{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T16:19:17Z","timestamp":1781713157814,"version":"3.54.5"},"reference-count":70,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,13]],"date-time":"2022-12-13T00:00:00Z","timestamp":1670889600000},"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":["61831012"],"award-info":[{"award-number":["61831012"]}],"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":["61401105"],"award-info":[{"award-number":["61401105"]}],"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":["SKLAO2022001A14"],"award-info":[{"award-number":["SKLAO2022001A14"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"State Key Laboratory of Applied Optics","award":["61831012"],"award-info":[{"award-number":["61831012"]}]},{"name":"State Key Laboratory of Applied Optics","award":["61401105"],"award-info":[{"award-number":["61401105"]}]},{"name":"State Key Laboratory of Applied Optics","award":["SKLAO2022001A14"],"award-info":[{"award-number":["SKLAO2022001A14"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In the remote sensing field, synthetic aperture radar (SAR) is a type of active microwave imaging sensor working in all-weather and all-day conditions, providing high-resolution SAR images of objects such as marine ships. Detection and instance segmentation of marine ships in SAR images has become an important question in remote sensing, but current deep learning models cannot accurately quantify marine ships because of the multi-scale property of marine ships in SAR images. In this paper, we propose a multi-scale feature pyramid network (MS-FPN) to achieve the simultaneous detection and instance segmentation of marine ships in SAR images. The proposed MS-FPN model uses a pyramid structure, and it is mainly composed of two proposed modules, namely the atrous convolutional pyramid (ACP) module and the multi-scale attention mechanism (MSAM) module. The ACP module is designed to extract both the shallow and deep feature maps, and these multi-scale feature maps are crucial for the description of multi-scale marine ships, especially the small ones. The MSAM module is designed to adaptively learn and select important feature maps obtained from different scales, leading to improved detection and segmentation accuracy. Quantitative comparison of the proposed MS-FPN model with several classical and recently developed deep learning models, using the high-resolution SAR images dataset (HRSID) that contains multi-scale marine ship SAR images, demonstrated the superior performance of MS-FPN over other models.<\/jats:p>","DOI":"10.3390\/rs14246312","type":"journal-article","created":{"date-parts":[[2022,12,14]],"date-time":"2022-12-14T02:54:21Z","timestamp":1670986461000},"page":"6312","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["A Multi-Scale Feature Pyramid Network for Detection and Instance Segmentation of Marine Ships in SAR Images"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5951-2527","authenticated-orcid":false,"given":"Zequn","family":"Sun","sequence":"first","affiliation":[{"name":"Institute of Modern Optics, Nankai University, Tianjin 300350, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunning","family":"Meng","sequence":"additional","affiliation":[{"name":"China Coast Guard Academy, Ningbo 315801, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5471-5085","authenticated-orcid":false,"given":"Jierong","family":"Cheng","sequence":"additional","affiliation":[{"name":"Institute of Modern Optics, Nankai University, Tianjin 300350, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2306-0340","authenticated-orcid":false,"given":"Zhiqing","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Modern Optics, Nankai University, Tianjin 300350, China"},{"name":"State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shengjiang","family":"Chang","sequence":"additional","affiliation":[{"name":"Institute of Modern Optics, Nankai University, Tianjin 300350, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1092","DOI":"10.1109\/TGRS.2010.2071879","article-title":"Ship Surveillance with TerraSAR-X","volume":"49","author":"Brusch","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1685","DOI":"10.1109\/TGRS.2008.2006504","article-title":"An Adaptive and Fast CFAR Algorithm Based on Automatic Censoring for Target Detection in High-Resolution SAR Images","volume":"47","author":"Gao","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1010","DOI":"10.1109\/36.508418","article-title":"An automatic ship and ship wake detection system for spaceborne SAR images in coastal regions","volume":"34","author":"Eldhuset","year":"1996","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1109\/LGRS.2005.845033","article-title":"A novel algorithm for ship detection in SAR imagery based on the wavelet transform","volume":"2","author":"Tello","year":"2005","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4585","DOI":"10.1109\/TGRS.2013.2282820","article-title":"An Improved Iterative Censoring Scheme for CFAR Ship Detection with SAR Imagery","volume":"52","author":"An","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhang, T., Zhang, X., and Ke, X. (2021). Quad-FPN: A Novel Quad Feature Pyramid Network for SAR Ship Detection. Remote Sens., 13.","DOI":"10.3390\/rs13142771"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4811","DOI":"10.1109\/TGRS.2017.2701813","article-title":"CFAR Ship Detection in Nonhomogeneous Sea Clutter Using Polarimetric SAR Data Based on the Notch Filter","volume":"55","author":"Gao","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Huo, W., Huang, Y., Pei, J., Zhang, Q., Gu, Q., and Yang, J. (2018). Ship Detection from Ocean SAR Image Based on Local Contrast Variance Weighted Information Entropy. Sensors, 18.","DOI":"10.3390\/s18041196"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Guo, H., Yang, X., Wang, N., and Gao, X. (2021). A CenterNet plus plus model for ship detection in SAR images. Pattern Recognit., 112.","DOI":"10.1016\/j.patcog.2020.107787"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhang, T., and Zhang, X. (2022). A polarization fusion network with geometric feature emb e dding for SAR ship classification. Pattern Recognit., 123.","DOI":"10.1016\/j.patcog.2021.108365"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"20881","DOI":"10.1109\/ACCESS.2018.2825376","article-title":"A Densely Connected End-to-End Neural Network for Multiscale and Multiscene SAI Ship Detection","volume":"6","author":"Jiao","year":"2018","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"120234","DOI":"10.1109\/ACCESS.2020.3005861","article-title":"HRSID: A High-Resolution SAR Images Dataset for Ship Detection and Instance Segmentation","volume":"8","author":"Wei","year":"2020","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Shi, H., Zhang, Q., Bian, M., Wang, H., Wang, Z., Chen, L., and Yang, J. (2018). A Novel Ship Detection Method Based on Gradient and Integral Feature for Single-Polarization Synthetic Aperture Radar Imagery. Sensors, 18.","DOI":"10.3390\/s18020563"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5394","DOI":"10.1109\/TGRS.2018.2815592","article-title":"Adaptive Ship Detection in Hybrid-Polarimetric SAR Images Based on the Power-Entropy Decomposition","volume":"56","author":"Gao","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1372","DOI":"10.1109\/LGRS.2018.2838043","article-title":"Ship Detection in SAR Images Based on Lognormal rho-Metric","volume":"15","author":"Yang","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"806","DOI":"10.1109\/LGRS.2012.2224317","article-title":"A CFAR Detection Algorithm for Generalized Gamma Distributed Background in High-Resolution SAR Images","volume":"10","author":"Qin","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3329","DOI":"10.1109\/JSTARS.2015.2417756","article-title":"Manifold Adaptation for Constant False Alarm Rate Ship Detection in South African Oceans","volume":"8","author":"Schwegmann","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1109\/LGRS.2017.2654450","article-title":"An Intensity-Space Domain CFAR Method for Ship Detection in HR SAR Images","volume":"14","author":"Wang","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1109\/JSTARS.2017.2755672","article-title":"OpenSARShip: A dataset dedicated to Sentinel-1 ship interpretation","volume":"11","author":"Huang","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2008","DOI":"10.1016\/j.neucom.2010.06.026","article-title":"Hebbian-based neural networks for bottom-up visual attention and its applications to ship detection in SAR images","volume":"74","author":"Yu","year":"2011","journal-title":"Neurocomputing"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Rostami, M., Kolouri, S., Eaton, E., and Kim, K. (2019). Deep Transfer Learning for Few-Shot SAR Image Classification. Remote Sens., 11.","DOI":"10.20944\/preprints201905.0030.v1"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Guo, Y., and Zhou, L. (2022). MEA-Net: A Lightweight SAR Ship Detection Model for Imbalanced Datasets. Remote Sens., 14.","DOI":"10.3390\/rs14184438"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Yu, W., Wang, Z., Li, J., Luo, Y., and Yu, Z. (2022). A Lightweight Network Based on One-Level Feature for Ship Detection in SAR Images. Remote Sens., 14.","DOI":"10.3390\/rs14143321"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature Pyramid Networks for Object Detection. Proceedings of the 30th IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Su, H., Wei, S., Liu, S., Liang, J., Wang, C., Shi, J., and Zhang, X. (2020). HQ-ISNet: High-Quality Instance Segmentation for Remote Sensing Imagery. Remote Sens., 12.","DOI":"10.3390\/rs12060989"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1007\/s41095-022-0271-y","article-title":"Attention mechanisms in computer vision: A survey","volume":"8","author":"Guo","year":"2022","journal-title":"Comput. Vis. Media"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., and Feng, J. (2021, January 19\u201325). Coordinate Attention for Efficient Mobile Network Design. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Electr Network, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., and Hu, Q. (2020). ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks. arXiv.","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"8983","DOI":"10.1109\/TGRS.2019.2923988","article-title":"Dense Attention Pyramid Networks for Multi-Scale Ship Detection in SAR Images","volume":"57","author":"Cui","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"751","DOI":"10.1109\/LGRS.2018.2882551","article-title":"Squeeze and Excitation Rank Faster R-CNN for Ship Detection in SAR Images","volume":"16","author":"Lin","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2738","DOI":"10.1109\/JSTARS.2020.2997081","article-title":"Attention Receptive Pyramid Network for Ship Detection in SAR Images","volume":"13","author":"Zhao","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wu, J., Pan, Z., Lei, B., and Hu, Y. (2021). LR-TSDet: Towards Tiny Ship Detection in Low-Resolution Remote Sensing Images. Remote Sens., 13.","DOI":"10.3390\/rs13193890"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"9325","DOI":"10.1109\/ACCESS.2020.2964540","article-title":"Attention Mask R-CNN for Ship Detection and Segmentation from Remote Sensing Images","volume":"8","author":"Nie","year":"2020","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Guo, L., Wang, Z., Yu, Y., Liu, X., and Xu, F. (2020). Intelligent Ship Detection in Remote Sensing Images Based on Multi-Layer Convolutional Feature Fusion. Remote Sens., 12.","DOI":"10.3390\/rs12203316"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zhou, Y., Wang, F., Wang, S., and Xu, Z. (2021). SDGH-Net: Ship Detection in Optical Remote Sensing Images Based on Gaussian Heatmap Regression. Remote Sens., 13.","DOI":"10.3390\/rs13030499"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Li, L., Zhou, Z., Wang, B., Miao, L., An, Z., and Xiao, X. (2021). Domain Adaptive Ship Detection in Optical Remote Sensing Images. Remote Sens., 13.","DOI":"10.3390\/rs13163168"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"11352","DOI":"10.1109\/JSTARS.2021.3123784","article-title":"Anchor-Free SAR Ship Instance Segmentation with Centroid-Distance Based Loss","volume":"14","author":"Gao","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","article-title":"Squeeze-and-Excitation Networks","volume":"42","author":"Hu","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Bell, S., Zitnick, C.L., Bala, K., and Girshick, R. (2016, January 27\u201330). Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR.2016.314"},{"key":"ref_40","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 31st IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00644"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Chen, H., Sun, K., Tian, Z., Shen, C., Huang, Y., and Yan, Y. (2020, January 13\u201319). Blendmask: Top-down meets bottom-up for instance segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00860"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Wang, X., Zhang, R., Shen, C., Kong, T., and Li, L. (2021). SOLO: A Simple Framework for Instance Segmentation. IEEE Trans. Pattern Anal. Mach. Intell., 44.","DOI":"10.1109\/TPAMI.2021.3111116"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Ke, X., Zhang, X., and Zhang, T. (2022). GCBANet: A Global Context Boundary-Aware Network for SAR Ship Instance Segmentation. Remote Sens., 14.","DOI":"10.3390\/rs14092165"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zhang, T., and Zhang, X. (2022). HTC plus for SAR Ship Instance Segmentation. Remote Sens., 14.","DOI":"10.3390\/rs14102395"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Zhang, T., Zhang, X., Li, J., and Shi, J. (2022, January 21\u201325). Contextual Squeeze-and-Excitation Mask R-CNN for SAR Ship Instance Segmentation. Proceedings of the IEEE Radar Conference (RadarConf), New York, NY, USA.","DOI":"10.1109\/RadarConf2248738.2022.9764228"},{"key":"ref_46","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":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_47","unstructured":"He, K., Girshick, R., and Doll\u00e1r, P. (November, January 27). Rethinking Imagenet Pre-Training. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_48","unstructured":"Wang, J., Chen, K., Xu, R., Liu, Z., Loy, C.C., and Lin, D. (November, January 27). CARAFE: Content-Aware ReAssembly of FEatures. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea."},{"key":"ref_49","unstructured":"Sun, K., Zhao, Y., Jiang, B., Cheng, T., Xiao, B., Liu, D., Mu, Y., Wang, X., Liu, W., and Wang, J. (2019). High-resolution representations for labeling pixels and regions. arXiv."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018, January 18\u201323). Path Aggregation Network for Instance Segmentation. Proceedings of the 31st IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"ref_51","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 15th European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1483","DOI":"10.1109\/TPAMI.2019.2956516","article-title":"Cascade R-CNN: High Quality Object Detection and Instance Segmentation","volume":"43","author":"Cai","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1109\/TPAMI.2018.2844175","article-title":"Mask R-CNN","volume":"42","author":"He","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Sun, K., Liang, Y., Ma, X., Huai, Y., and Xing, M. (2021). DSDet: A Lightweight Densely Connected Sparsely Activated Detector for Ship Target Detection in High-Resolution SAR Images. Remote Sens., 13.","DOI":"10.3390\/rs13142743"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Dollar, P., Tu, Z., and He, K. (2017, January 21\u201326). Aggregated Residual Transformations for Deep Neural Networks. Proceedings of the 30th IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Huang, Z., Huang, L., Gong, Y., Huang, C., and Wang, X. (2019, January 16\u201320). Mask Scoring R-CNN. Proceedings of the 32nd IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00657"},{"key":"ref_57","unstructured":"Bolya, D., Zhou, C., Xiao, F., and Lee, Y.J. (November, January 27). YOLACT Real-time Instance Segmentation. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Rossi, L., Karimi, A., and Prati, A. (2021, January 10\u201315). A Novel Region of Interest Extraction Layer for Instance Segmentation. Proceedings of the 25th International Conference on Pattern Recognition (ICPR), Electr Network, Milan, Italy.","DOI":"10.1109\/ICPR48806.2021.9412258"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Zhang, L., Wang, H., Wang, L., Pan, C., Huo, C., Liu, Q., and Wang, X. (2022). Filtered Convolution for Synthetic Aperture Radar Images Ship Detection. Remote Sens., 14.","DOI":"10.3390\/rs14205257"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Zhang, T., and Zhang, X. (2022). A Full-Level Context Squeeze-and-Excitation ROI Extractor for SAR Ship Instance Segmentation. IEEE Geosci. Remote Sens. Lett., 19.","DOI":"10.1109\/LGRS.2022.3166387"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Chen, K., Pang, J., Wang, J., Xiong, Y., Li, X., Sun, S., Feng, W., Liu, Z., Shi, J., and Ouyang, W. (2019, January 16\u201320). Hybrid Task Cascade for Instance Segmentation. Proceedings of the 32nd IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00511"},{"key":"ref_62","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An image is worth 16 \u00d7 16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Kim, B., Lee, J., Kang, J., Kim, E.-S., and Kim, H.J. (2021, January 19\u201325). HOTR: End-to-End Human-Object Interaction Detection with Transformers. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Electr Network, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00014"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.isprsjprs.2014.10.002","article-title":"Multi-class geospatial object detection and geographic image classification based on collection of part detectors","volume":"98","author":"Cheng","year":"2014","journal-title":"Isprs J. Photogramm. Remote Sens."},{"key":"ref_65","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\u201323). DOTA: A Large-scale Dataset for Object Detection in Aerial Images. Proceedings of the 31st IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00418"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Tuggener, L., Satyawan, Y.P., Pacha, A., Schmidhuber, J., and Stadelmann, T. (2021, January 10\u201315). The DeepScoresV2 Dataset and Benchmark for Music Object Detection. Proceedings of the 25th International Conference on Pattern Recognition (ICPR), Electr Network, Milan, Italy.","DOI":"10.1109\/ICPR48806.2021.9412290"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1109\/TMM.2019.2929005","article-title":"WiderPerson: A Diverse Dataset for Dense Pedestrian Detection in the Wild","volume":"22","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Multimed."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Lim, J.-S., Astrid, M., Yoon, H.-J., and Lee, S.-I. (2021, January 13\u201316). Small Object Detection using Context and Attention. Proceedings of the 3rd International Conference on Artificial Intelligence in Information and Communication (IEEE ICAIIC), Jeju Island, Republic of Korea.","DOI":"10.1109\/ICAIIC51459.2021.9415217"},{"key":"ref_69","unstructured":"Nie, J., Anwer, R.M., Cholakkal, H., Khan, F.S., Pang, Y., and Shao, L. (November, January 27). Enriched Feature Guided Refinement Network for Object Detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea."},{"key":"ref_70","first-page":"1","article-title":"P2T: Pyramid Pooling Transformer for Scene Understanding","volume":"2765","author":"Wu","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/24\/6312\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:40:34Z","timestamp":1760146834000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/24\/6312"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,13]]},"references-count":70,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["rs14246312"],"URL":"https:\/\/doi.org\/10.3390\/rs14246312","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,13]]}}}