u201324). Vehicle detection and counting from VHR satellite images: Efforts and open issues. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Waikoloa, HI, USA.","DOI":"10.1109\/IGARSS39084.2020.9323827"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Shi, W., Caballero, J., Husz\u00e1r, F., Totz, J., Aitken, A.P., Bishop, R., Rueckert, D., and Wang, Z. (2016, January 27\u201330). Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.207"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","article-title":"Image super-resolution using deep convolutional networks","volume":"38","author":"Dong","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3275","DOI":"10.1109\/TNNLS.2018.2890550","article-title":"Separability and compactness network for image recognition and superresolution","volume":"30","author":"Zhou","year":"2019","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Kim, J., Kwon Lee, J., and Mu Lee, K. (2016, January 27\u201330). Accurate image super-resolution using very deep convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.182"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., and Mu Lee, K. (2017, January 21\u201326). Enhanced deep residual networks for single image super-resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPR-WS), Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.151"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., and Fu, Y. (2018, January 8\u201314). Image super-resolution using very deep residual channel attention networks. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Dai, T., Cai, J., Zhang, Y., Xia, S.T., and Zhang, L. (2019, January 16\u201320). Second-order attention network for single image super-resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01132"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ahn, N., Kang, B., and Sohn, K.A. (2018, January 8\u201314). Fast, accurate, and lightweight super-resolution with cascading residual network. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01249-6_16"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3106","DOI":"10.1109\/TMM.2019.2919431","article-title":"Deep learning for single image super-resolution: A brief review","volume":"21","author":"Yang","year":"2019","journal-title":"IEEE Trans. Multimed."},{"key":"ref_27","unstructured":"Anwar, S., Khan, S., and Barnes, N. (2019). A deep journey into super-resolution: A survey. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ferdous, S.N., Mostofa, M., and Nasrabadi, N.M. (2019, January 15\u201317). Super resolution-assisted deep aerial vehicle detection. Proceedings of the Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, Baltimore, MD, USA.","DOI":"10.1117\/12.2519045"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Shermeyer, J., and Van Etten, A. (2019, January 16\u201320). The effects of super-resolution on object detection performance in satellite imagery. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPR-WS), Long Beach, CA, USA.","DOI":"10.1109\/CVPRW.2019.00184"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Rabbi, J., Ray, N., Schubert, M., Chowdhury, S., and Chao, D. (2020). Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network. Remote Sens., 12.","DOI":"10.20944\/preprints202003.0313.v2"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Rottensteiner, F., Sohn, G., Jung, J., Gerke, M., Baillard, C., Benitez, S., and Breitkopf, U. (September, January 25). The ISPRS benchmark on urban object classification and 3D building reconstruction. Proceedings of the ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences I-3 (2012), Nr. 1, Melbourne, Australia.","DOI":"10.5194\/isprsannals-I-3-293-2012"},{"key":"ref_32","unstructured":"Lam, D., Kuzma, R., McGee, K., Dooley, S., Laielli, M., Klaric, M., Bulatov, Y., and McCord, B. (2018). xView: Objects in context in overhead imagery. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3633","DOI":"10.1109\/TGRS.2019.2959020","article-title":"Coupled Adversarial Training for Remote Sensing Image Super-Resolution","volume":"58","author":"Lei","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Audebert, N., Le Saux, B., and Lef\u00e8vre, S. (2017). Segment-before-Detect: Vehicle Detection and Classification through Semantic Segmentation of Aerial Images. Remote Sens., 9.","DOI":"10.3390\/rs9040368"},{"key":"ref_35","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ledig, C., Theis, L., Husz\u00e1r, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., and Wang, Z. (2017, January 21\u201326). Photo-realistic single image super-resolution using a generative adversarial network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.19"},{"key":"ref_37","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A.C. (2017, January 4\u20139). Improved training of wasserstein gans. Proceedings of the Advances in Neural Information Processing Systems (NIPS), Long Beach, CA, USA."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., and Efros, A.A. (2017, January 22\u201329). Unpaired image-to-image translation using cycle-consistent adversarial networks. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref_39","unstructured":"Yann, H. (2020, September 16). Pytorch-Retinanet. Available online: https:\/\/github.com\/yhenon\/pytorch-retinanet."},{"key":"ref_40","unstructured":"zylo117 (2020, September 16). Yet-Another-EfficientDet-Pytorch. Available online: https:\/\/github.com\/zylo117\/Yet-Another-EfficientDet-Pytorch."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/19\/3152\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:13:46Z","timestamp":1760177626000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/19\/3152"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,25]]},"references-count":40,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2020,10]]}},"alternative-id":["rs12193152"],"URL":"https:\/\/doi.org\/10.3390\/rs12193152","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,25]]}}}