{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T14:40:58Z","timestamp":1781880058261,"version":"3.54.5"},"reference-count":66,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,6,5]],"date-time":"2024-06-05T00:00:00Z","timestamp":1717545600000},"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":["62371084"],"award-info":[{"award-number":["62371084"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["CSTB2022NSCQ-MSX1418"],"award-info":[{"award-number":["CSTB2022NSCQ-MSX1418"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2022MD723727"],"award-info":[{"award-number":["2022MD723727"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2022CQBSHTB2041"],"award-info":[{"award-number":["2022CQBSHTB2041"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["E011A2022330"],"award-info":[{"award-number":["E011A2022330"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Chongqing, China","award":["62371084"],"award-info":[{"award-number":["62371084"]}]},{"name":"Natural Science Foundation of Chongqing, China","award":["CSTB2022NSCQ-MSX1418"],"award-info":[{"award-number":["CSTB2022NSCQ-MSX1418"]}]},{"name":"Natural Science Foundation of Chongqing, China","award":["2022MD723727"],"award-info":[{"award-number":["2022MD723727"]}]},{"name":"Natural Science Foundation of Chongqing, China","award":["2022CQBSHTB2041"],"award-info":[{"award-number":["2022CQBSHTB2041"]}]},{"name":"Natural Science Foundation of Chongqing, China","award":["E011A2022330"],"award-info":[{"award-number":["E011A2022330"]}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["62371084"],"award-info":[{"award-number":["62371084"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["CSTB2022NSCQ-MSX1418"],"award-info":[{"award-number":["CSTB2022NSCQ-MSX1418"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2022MD723727"],"award-info":[{"award-number":["2022MD723727"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2022CQBSHTB2041"],"award-info":[{"award-number":["2022CQBSHTB2041"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["E011A2022330"],"award-info":[{"award-number":["E011A2022330"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Special Support for Chongqing Postdoctoral Research Project","award":["62371084"],"award-info":[{"award-number":["62371084"]}]},{"name":"Special Support for Chongqing Postdoctoral Research Project","award":["CSTB2022NSCQ-MSX1418"],"award-info":[{"award-number":["CSTB2022NSCQ-MSX1418"]}]},{"name":"Special Support for Chongqing Postdoctoral Research Project","award":["2022MD723727"],"award-info":[{"award-number":["2022MD723727"]}]},{"name":"Special Support for Chongqing Postdoctoral Research Project","award":["2022CQBSHTB2041"],"award-info":[{"award-number":["2022CQBSHTB2041"]}]},{"name":"Special Support for Chongqing Postdoctoral Research Project","award":["E011A2022330"],"award-info":[{"award-number":["E011A2022330"]}]},{"name":"Funding of Institute for Advanced Sciences of Chongqing University of Posts and Telecommunications","award":["62371084"],"award-info":[{"award-number":["62371084"]}]},{"name":"Funding of Institute for Advanced Sciences of Chongqing University of Posts and Telecommunications","award":["CSTB2022NSCQ-MSX1418"],"award-info":[{"award-number":["CSTB2022NSCQ-MSX1418"]}]},{"name":"Funding of Institute for Advanced Sciences of Chongqing University of Posts and Telecommunications","award":["2022MD723727"],"award-info":[{"award-number":["2022MD723727"]}]},{"name":"Funding of Institute for Advanced Sciences of Chongqing University of Posts and Telecommunications","award":["2022CQBSHTB2041"],"award-info":[{"award-number":["2022CQBSHTB2041"]}]},{"name":"Funding of Institute for Advanced Sciences of Chongqing University of Posts and Telecommunications","award":["E011A2022330"],"award-info":[{"award-number":["E011A2022330"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Crater detection can provide valuable information for humans to explore the topography and understand the history of extraterrestrial planets. Due to the significantly varying scenario distributions, existing detection models trained on known labelled crater datasets are hardly effective when applied to new unlabelled planets. To address this issue, we propose a two-stage adaptive network (TAN) for semi-supervised cross-domain crater detection. Our network is built on the YOLOv5 detector, where a series of strategies are employed to enhance its cross-domain generalisation ability. In the first stage, we propose an attention-based scale-adaptive fusion (ASAF) strategy to handle objects with significant scale variances. Furthermore, we propose a smoothing hard example mining (SHEM) loss function to address the issue of overfitting on hard examples. In the second stage, we propose a sort-based pseudo-labelling fine-tuning (SPF) strategy for semi-supervised learning to mitigate the distributional differences between source and target domains. For both stages, we employ weak or strong image augmentation to suit different cross-domain tasks. Experimental results on benchmark datasets demonstrate that the proposed network can enhance domain adaptation ability for crater detection under varying scenario distributions.<\/jats:p>","DOI":"10.3390\/rs16112024","type":"journal-article","created":{"date-parts":[[2024,6,5]],"date-time":"2024-06-05T03:49:28Z","timestamp":1717559368000},"page":"2024","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Two-Stage Adaptive Network for Semi-Supervised Cross-Domain Crater Detection under Varying Scenario Distributions"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-8750-7882","authenticated-orcid":false,"given":"Yifan","family":"Liu","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1264-2812","authenticated-orcid":false,"given":"Tiecheng","family":"Song","sequence":"additional","affiliation":[{"name":"School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengye","family":"Xian","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruiyuan","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-7811-2649","authenticated-orcid":false,"given":"Yi","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Li","sequence":"additional","affiliation":[{"name":"International College of Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tan","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,5]]},"reference":[{"key":"ref_1","first-page":"6460","article-title":"Towards Accurate and Robust Domain Adaptation Under Multiple Noisy Environments","volume":"45","author":"Han","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Xu, M., Qin, L., Chen, W., Pu, S., and Zhang, L. (2023, January 18\u201322). Multi-view Adversarial Discriminator: Mine the Non-causal Factors for Object Detection in Unseen Domains. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00783"},{"key":"ref_3","unstructured":"Qin, R., Zhang, G., and Tang, Y. (2023). On the Transferability of Learning Models for Semantic Segmentation for Remote Sensing Data. arXiv."},{"key":"ref_4","unstructured":"W1 (2024, March 24). Lunar_crater Dataset. Available online: https:\/\/universe.roboflow.com\/w1-lnwdz\/lunar_crater."},{"key":"ref_5","first-page":"1","article-title":"Progressive Domain Adaptive Network for Crater Detection","volume":"60","author":"Yang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3681","DOI":"10.1109\/TGRS.2018.2806371","article-title":"Lunar Crater Detection Based on Terrain Analysis and Mathematical Morphology Methods Using Digital Elevation Models","volume":"56","author":"Chen","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","first-page":"1","article-title":"High-Resolution Feature Pyramid Network for Automatic Crater Detection on Mars","volume":"60","author":"Yang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","first-page":"1","article-title":"CraterDANet: A Convolutional Neural Network for Small-Scale Crater Detection via Synthetic-to-Real Domain Adaptation","volume":"60","author":"Yang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Hsu, C.Y., Li, W., and Wang, S. (2021). Knowledge-Driven GeoAI: Integrating Spatial Knowledge into Multi-Scale Deep Learning for Mars Crater Detection. Remote Sens., 13.","DOI":"10.3390\/rs13112116"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Hua, W., Liang, D., Li, J., Liu, X., Zou, Z., Ye, X., and Bai, X. (2023, January 18\u201322). SOOD: Towards Semi-Supervised Oriented Object Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.01493"},{"key":"ref_11","unstructured":"Sohn, K., Berthelot, D., Li, C.L., Zhang, Z., Carlini, N., Cubuk, E.D., Kurakin, A., Zhang, H., and Raffel, C. (2020, January 6\u201312). FixMatch: Simplifying semi-supervised learning with consistency and confidence. Proceedings of the 34th International Conference on Neural Information Processing Systems, Red Hook, NY, USA."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zheng, M., You, S., Huang, L., Wang, F., Qian, C., and Xu, C. (2022, January 18\u201324). SimMatch: Semi-supervised Learning with Similarity Matching. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01407"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Tang, Z., Sun, Y., Liu, S., and Yang, Y. (2023, January 18\u201322). DETR with Additional Global Aggregation for Cross-domain Weakly Supervised Object Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.01099"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Yu, H., Zhang, Z., Qin, Z., Wu, H., Li, D., Zhao, J., and Lu, X. (2018, January 8\u201313). Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors. Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil.","DOI":"10.1109\/IJCNN.2018.8489071"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Lin, C., Wang, S., Xu, D., Lu, Y., and Zhang, W. (2020, January 7\u201312). Object Instance Mining for Weakly Supervised Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i07.6813"},{"key":"ref_16","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_17","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 IEEE Conference on Computer Vision and Pattern Recognition, (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"8085","DOI":"10.1109\/JSTARS.2022.3206399","article-title":"YOLOv5-Tassel: Detecting Tassels in RGB UAV Imagery With Improved YOLOv5 Based on Transfer Learning","volume":"15","author":"Liu","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1734","DOI":"10.1109\/JSTARS.2023.3339235","article-title":"Small Object Detection Algorithm Based on Improved YOLOv8 for Remote Sensing","volume":"17","author":"Yi","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_20","unstructured":"Liu, S., Huang, D., and Wang, Y. (2019). Learning Spatial Fusion for Single-Shot Object Detection. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Shen, F., Peng, X., Wang, L., Hao, X., Shu, M., and Wang, Y. (2022, January 18\u201322). HSGM: A Hierarchical Similarity Graph Module for Object Re-Identification. Proceedings of the 2022 IEEE International Conference on Multimedia and Expo (ICME), Taipei, Taiwan.","DOI":"10.1109\/ICME52920.2022.9859883"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018, January 18\u201322). Path Aggregation Network for Instance Segmentation. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Qiao, S., Chen, L.C., and Yuille, A. (2021, January 19\u201325). DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Virtual.","DOI":"10.1109\/CVPR46437.2021.01008"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs","volume":"40","author":"Chen","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_25","unstructured":"Li, Y., Chen, Y., Wang, N., and Zhang, Z.X. (November, January 27). Scale-Aware Trident Networks for Object Detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Woo, S., 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 (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhu, L., Wang, X., Ke, Z., Zhang, W., and Lau, R.W. (2023, January 18\u201322). BiFormer: Vision Transformer with Bi-Level Routing Attention. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00995"},{"key":"ref_28","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_29","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, (WACV), Snowmass Village, CO, USA.","DOI":"10.1109\/WACV45572.2020.9093394"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Kennerley, M., Wang, J.G., Veeravalli, B., and Tan, R.T. (2023, January 18\u201322). 2PCNet: Two-Phase Consistency Training for Day-to-Night Unsupervised Domain Adaptive Object Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.01105"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2944","DOI":"10.1109\/JSTARS.2019.2918302","article-title":"Segmentation Convolutional Neural Networks for Automatic Crater Detection on Mars","volume":"12","author":"DeLatte","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Tian, K., Zhang, C., Wang, Y., Xiang, S., and Pan, C. (2021, January 11\u201317). Knowledge Mining and Transferring for Domain Adaptive Object Detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, (ICCV), Virtual.","DOI":"10.1109\/ICCV48922.2021.00900"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Li, Y.J., Dai, X., Ma, C.Y., Liu, Y.C., Chen, K., Wu, B., He, Z., Kitani, K., and Vajda, P. (2022, January 18\u201324). Cross-Domain Adaptive Teacher for Object Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00743"},{"key":"ref_34","unstructured":"Zhang, L., Zhou, W., Fan, H., Luo, T., and Ling, H. (2023). Robust Domain Adaptive Object Detection with Unified Multi-Granularity Alignment. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wu, J., Chen, J., He, M., Wang, Y., Li, B., Ma, B., Gan, W., Wu, W., Wang, Y., and Huang, D. (2022, January 18\u201324). Target-Relevant Knowledge Preservation for Multi-Source Domain Adaptive Object Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00523"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1029\/2018JE005592","article-title":"A New Global Database of Lunar Impact Craters >1\u20132 km: 1. Crater Locations and Sizes, Comparisons With Published Databases, and Global Analysis","volume":"124","author":"Robbins","year":"2019","journal-title":"J. Geophys. Res. Planets"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3554729","article-title":"Diffusion Models: A Comprehensive Survey of Methods and Applications","volume":"56","author":"Yang","year":"2023","journal-title":"ACM Comput. Surv."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Li, T., Chang, H., Mishra, S., Zhang, H., Katabi, D., and Krishnan, D. (2023, January 18\u201322). MAGE: MAsked Generative Encoder to Unify Representation Learning and Image Synthesis. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00213"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Li, Y., Liu, H., Wu, Q., Mu, F., Yang, J., Gao, J., Li, C., and Lee, Y.J. (2023, January 18\u201322). GLIGEN: Open-Set Grounded Text-to-Image Generation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.02156"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Koksal, A., Tuzcuoglu, O., Ince, K.G., Ataseven, Y., and Alatan, A.A. (2022, January 16\u201319). Improved Hard Example Mining Approach for Single Shot Object Detectors. Proceedings of the 2022 IEEE International Conference on Image Processing (ICIP), Bordeaux, France.","DOI":"10.1109\/ICIP46576.2022.9897806"},{"key":"ref_41","unstructured":"Shrivastava, A., Gupta, A., and Girshick, R. (July, January 26). Training Region-Based Object Detectors with Online Hard Example Mining. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA."},{"key":"ref_42","unstructured":"Jocher, G., and Stoken, A. (2021). ultralytics\/yolov5: V6.0\u2014YOLOv5n \u2018Nano\u2019 models, Roboflow integration, TensorFlow export, OpenCV DNN support. Zenodo."},{"key":"ref_43","unstructured":"Liu, Y., Shao, Z., Teng, Y., and Hoffmann, N. (2021). NAM: Normalization-based Attention Module. arXiv."},{"key":"ref_44","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2021, January 3\u20137). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. Proceedings of the 9th International Conference on Learning Representations (ICLR), Virtual."},{"key":"ref_45","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 IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_46","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_47","doi-asserted-by":"crossref","unstructured":"Hu, J., Huang, Z., Shen, F., He, D., and Xian, Q. (2023, January 16\u201321). A Bag of Tricks for Fine-Grained Roof Extraction. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Pasadena, CA, USA.","DOI":"10.1109\/IGARSS52108.2023.10283210"},{"key":"ref_48","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_49","doi-asserted-by":"crossref","first-page":"1376","DOI":"10.1007\/s11263-021-01434-2","article-title":"Towards Balanced Learning for Instance Recognition","volume":"129","author":"Pang","year":"2021","journal-title":"Int. J. Comput. Vis."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2023, January 18\u201322). YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Chen, Y., Li, W., Sakaridis, C., Dai, D., and Van Gool, L. (2018, January 18\u201322). Domain Adaptive Faster R-CNN for Object Detection in the Wild. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00352"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Hsu, C.C., Tsai, Y.H., Lin, Y.Y., and Yang, M.H. (2020, January 23\u201328). Every Pixel Matters: Center-aware Feature Alignment for Domain Adaptive Object Detector. Proceedings of the 16th European Conference, Glasgow, UK.","DOI":"10.1007\/978-3-030-58545-7_42"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Chen, Y., Cao, Y., Hu, H., and Wang, L. (2020, January 13\u201319). Memory Enhanced Global-Local Aggregation for Video Object Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01035"},{"key":"ref_54","unstructured":"Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., and Wei, Y. (2001, January 7\u201314). Deformable Convolutional Networks. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Vancouver, BC, Canada."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Wang, J., Zhang, W., Cao, Y., Chen, K., Pang, J., Gong, T., Shi, J., Loy, C.C., and Lin, D. (2020, January 23\u201328). Side-Aware Boundary Localization for More Precise Object Detection. Proceedings of the European Conference Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58548-8_24"},{"key":"ref_56","unstructured":"Zhu, X., Su, W., Lu, L., Li, B., Wang, X., and Dai, J. (2020). Deformable DETR: Deformable Transformers for End-to-End Object Detection. arXiv."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Wang, W., Xie, E., Li, X., Fan, D.P., Song, K., Liang, D., Lu, T., Luo, P., and Shao, L. (2021, January 11\u201317). Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions. Proceedings of the IEEE\/CVF International Conference on Computer Vision, (ICCV), Virtual.","DOI":"10.1109\/ICCV48922.2021.00061"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Zhang, S., Chi, C., Yao, Y., Lei, Z., and Li, S.Z. (2020, January 13\u201319). Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00978"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201322). Squeeze-and-Excitation Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., and Hu, Q. (2020, January 14\u201319). ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (ICCV), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"ref_61","unstructured":"Liu, Y., Shao, Z., and Hoffmann, N. (2021). Global Attention Mechanism: Retain Information to Enhance Channel-Spatial Interactions. arXiv."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Liu, Z., Mao, H., Wu, C.Y., Feichtenhofer, C., Darrell, T., and Xie, S. (2022, January 18\u201324). A ConvNet for the 2020s. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., and Xu, C. (2020, January 14\u201319). GhostNet: More Features From Cheap Operations. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (ICCV), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00165"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1109\/TPAMI.2019.2938758","article-title":"Res2Net: A New Multi-Scale Backbone Architecture","volume":"43","author":"Gao","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_65","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Mark Liao, H.Y., Wu, Y.H., Chen, P.Y., Hsieh, J.W., and Yeh, I.H. (2020, January 14\u201319). CSPNet: A New Backbone that can Enhance Learning Capability of CNN. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00203"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/11\/2024\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:53:57Z","timestamp":1760108037000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/11\/2024"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,5]]},"references-count":66,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["rs16112024"],"URL":"https:\/\/doi.org\/10.3390\/rs16112024","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,5]]}}}