{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T21:13:45Z","timestamp":1780434825325,"version":"3.54.1"},"reference-count":67,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2023,7,30]],"date-time":"2023-07-30T00:00:00Z","timestamp":1690675200000},"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 (NSFC)","doi-asserted-by":"publisher","award":["62171040"],"award-info":[{"award-number":["62171040"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Recently, the improvement of detection performance always relies on deeper convolutional layers and complex convolutional structures in remote sensing images, which significantly increases the storage space and computational complexity of the detector. Although previous work has designed various novel lightweight convolutions, when these convolutional structures are applied to remote sensing detection tasks, the inconsistency between features and targets as well as between features and tasks in the detection architecture is often ignored: (1) The features extracted by convolution sliding in a fixed direction make it difficult to effectively model targets with arbitrary direction distribution, which leads to the detector needing more parameters to encode direction information and the network parameters being highly redundant; (2) The detector shares features from the backbone, but the classification task requires rotation-invariant features while the regression task requires rotation-sensitive features. This inconsistency in the task can lead to inefficient convolutional structures. Therefore, this paper proposed a detector that uses the Feature Decoupling for Lightweight Oriented Object Detection (FDLO-Det). Specifically, we constructed a rotational separable convolution that extracts rotational equivariant features while significantly compressing network parameters and computational complexity through highly shared parameters. Next, we introduced an orthogonal polarization transformation module that decomposes rotational equivariant features in both horizontal and vertical orthogonal directions, and used polarization functions to filter out the required features for classification and regression tasks, effectively improving detector performance. Extensive experiments on DOTA, HRSC2016, and UCAS-AOD show that the proposed detector can achieve the best performance and achieve an effective balance between computational complexity and detection accuracy.<\/jats:p>","DOI":"10.3390\/rs15153801","type":"journal-article","created":{"date-parts":[[2023,7,31]],"date-time":"2023-07-31T01:48:50Z","timestamp":1690768130000},"page":"3801","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Towards Feature Decoupling for Lightweight Oriented Object Detection in Remote Sensing Images"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3747-5128","authenticated-orcid":false,"given":"Chenwei","family":"Deng","sequence":"first","affiliation":[{"name":"Chongqing lnnovation Center, Beijing Institute of Technology, Chongqing 401135, China"},{"name":"School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3021-5371","authenticated-orcid":false,"given":"Donglin","family":"Jing","sequence":"additional","affiliation":[{"name":"School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7905-0163","authenticated-orcid":false,"given":"Yuqi","family":"Han","sequence":"additional","affiliation":[{"name":"School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiyuan","family":"Deng","sequence":"additional","affiliation":[{"name":"School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Astronautics, Beihang University, Beijing 100191, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,30]]},"reference":[{"key":"ref_1","first-page":"2272492","article-title":"A new method on inshore ship detection in high-resolution satellite images using shape and context information","volume":"11","author":"Liu","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2664118","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_3","doi-asserted-by":"crossref","first-page":"2878958","DOI":"10.1109\/TIP.2018.2878958","article-title":"An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing","volume":"28","author":"Hong","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_4","first-page":"3130716","article-title":"SpectralFormer: Rethinking Hyperspectral Image Classification with Transformers","volume":"60","author":"Hong","year":"2021","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zhao, B., Zhao, B., Tang, L., Han, Y., and Wang, W. (2018). Deep Spatial-Temporal Joint Feature Representation for Video Object Detection. Sensors, 18.","DOI":"10.3390\/s18030774"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Tang, L., Tang, W., Qu, X., Han, Y., Wang, W., and Zhao, B. (2022). A scale-aware pyramid network for multi-scale object detection in SAR images. Remote Sens., 14.","DOI":"10.3390\/rs14040973"},{"key":"ref_7","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016). Computer Vision\u2014ECCV 2016\u201414th European Conference, Amsterdam, The Netherlands, 11\u201314 October 2016, Springer. Lecture Notes in Computer Science."},{"key":"ref_8","unstructured":"Wang, K., Liew, J.H., Zou, Y., Zhou, D., and Feng, J. (November, January 27). PANet: Few-shot image semantic segmentation with prototype alignment. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Cai, Z., and Vasconcelos, N. (2018, January 18\u201323). Cascade R-CNN: Delving Into High Quality Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00644"},{"key":"ref_11","first-page":"1","article-title":"R-FCN: Object Detection via Region-based Fully Convolutional Networks","volume":"29","author":"Dai","year":"2016","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Zhu, X., Wang, X., Yang, S., Li, W., Wang, H., Fu, P., and Luo, Z. (2017). R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection. arXiv.","DOI":"10.1109\/ICPR.2018.8545598"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2384","DOI":"10.1109\/TPAMI.2022.3166956","article-title":"SCRDet++: Detecting Small, Cluttered and Rotated Objects via Instance-Level Feature Denoising and Rotation Loss Smoothing","volume":"45","author":"Yang","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"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","first-page":"1452","DOI":"10.1109\/TPAMI.2020.2974745","article-title":"Gliding Vertex on the Horizontal Bounding Box for Multi-Oriented Object Detection","volume":"43","author":"Xu","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2577031","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":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18\u201322). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Howard, A., Sandler, M., Chu, G., Chen, L.C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., and Vasudevan, V. (2019\u20132, January 27). Searching for mobilenetv3. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea.","DOI":"10.1109\/ICCV.2019.00140"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., and Sun, J. (2018\u201323, January 18). Shufflenet: An extremely efficient convolutional neural network for mobile devices. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00716"},{"key":"ref_20","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 Computer Vision\u2014ECCV 2018: 15th European Conference, Munich, Germany.","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., and Xu, C. (2020, January 13\u201319). GhostNet: More Features From Cheap Operations. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00165"},{"key":"ref_22","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv."},{"key":"ref_23","unstructured":"Li, Z., Peng, C., Yu, G., Zhang, X., Deng, Y., and Sun, J. (2017). Light-Head R-CNN: In Defense of Two-Stage Object Detector. arXiv."},{"key":"ref_24","first-page":"3062048","article-title":"Align Deep Features for Oriented Object Detection","volume":"60","author":"Han","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wang, J., Chen, K., Yang, S., Loy, C.C., and Lin, D. (2019, January 15\u201320). Region proposal by guided anchoring. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00308"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Jiang, B., Luo, R., Mao, J., Xiao, T., and Jiang, Y. (2018, January 8\u201314). Acquisition of Localization Confidence for Accurate Object Detection. Proceedings of the ECCV 2018, Munich, Germany.","DOI":"10.1007\/978-3-030-01264-9_48"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wu, Y., Chen, Y., Yuan, L., Liu, Z., Wang, L., Li, H., and Fu, Y. (2020, January 13\u201319). Rethinking Classification and Localization for Object Detection. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01020"},{"key":"ref_28","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 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.89"},{"key":"ref_29","unstructured":"Chen, Y., Han, C., Wang, N., and Zhang, Z. (2019). Revisiting Feature Alignment for One-stage Object Detection. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Cheng, G., Zhou, P., and Han, J. (2016\u20131, January 26). RIFD-CNN: Rotation-Invariant and Fisher Discriminative Convolutional Neural Networks for Object Detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.315"},{"key":"ref_31","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_32","doi-asserted-by":"crossref","unstructured":"Yang, X., Yang, J., Yan, J., Zhang, Y., Zhang, T., Guo, Z., Sun, X., and Fu, K. (2019\u20132, January 27). SCRDet: Towards more robust detection for small, cluttered and rotated objects. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea.","DOI":"10.1109\/ICCV.2019.00832"},{"key":"ref_33","first-page":"4335","article-title":"Detecting Rotated Objects as Gaussian Distributions and its 3-D Generalization","volume":"45","author":"Yang","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_34","unstructured":"Lin, Y., Feng, P., and Guan, J. (2019). IENet: Interacting Embranchment One Stage Anchor Free Detector for Orientation Aerial Object Detection. arXiv."},{"key":"ref_35","first-page":"195","article-title":"PIoU Loss: Towards Accurate Oriented Object Detection in Complex Environments","volume":"12350","author":"Chen","year":"2020","journal-title":"Comput. Vis. ECCV"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R.B., Doll\u00e1r, P., Tu, Z., and He, K. (2017, January 21\u201326). Aggregated Residual Transformations for Deep Neural Networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Dang, J., and Yang, J. (2021, January 6\u201311). HIGCNN: Hierarchical Interleaved Group Convolutional Neural Networks for Point Clouds Analysis. Proceedings of the ICASSP 2021\u20142021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, ON, Canada.","DOI":"10.1109\/ICASSP39728.2021.9413879"},{"key":"ref_39","unstructured":"Yu, F., and Koltun, V. (2016, January 2\u20134). Multi-Scale Context Aggregation by Dilated Convolutions. Proceedings of the 4th International Conference on Learning Representations, ICLR, San Juan, Puerto Rico."},{"key":"ref_40","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_41","first-page":"776","article-title":"WeightNet: Revisiting the Design Space of Weight Networks","volume":"Volume 12360","author":"Ma","year":"2020","journal-title":"Computer Vision\u2014ECCV 2020, 16th European Conference, Glasgow, UK, 23\u201328 August 2020, Proceedings, Part XV"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Li, Y., Chen, Y., Dai, X., Chen, D., Liu, M., Yuan, L., Liu, Z., Zhang, L., and Vasconcelos, N. (2021, January 10\u201317). MicroNet: Improving Image Recognition with Extremely Low FLOPs. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision, ICCV, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00052"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Tang, Q., Li, J., Shi, Z., and Hu, Y. (2020, January 4\u20138). Lightdet: A Lightweight and Accurate Object Detection Network. Proceedings of the 2020 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2020, Barcelona, Spain.","DOI":"10.1109\/ICASSP40776.2020.9054101"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Qin, Z., Li, Z., Zhang, Z., Bao, Y., Yu, G., Peng, Y., and Sun, J. (2019\u20132, January 27). ThunderNet: Towards Real-Time Generic Object Detection on Mobile Devices. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision, ICCV, Seoul, Republic of Korea.","DOI":"10.1109\/ICCV.2019.00682"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.isprsjprs.2018.05.005","article-title":"A light and faster regional convolutional neural network for object detection in optical remote sensing images","volume":"141","author":"Ding","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"4324","DOI":"10.1109\/TGRS.2020.3008993","article-title":"Ship Detection in Spaceborne Infrared Image Based on Lightweight CNN and Multisource Feature Cascade Decision","volume":"59","author":"Wang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_47","first-page":"5612414","article-title":"Arbitrary-oriented ship detection through center-head point extraction","volume":"60","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_48","unstructured":"Yang, X., Yan, J., Feng, Z., and He, T. (2019). R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object. arXiv."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"3111","DOI":"10.1109\/TMM.2018.2818020","article-title":"Arbitrary-oriented scene text detection via rotation proposals","volume":"20","author":"Ma","year":"2018","journal-title":"IEEE Trans. Multimed."},{"key":"ref_50","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 ICPRAM, Porto, Portugal.","DOI":"10.5220\/0006120603240331"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Zhu, H., Chen, X., Dai, W., Fu, K., Ye, Q., and Jiao, J. (2015, January 27\u201330). Orientation robust object detection in aerial images using deep convolutional neural network. Proceedings of the 2015 IEEE International Conference on Image Processing, ICIP, Quebec City, QC, Canada.","DOI":"10.1109\/ICIP.2015.7351502"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Xia, G., Bai, X., Ding, J., Zhu, Z., Belongie, S.J., Luo, J., Datcu, M., Pelillo, M., and Zhang, L. (2018, January 18\u201323). DOTA: A Large-Scale Dataset for Object Detection in Aerial Images. Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00418"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Ding, J., Xue, N., Long, Y., Xia, G.S., and Lu, Q. (2019, January 15\u201320). Learning roi transformer for oriented object detection in aerial images. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00296"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Wang, J., Ding, J., Guo, H., Cheng, W., Pan, T., and Yang, W. (2019). Mask OBB: A Semantic Attention-Based Mask Oriented Bounding Box Representation for Multi-Category Object Detection in Aerial Images. Remote Sens., 11.","DOI":"10.3390\/rs11242930"},{"key":"ref_55","unstructured":"Qian, W., Yang, X., Peng, S., Guo, Y., and Yan, J. (2019). Learning Modulated Loss for Rotated Object Detection. arXiv."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","article-title":"Focal Loss for Dense Object Detection","volume":"42","author":"Lin","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Yi, J., Wu, P., Liu, B., Huang, Q., Qu, H., and Metaxas, D. (2021, January 3\u20138). Oriented object detection in aerial images with box boundary-aware vectors. Proceedings of the 2021 IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA.","DOI":"10.1109\/WACV48630.2021.00220"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Yang, X., and Yan, J. (2022). On the Arbitrary-Oriented Object Detection: Classification based Approaches Revisited. arXiv.","DOI":"10.1007\/s11263-022-01593-w"},{"key":"ref_59","first-page":"5601920","article-title":"A Novel Nonlocal-Aware Pyramid and Multiscale Multitask Refinement Detector for Object Detection in Remote Sensing Images","volume":"60","author":"Huang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1895","DOI":"10.1109\/TIP.2022.3148874","article-title":"A General Gaussian Heatmap Label Assignment for Arbitrary-Oriented Object Detection","volume":"31","author":"Huang","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"8021505","DOI":"10.1109\/LGRS.2021.3115110","article-title":"Optimization for Arbitrary-Oriented Object Detection via Representation Invariance Loss","volume":"19","author":"Ming","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Ming, Q., Miao, L., Zhou, Z., and Dong, Y. (2021). CFC-Net: A Critical Feature Capturing Network for Arbitrary-Oriented Object Detection in Remote Sensing Images. arXiv.","DOI":"10.1109\/TGRS.2021.3095186"},{"key":"ref_63","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_64","doi-asserted-by":"crossref","unstructured":"Liao, M., Zhu, Z., Shi, B., Xia, G.S., and Bai, X. (2018, January 18\u201323). Rotation-Sensitive Regression for Oriented Scene Text Detection. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00619"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1100","DOI":"10.1109\/LGRS.2018.2822760","article-title":"Center-Point-Guided Proposal Generation for Detection of Small and Dense Buildings in Aerial Imagery","volume":"15","author":"Shu","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_66","first-page":"5605414","article-title":"AR2Det: An Accurate and Real-Time Rotational One-Stage Ship Detector in Remote Sensing Images","volume":"60","author":"Yang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"5623616","DOI":"10.1109\/TGRS.2022.3173610","article-title":"Ship Detection in High-Resolution Optical Remote Sensing Images Aided by Saliency Information","volume":"60","author":"Ren","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/15\/3801\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:22:45Z","timestamp":1760127765000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/15\/3801"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,30]]},"references-count":67,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["rs15153801"],"URL":"https:\/\/doi.org\/10.3390\/rs15153801","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,30]]}}}