{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T16:13:31Z","timestamp":1780676011356,"version":"3.54.1"},"reference-count":63,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2023,8,10]],"date-time":"2023-08-10T00:00:00Z","timestamp":1691625600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shenzhen Science and Technology Program","award":["ZDSYS20210623091808026"],"award-info":[{"award-number":["ZDSYS20210623091808026"]}]},{"name":"Shenzhen Science and Technology Program","award":["2022YFE0209300"],"award-info":[{"award-number":["2022YFE0209300"]}]},{"name":"National Key Research and Development Program of China","award":["ZDSYS20210623091808026"],"award-info":[{"award-number":["ZDSYS20210623091808026"]}]},{"name":"National Key Research and Development Program of China","award":["2022YFE0209300"],"award-info":[{"award-number":["2022YFE0209300"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Multi-class geospatial object detection in high-resolution remote sensing images has significant potential in various domains such as industrial production, military warning, disaster monitoring, and urban planning. However, the traditional process of remote sensing object detection involves several time-consuming steps, including image acquisition, image download, ground processing, and object detection. These steps may not be suitable for tasks with shorter timeliness requirements, such as military warning and disaster monitoring. Additionally, the transmission of massive data from satellites to the ground is limited by bandwidth, resulting in time delays and redundant information, such as cloud coverage images. To address these challenges and achieve efficient utilization of information, this paper proposes a comprehensive on-board multi-class geospatial object detection scheme. The proposed scheme consists of several steps. Firstly, the satellite imagery is sliced, and the PID-Net (Proportional-Integral-Derivative Network) method is employed to detect and filter out cloud-covered tiles. Subsequently, our Manhattan Intersection over Union (MIOU) loss-based YOLO (You Only Look Once) v7-Tiny method is used to detect remote-sensing objects in the remaining tiles. Finally, the detection results are mapped back to the original image, and the truncated NMS (Non-Maximum Suppression) method is utilized to filter out repeated and noisy boxes. To validate the reliability of the scheme, this paper creates a new dataset called DOTA-CD (Dataset for Object Detection in Aerial Images-Cloud Detection). Experiments were conducted on both ground and on-board equipment using the AIR-CD dataset, DOTA dataset, and DOTA-CD dataset. The results demonstrate the effectiveness of our method.<\/jats:p>","DOI":"10.3390\/rs15163963","type":"journal-article","created":{"date-parts":[[2023,8,10]],"date-time":"2023-08-10T10:24:47Z","timestamp":1691663087000},"page":"3963","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["On-Board Multi-Class Geospatial Object Detection Based on Convolutional Neural Network for High Resolution Remote Sensing Images"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0731-8401","authenticated-orcid":false,"given":"Yanyun","family":"Shen","sequence":"first","affiliation":[{"name":"School of Aeronautics and Astronautics, Shenzhen Campus of Sun Yat-sen University, No. 66, Gongchang Road, Guangming District, Shenzhen 518107, China"},{"name":"Shenzhen Key Laboratory of Intelligent Microsatellite Constellation, Shenzhen Campus of Sun Yat-sen University, No. 66, Gongchang Road, Guangming District, Shenzhen 518107, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7510-5646","authenticated-orcid":false,"given":"Di","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Aeronautics and Astronautics, Shenzhen Campus of Sun Yat-sen University, No. 66, Gongchang Road, Guangming District, Shenzhen 518107, China"},{"name":"Shenzhen Key Laboratory of Intelligent Microsatellite Constellation, Shenzhen Campus of Sun Yat-sen University, No. 66, Gongchang Road, Guangming District, Shenzhen 518107, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junyi","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Aeronautics and Astronautics, Shenzhen Campus of Sun Yat-sen University, No. 66, Gongchang Road, Guangming District, Shenzhen 518107, China"},{"name":"Shenzhen Key Laboratory of Intelligent Microsatellite Constellation, Shenzhen Campus of Sun Yat-sen University, No. 66, Gongchang Road, Guangming District, Shenzhen 518107, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhipan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Aeronautics and Astronautics, Shenzhen Campus of Sun Yat-sen University, No. 66, Gongchang Road, Guangming District, Shenzhen 518107, China"},{"name":"Shenzhen Key Laboratory of Intelligent Microsatellite Constellation, Shenzhen Campus of Sun Yat-sen University, No. 66, Gongchang Road, Guangming District, Shenzhen 518107, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhe","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Aeronautics and Astronautics, Shenzhen Campus of Sun Yat-sen University, No. 66, Gongchang Road, Guangming District, Shenzhen 518107, China"},{"name":"Shenzhen Key Laboratory of Intelligent Microsatellite Constellation, Shenzhen Campus of Sun Yat-sen University, No. 66, Gongchang Road, Guangming District, Shenzhen 518107, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingling","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Aeronautics and Astronautics, Shenzhen Campus of Sun Yat-sen University, No. 66, Gongchang Road, Guangming District, Shenzhen 518107, China"},{"name":"Shenzhen Key Laboratory of Intelligent Microsatellite Constellation, Shenzhen Campus of Sun Yat-sen University, No. 66, Gongchang Road, Guangming District, Shenzhen 518107, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_3","first-page":"91","article-title":"Faster r-cnn: Towards real-time object detection with region proposal networks","volume":"28","author":"Ren","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_4","unstructured":"Dai, J., Li, Y., He, K., and Sun, J. (2016, January 5\u201310). R-fcn: Object detection via region-based fully convolutional networks. Proceedings of the Advances in Neural Information Processing Systems, Barcelona, Spain."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_6","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 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, faster, stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_8","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_9","unstructured":"Bochkovskiy, A., Wang, C.-Y., and Liao, H.-Y.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_10","unstructured":"Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., and Nie, W. (2022). YOLOv6: A single-stage object detection framework for industrial applications. arXiv."},{"key":"ref_11","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, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A.C. (2016, January 8\u201316). Ssd: Single shot multibox detector. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_13","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_14","unstructured":"Yang, X., Yan, J., Yang, X., Tang, J., Liao, W., and He, T. (2020). Scrdet++: Detecting small, cluttered and rotated objects via instance-level feature denoising and rotation loss smoothing. arXiv."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3513133","article-title":"Towards accurate oriented object detection in aerial images with adaptive multi-level feature fusion","volume":"19","author":"Zhen","year":"2023","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yu, Y., and Da, F. (2023, January 18\u201322). Phase-shifting coder: Predicting accurate orientation in oriented object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.01283"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1495","DOI":"10.1007\/s11063-021-10679-4","article-title":"Multiview objects recognition using deep learning-based wrap-CNN with voting scheme","volume":"54","author":"Balamurugan","year":"2022","journal-title":"Neural Process. Lett."},{"key":"ref_18","unstructured":"Van Etten, A. (2018). You only look twice: Rapid multi-scale object detection in satellite imagery. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.isprsjprs.2020.05.016","article-title":"HyperLi-Net: A hyper-light deep learning network for high-accurate and high-speed ship detection from synthetic aperture radar imagery","volume":"167","author":"Zhang","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","first-page":"1","article-title":"ASSD: Feature Aligned Single-Shot Detection for Multiscale Objects in Aerial Imagery","volume":"60","author":"Xu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","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 IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00418"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1995","DOI":"10.3390\/rs13101995","article-title":"On-board real-time ship detection in HISEA-1 SAR images based on CFAR and lightweight deep learning","volume":"13","author":"Xu","year":"2021","journal-title":"Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1018","DOI":"10.3390\/rs14041018","article-title":"Lite-yolov5: A lightweight deep learning detector for on-board ship detection in large-scene sentinel-1 sar images","volume":"14","author":"Xu","year":"2022","journal-title":"Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3479","DOI":"10.3390\/rs13173479","article-title":"On-board volcanic eruption detection through cnns and satellite multispectral imagery","volume":"13","author":"Sebastianelli","year":"2021","journal-title":"Remote Sens."},{"key":"ref_25","first-page":"1","article-title":"SOCNet: A Lightweight and Fine-Grained Object Recognition Network for Satellite On-Orbit Computing","volume":"60","author":"Pang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","first-page":"789","article-title":"A new generation of intelligent mapping and remote sensing scientific test satellite Luojia-3 01","volume":"51","author":"Deren","year":"2022","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Rossow, W.B., Lacis, A.A., Oinas, V., and Mishchenko, M.I. (2004). Calculation of radiative fluxes from the surface to top of atmosphere based on ISCCP and other global data sets: Refinements of the radiative transfer model and the input data. J. Geophys. Res. Atmos., 109.","DOI":"10.1029\/2003JD004457"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.rse.2019.03.039","article-title":"A cloud detection algorithm for satellite imagery based on deep learning","volume":"229","author":"Jeppesen","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"107890","DOI":"10.1016\/j.knosys.2021.107890","article-title":"GCDB-UNet: A novel robust cloud detection approach for remote sensing images","volume":"238","author":"Li","year":"2022","journal-title":"Knowl. Based Syst."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Pu, W., Wang, Z., Liu, D., and Zhang, Q. (2022). Optical remote sensing image cloud detection with self-attention and spatial pyramid pooling fusion. Remote Sens., 14.","DOI":"10.3390\/rs14174312"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Xu, J., Xiong, Z., and Bhattacharyya, S.P. (2023, January 18\u201322). PIDNet: A Real-Time Semantic Segmentation Network Inspired by PID Controllers. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.01871"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.neucom.2022.05.052","article-title":"Manhattan-distance IOU loss for fast and accurate bounding box regression and object detection","volume":"500","author":"Shen","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/j.isprsjprs.2022.07.019","article-title":"Fast and accurate multi-class geospatial object detection with large-size remote sensing imagery using CNN and Truncated NMS","volume":"191","author":"Shen","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_34","first-page":"1","article-title":"DABNet: Deformable contextual and boundary-weighted network for cloud detection in remote sensing images","volume":"60","author":"He","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3051","DOI":"10.1007\/s11263-021-01515-2","article-title":"Bisenet v2: Bilateral network with guided aggregation for real-time semantic segmentation","volume":"129","author":"Yu","year":"2021","journal-title":"Int. J. Comput. Vis."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Li, X., You, A., Zhu, Z., Zhao, H., Yang, M., Yang, K., Tan, S., and Tong, Y. (2020, January 23\u201328). Semantic flow for fast and accurate scene parsing. Proceedings of the Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK.","DOI":"10.1007\/978-3-030-58452-8_45"},{"key":"ref_37","unstructured":"Hong, Y., Pan, H., Sun, W., and Jia, Y. (2021). Deep dual-resolution networks for real-time and accurate semantic segmentation of road scenes. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Deng, R., Shen, C., Liu, S., Wang, H., and Liu, X. (2018, January 8\u201314). Learning to predict crisp boundaries. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01231-1_35"},{"key":"ref_39","unstructured":"Takikawa, T., Acuna, D., Jampani, V., and Fidler, S. (November, January 27). Gated-scnn: Gated shape cnns for semantic segmentation. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Lee, Y., Hwang, J.-w., Lee, S., Bae, Y., and Park, J. (2019, January 16\u201317). An energy and GPU-computation efficient backbone network for real-time object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Long Beach, CA, USA.","DOI":"10.1109\/CVPRW.2019.00103"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Liao, H.-Y.M., 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, Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00203"},{"key":"ref_42","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 IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"ref_43","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, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","article-title":"Spatial pyramid pooling in deep convolutional networks for visual recognition","volume":"37","author":"He","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., and Savarese, S. (2019, January 15\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, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00075"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Neubeck, A., and Van Gool, L. (2006, January 20\u201324). Efficient non-maximum suppression. Proceedings of the 18th International Conference on Pattern Recognition (ICPR\u201906), Hong Kong, China.","DOI":"10.1109\/ICPR.2006.479"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., and Schiele, B. (2016, January 27\u201330). The cityscapes dataset for semantic urban scene understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.350"},{"key":"ref_48","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":"2009","journal-title":"Int. J. Comput. Vis."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Wang, H., Xie, S., Lin, L., Iwamoto, Y., Han, X.-H., Chen, Y.-W., and Tong, R. (2022, January 22\u201327). Mixed transformer u-net for medical image segmentation. Proceedings of the ICASSP 2022\u20142022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Singapore.","DOI":"10.1109\/ICASSP43922.2022.9746172"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.isprsjprs.2019.02.017","article-title":"Deep learning based cloud detection for medium and high resolution remote sensing images of different sensors","volume":"150","author":"Li","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"6195","DOI":"10.1109\/TGRS.2019.2904868","article-title":"CDnet: CNN-based cloud detection for remote sensing imagery","volume":"57","author":"Yang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_53","unstructured":"Yang, X., Liu, Q., Yan, J., Li, A., Zhang, Z., and Yu, G. (2019). R3det: Refined single-stage detector with feature refinement for rotating object. arXiv."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Azimi, S.M., Vig, E., Bahmanyar, R., K\u00f6rner, M., and Reinartz, P. (2018, January 2\u20136). Towards multi-class object detection in unconstrained remote sensing imagery. Proceedings of the Asian Conference on Computer Vision, Perth, Australia.","DOI":"10.1007\/978-3-030-20893-6_10"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Yan, J., Wang, H., Yan, M., Diao, W., Sun, X., and Li, H. (2019). IoU-Adaptive Deformable R-CNN: Make Full Use of IoU for Multi-Class Object Detection in Remote Sensing Imagery. Remote Sens., 11.","DOI":"10.3390\/rs11030286"},{"key":"ref_56","unstructured":"Yang, X., Yang, J., Yan, J., Zhang, Y., Zhang, T., Guo, Z., Sun, X., and Fu, K. (November, January 27). Scrdet: Towards more robust detection for small, cluttered and rotated objects. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Li, C., Xu, C., Cui, Z., Wang, D., Zhang, T., and Yang, J. (2019, January 22\u201325). Feature-attentioned object detection in remote sensing imagery. Proceedings of the 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan.","DOI":"10.1109\/ICIP.2019.8803521"},{"key":"ref_58","unstructured":"Sun, P., Chen, G., Luke, G., and Shang, Y. (2018). Salience biased loss for object detection in aerial images. arXiv."},{"key":"ref_59","unstructured":"Fu, C.-Y., Liu, W., Ranga, A., Tyagi, A., and Berg, A.C. (2017). Dssd: Deconvolutional single shot detector. arXiv."},{"key":"ref_60","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_61","doi-asserted-by":"crossref","unstructured":"Fu, K., Chen, Z., Zhang, Y., and Sun, X. (2019). Enhanced Feature Representation in Detection for Optical Remote Sensing Images. Remote Sens., 11.","DOI":"10.3390\/rs11182095"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Kegelmeyer, W. (1994). Extraction of Cloud Statistics from Whole Sky Imaging Cameras, Sandia National Lab. (SNL-CA).","DOI":"10.2172\/10141846"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.neucom.2014.09.102","article-title":"A cloud image detection method based on SVM vector machine","volume":"169","author":"Li","year":"2015","journal-title":"Neurocomputing"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/16\/3963\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:30:27Z","timestamp":1760128227000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/16\/3963"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,10]]},"references-count":63,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["rs15163963"],"URL":"https:\/\/doi.org\/10.3390\/rs15163963","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,10]]}}}