{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T03:04:39Z","timestamp":1780542279988,"version":"3.54.1"},"reference-count":73,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2021,9,28]],"date-time":"2021-09-28T00:00:00Z","timestamp":1632787200000},"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>Recently, deep learning-based methods have made great improvements in object detection in remote sensing images (RSIs). However, detecting tiny objects in low-resolution images is still challenging. The features of these objects are not distinguishable enough due to their tiny size and confusing backgrounds and can be easily lost as the network deepens or downsamples. To address these issues, we propose an effective Tiny Ship Detector for Low-Resolution RSIs, abbreviated as LR-TSDet, consisting of three key components: a filtered feature aggregation (FFA) module, a hierarchical-atrous spatial pyramid (HASP) module, and an IoU-Joint loss. The FFA module captures long-range dependencies by calculating the similarity matrix so as to strengthen the responses of instances. The HASP module obtains deep semantic information while maintaining the resolution of feature maps by aggregating four parallel hierarchical-atrous convolution blocks of different dilation rates. The IoU-Joint loss is proposed to alleviate the inconsistency between classification and regression tasks, and guides the network to focus on samples that have both high localization accuracy and high confidence. Furthermore, we introduce a new dataset called GF1-LRSD collected from the Gaofen\u20131 satellite for tiny ship detection in low-resolution RSIs. The resolution of images is 16m and the mean size of objects is about 10.9 pixels, which are much smaller than public RSI datasets. Extensive experiments on GF1-LRSD and DOTA-Ship show that our method outperforms several competitors, proving its effectiveness and generality.<\/jats:p>","DOI":"10.3390\/rs13193890","type":"journal-article","created":{"date-parts":[[2021,9,28]],"date-time":"2021-09-28T21:39:29Z","timestamp":1632865169000},"page":"3890","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["LR-TSDet: Towards Tiny Ship Detection in Low-Resolution Remote Sensing Images"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6419-8982","authenticated-orcid":false,"given":"Jixiang","family":"Wu","sequence":"first","affiliation":[{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5041-3300","authenticated-orcid":false,"given":"Zongxu","family":"Pan","sequence":"additional","affiliation":[{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Lei","sequence":"additional","affiliation":[{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuxin","family":"Hu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,28]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","unstructured":"Xia, G.S., Bai, X., Ding, J., Zhu, Z., Belongie, S., Luo, J., Datcu, M., Pelillo, M., and Zhang, L. (2018, January 18\u201322). DOTA: A Large-Scale Dataset for Object Detection in Aerial Images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00418"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1016\/j.isprsjprs.2019.11.023","article-title":"Object detection in optical remote sensing images: A survey and a new benchmark","volume":"159","author":"Li","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Yang, X., Sun, H., Fu, K., Yang, J., Sun, X., Yan, M., and Guo, Z. (2018). Automatic ship detection in remote sensing images from google earth of complex scenes based on multiscale rotation dense feature pyramid networks. Remote Sens., 10.","DOI":"10.3390\/rs10010132"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1745","DOI":"10.1109\/LGRS.2018.2856921","article-title":"Toward arbitrary-oriented ship detection with rotated region proposal and discrimination networks","volume":"15","author":"Zhang","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"5327","DOI":"10.1016\/j.ijleo.2014.06.062","article-title":"Airplane detection based on rotation invariant and sparse coding in remote sensing images","volume":"125","author":"Liu","year":"2014","journal-title":"Optik"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Li, Y., Fu, K., Sun, H., and Sun, X. (2018). An aircraft detection framework based on reinforcement learning and convolutional neural networks in remote sensing images. Remote Sens., 10.","DOI":"10.3390\/rs10020243"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4895","DOI":"10.1109\/JSTARS.2015.2467377","article-title":"A hierarchical oil tank detector with deep surrounding features for high-resolution optical satellite imagery","volume":"8","author":"Zhang","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1347","DOI":"10.1109\/LGRS.2015.2401600","article-title":"Circular oil tank detection from panchromatic satellite images: A new automated approach","volume":"12","author":"Ok","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3965","DOI":"10.1109\/TGRS.2017.2685945","article-title":"AID: A benchmark data set for performance evaluation of aerial scene classification","volume":"55","author":"Xia","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.isprsjprs.2020.01.013","article-title":"ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data","volume":"162","author":"Diakogiannis","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","unstructured":"Van Etten, A. (2018). You only look twice: Rapid multi-scale object detection in satellite imagery. arXiv."},{"key":"ref_13","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_14","doi-asserted-by":"crossref","first-page":"7860","DOI":"10.1109\/TGRS.2019.2916953","article-title":"Tracking objects from satellite videos: A velocity feature based correlation filter","volume":"57","author":"Shao","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","first-page":"198","article-title":"A method for automatic detection of ships in harbor area in high-resolution remote sensing image","volume":"24","author":"Long","year":"2007","journal-title":"Comput. Simul."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2070","DOI":"10.1109\/LGRS.2014.2319082","article-title":"Automatic detection of inshore ships in high-resolution remote sensing images using robust invariant generalized Hough transform","volume":"11","author":"Xu","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_17","unstructured":"Dalal, N., and Triggs, B. (2005, January 20\u201326). Histograms of oriented gradients for human detection. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), San Diego, CA, USA."},{"key":"ref_18","unstructured":"Liao, S., Zhu, X., Lei, Z., Zhang, L., and Li, S.Z. (2007, January 27\u201329). Learning multi-scale block local binary patterns for face recognition. Proceedings of the International Conference on Biometrics, Seoul, Korea."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Lowe, D. (1999, January 20\u201327). Object recognition from local scale-invariant features. Proceedings of the Seventh IEEE International Conference on Computer Vision, Kerkyra, Greece.","DOI":"10.1109\/ICCV.1999.790410"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1109\/LGRS.2017.2664118","article-title":"Ship detection from optical satellite images based on saliency segmentation and structure-LBP feature","volume":"14","author":"Yang","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1006\/jcss.1997.1504","article-title":"A decision-theoretic generalization of on-line learning and an application to boosting","volume":"55","author":"Freund","year":"1997","journal-title":"J. Comput. Syst. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). ImageNet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","article-title":"The pascal visual object classes (VOC) challenge","volume":"88","author":"Everingham","year":"2010","journal-title":"Int. J. Comput. Vis."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. (2014, January 6\u201312). Microsoft coco: Common objects in context. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal Loss for Dense Object Detection. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_27","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_28","doi-asserted-by":"crossref","unstructured":"Liu, Z., Yuan, L., Weng, L., and Yang, Y. (2017, January 24\u201326). A High Resolution Optical Satellite Image Dataset for Ship Recognition and Some New Baselines. Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods\u2014ICPRAM, Porto, Portugal.","DOI":"10.5220\/0006120603240331"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ding, J., Xue, N., Long, Y., Xia, G.S., and Lu, Q. (2019, January 16\u201320). Learning roi transformer for oriented object detection in aerial images. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00296"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Yang, X., Yan, J., Feng, Z., and He, T. (2021, January 2\u20139). R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object. Proceedings of the AAAI Conference on Artificial Intelligence, Palo Alto, CA, USA.","DOI":"10.1609\/aaai.v35i4.16426"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wang, J., Yang, W., Guo, H., Zhang, R., and Xia, G.S. (2021, January 10\u201315). Tiny Object Detection in Aerial Images. Proceedings of the 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy.","DOI":"10.1109\/ICPR48806.2021.9413340"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Yang, X., Hou, L., Zhou, Y., Wang, W., and Yan, J. (2021, January 19\u201325). Dense Label Encoding for Boundary Discontinuity Free Rotation Detection. Proceedings of the IEEE Computer Vision and Pattern Recognition (CVPR), Online.","DOI":"10.1109\/CVPR46437.2021.01556"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature Pyramid Networks for Object Detection. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R., Gupta, A., and He, K. (2018, January 18\u201322). Non-local Neural Networks. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00813"},{"key":"ref_35","unstructured":"Yu, F., and Koltun, V. (2016, January 2\u20134). Multi-Scale Context Aggregation by Dilated Convolutions. Proceedings of the International Conference on Learning Representations (ICLR), San Juan, PR, USA."},{"key":"ref_36","unstructured":"Gao, S., Cheng, M.M., Zhao, K., Zhang, X.Y., Yang, M.H., and Torr, P.H. (2019). Res2net: A new multi-scale backbone architecture. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., and Yang, J. (2020). Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection. arXiv.","DOI":"10.1109\/CVPR46437.2021.01146"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016). SSD: Single Shot MultiBox Detector, ECCV.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_40","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Pang, J., Chen, K., Shi, J., Feng, H., Ouyang, W., and Lin, D. (2019, January 16\u201320). Libra r-cnn: Towards balanced learning for object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00091"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"10015","DOI":"10.1109\/TGRS.2019.2930982","article-title":"CAD-Net: A context-aware detection network for objects in remote sensing imagery","volume":"57","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"8333","DOI":"10.1109\/TGRS.2019.2920534","article-title":"DRBox-v2: An improved detector with rotatable boxes for target detection in SAR images","volume":"57","author":"An","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","unstructured":"Yang, X., Yan, J., Qi, M., Wang, W., Xiaopeng, Z., and Qi, T. (2021, January 18\u201324). Rethinking Rotated Object Detection with Gaussian Wasserstein Distance Loss. Proceedings of the International Conference on Machine Learning (ICML), Online."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Xu, Y., Fu, M., Wang, Q., Wang, Y., Chen, K., Xia, G.S., and Bai, X. (2020). Gliding vertex on the horizontal bounding box for multi-oriented object detection. arXiv.","DOI":"10.1109\/TPAMI.2020.2974745"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Han, J., Ding, J., Li, J., and Xia, G.S. (2021). Align deep features for oriented object detection. arXiv.","DOI":"10.1109\/TGRS.2021.3062048"},{"key":"ref_47","unstructured":"Tian, Z., Shen, C., Chen, H., and He, T. (November, January 27). FCOS: Fully Convolutional One-Stage Object Detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea."},{"key":"ref_48","unstructured":"Zhou, X., Wang, D., and Kr\u00e4henb\u00fchl, P. (2019). Objects as points. arXiv."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Law, H., and Deng, J. (2018, January 8\u201314). Cornernet: Detecting objects as paired keypoints. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01264-9_45"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Qiu, H., Ma, Y., Li, Z., Liu, S., and Sun, J. (2020, January 23\u201328). Borderdet: Border feature for dense object detection. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58452-8_32"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1016\/j.isprsjprs.2020.09.022","article-title":"Oriented objects as pairs of middle lines","volume":"169","author":"Wei","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_52","unstructured":"Lin, Y., Feng, P., Guan, J., Wang, W., and Chambers, J. (2019). IENet: Interacting embranchment one stage anchor free detector for orientation aerial object detection. arXiv."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Xiao, Z., Qian, L., Shao, W., Tan, X., and Wang, K. (2020). Axis Learning for Orientated Objects Detection in Aerial Images. Remote Sens., 12.","DOI":"10.3390\/rs12060908"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Yang, Y., Pan, Z., Hu, Y., and Ding, C. (2021). CPS-Det: An Anchor-Free Based Rotation Detector for Ship Detection. Remote Sens., 13.","DOI":"10.3390\/rs13112208"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Kisantal, M., Wojna, Z., Murawski, J., Naruniec, J., and Cho, K. (2019). Augmentation for small object detection. arXiv.","DOI":"10.5121\/csit.2019.91713"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Singh, B., and Davis, L.S. (2018, January 18\u201322). An analysis of scale invariance in object detection snip. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00377"},{"key":"ref_57","unstructured":"Noh, J., Bae, W., Lee, W., Seo, J., and Kim, G. (November, January 27). Better to follow, follow to be better: Towards precise supervision of feature super-resolution for small object detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Yu, X., Gong, Y., Jiang, N., Ye, Q., and Han, Z. (2020, January 1\u20135). Scale match for tiny person detection. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, The Westin Snowmass Resort, Snowmass Village, CO, USA.","DOI":"10.1109\/WACV45572.2020.9093394"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Yang, X., Yang, J., Yan, J., Zhang, Y., Zhang, T., Guo, Z., Sun, X., and Fu, K. (2019, January 27\u201328). Scrdet: Towards more robust detection for small, cluttered and rotated objects. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00832"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Hu, P., and Ramanan, D. (2017, January 21\u201326). Finding Tiny Faces. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.166"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_62","unstructured":"Tan, M., and Le, Q. (2019, January 9\u201315). Efficientnet: Rethinking model scaling for convolutional neural networks. Proceedings of the International Conference on Machine Learning, Long Beach, CA, USA."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021). Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. arXiv.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_65","unstructured":"Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R. (2017). Attention is All you Need. Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Wu, Y., and He, K. (2018, January 8\u201314). Group normalization. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01261-8_1"},{"key":"ref_67","unstructured":"Nair, V., and Hinton, G.E. (2010). Rectified Linear Units Improve Restricted Boltzmann Machines, Icml."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Ma, N., Zhang, X., Zheng, H.T., and Sun, J. (2018, January 8\u201314). Shufflenet v2: Practical guidelines for efficient cnn architecture design. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., and Savarese, S. (2019, January 16\u201320). Generalized Intersection Over Union: A Metric and a Loss for Bounding Box Regression. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00075"},{"key":"ref_70","unstructured":"Tzutalin, D. (2015, October 05). LabelImg. GitHub Repository. Available online: https:\/\/github.com\/tzutalin\/labelImg."},{"key":"ref_71","unstructured":"Chen, K., Wang, J., Pang, J., Cao, Y., Xiong, Y., Li, X., Sun, S., Feng, W., Liu, Z., and Xu, J. (2019). MMDetection: Open MMLab Detection Toolbox and Benchmark. arXiv."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Wang, P., Chen, P., Yuan, Y., Liu, D., Huang, Z., Hou, X., and Cottrell, G. (2018, January 12\u201315). Understanding convolution for semantic segmentation. Proceedings of the 2018 IEEE winter conference on applications of computer vision (WACV), Lake Tahoe, NV, USA.","DOI":"10.1109\/WACV.2018.00163"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Zhang, S., Chi, C., Yao, Y., Lei, Z., and Li, S.Z. (2020). Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection. arXiv.","DOI":"10.1109\/CVPR42600.2020.00978"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/19\/3890\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:06:38Z","timestamp":1760166398000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/19\/3890"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,28]]},"references-count":73,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2021,10]]}},"alternative-id":["rs13193890"],"URL":"https:\/\/doi.org\/10.3390\/rs13193890","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,28]]}}}