{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T14:58:35Z","timestamp":1784905115765,"version":"3.55.0"},"reference-count":56,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,1,26]],"date-time":"2022-01-26T00:00:00Z","timestamp":1643155200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The intelligent detection of objects in remote sensing images has gradually become a research hotspot for experts from various countries, among which optical remote sensing images are considered to be the most important because of the rich feature information, such as the shape, texture and color, that they contain. Optical remote sensing image target detection is an important method for accomplishing tasks, such as land use, urban planning, traffic guidance, military monitoring and maritime rescue. In this paper, a multi stages feature pyramid network, namely the Multi-stage Feature Enhancement Pyramid Network (Multi-stage FEPN), is proposed, which can effectively solve the problems of blurring of small-scale targets and large scale variations of targets detected in optical remote sensing images. The Content-Aware Feature Up-Sampling (CAFUS) and Feature Enhancement Module (FEM) used in the network can perfectly solve the problem of fusion of adjacent-stages feature maps. Compared with several representative frameworks, the Multi-stage FEPN performs better in a range of common detection metrics, such as model accuracy and detection accuracy. The mAP reaches 0.9124, and the top-1 detection accuracy reaches 0.921 on NWPU VHR-10. The results demonstrate that Multi-stage FEPN provides a new solution for the intelligent detection of targets in optical remote sensing images.<\/jats:p>","DOI":"10.3390\/rs14030579","type":"journal-article","created":{"date-parts":[[2022,1,27]],"date-time":"2022-01-27T04:49:51Z","timestamp":1643258991000},"page":"579","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":53,"title":["Multi-Stage Feature Enhancement Pyramid Network for Detecting Objects in Optical Remote Sensing Images"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2884-833X","authenticated-orcid":false,"given":"Kaihua","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Ministry of Education, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haikuo","family":"Shen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Vehicle Advanced Manufacturing, Measuring and Control Technology, School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Ministry of Education, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.isprsjprs.2016.03.014","article-title":"A Survey on Object Detection in Optical Remote Sensing Images","volume":"117","author":"Cheng","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1016\/j.isprsjprs.2019.11.023","article-title":"Object detection in optical remote sensing images: A survey and a new benchmark","volume":"159","author":"Li","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","first-page":"1749\u22121768","article-title":"A Survey of Object Detection in Optical Remote Sensing Images","volume":"47","author":"Nie","year":"2021","journal-title":"Acta Autom. Sin."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4069","DOI":"10.1109\/JSTARS.2014.2308301","article-title":"Detection of Buildings in Multispectral Very High Spatial Resolution Images Using the Percentage Occupancy Hit-or-Miss Transform","volume":"7","author":"Stankov","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.isprsjprs.2015.01.013","article-title":"Water flow based geometric active deformable model for road network","volume":"102","author":"Leninisha","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.isprsjprs.2013.09.004","article-title":"Automated detection of buildings from single VHR multispectral images using shadow information and graph cuts","volume":"86","author":"Ok","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"6844","DOI":"10.1109\/TGRS.2014.2303895","article-title":"A Discriminative Metric Learning Based Anomaly Detection Method","volume":"52","author":"Bo","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1511","DOI":"10.1109\/JSTARS.2016.2620900","article-title":"Airport Detection and Aircraft Recognition Based on Two-Layer Saliency Model in High Spatial Resolution Remote-Sensing Images","volume":"10","author":"Zhang","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"17719\u221217734","DOI":"10.1007\/s11042-015-2960-3","article-title":"Change detection method for remote sensing images based on an improved Markov random field","volume":"76","author":"Gu","year":"2017","journal-title":"Multimed. Tools Appl."},{"key":"ref_10","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_11","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 (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"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). SSD: Single Shot MultiBox Detector. Computer Vision\u2014ECCV 2016, Proceedings of the 14th European Conference, Amsterdam, The Netherlands, 11\u201314 October 2016, Springer.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"642","DOI":"10.1007\/s11263-019-01204-1","article-title":"CornerNet: Detecting Objects as Paired Keypoints","volume":"128","author":"Law","year":"2020","journal-title":"Int. J. Comput. Vis."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Duan, K., Bai, S., Xie, L., Qi, H., Huang, Q., and Tian, Q. (November, January 27). CenterNet: Keypoint Triplets for Object Detection. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00667"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Tian, Z., Shen, C., Chen, H., and He, T. (November, January 27). FCOS: Fully Convolutional One-Stage Object Detection. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00972"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"He, Y., Xu, S., Gao, L., and Zhang, B. (2018, January 22\u201327). Ship Detection Without Sea-Land Segmentation for Large-Scale High-Resolution Optical Satellite Images. Proceedings of the IGARSS 2018\u20142018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8519391"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Shen, Y., Ji, R., Wang, Y., Chen, Z., Zheng, F., Huang, F., and Wu, Y. (2020). Enabling Deep Residual Networks for Weakly Supervised Object Detection. Computer Vision\u2014ECCV 2020, Proceedings of the 16th European Conference, Glasgow, UK, 23\u201328 August 2020, Springer.","DOI":"10.1007\/978-3-030-58598-3_8"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Qiu, H., Ma, Y., Li, Z., Liu, S., and Sun, J. (2020). BorderDet: Border Feature for Dense Object Detection. Computer Vision\u2014ECCV 2020, Proceedings of the 16th European Conference, Glasgow, UK, 23\u201328 August 2020, Springer.","DOI":"10.1007\/978-3-030-58452-8_32"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chen, Z.M., Jin, X., Zhao, B., Wei, X.S., and Guo, Y. (2020). Hierarchical Context Embedding for Region-based Object Detection. Computer Vision\u2014ECCV 2020, Proceedings of the 16th European Conference, Glasgow, UK, 23\u201328 August 2020, Springer.","DOI":"10.1007\/978-3-030-58589-1_38"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhang, K.H., and Shen, H.K. (2021). Solder Joint Defect Detection in the Connectors Using Improved Faster-RCNN Algorithm. Appl. Sci., 11.","DOI":"10.3390\/app11020576"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"7405","DOI":"10.1109\/TGRS.2016.2601622","article-title":"Learning Rotation-Invariant Convolutional Neural Networks for Object Detection in VHR Optical Remote Sensing Images","volume":"54","author":"Gong","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Han, X.B., Zhong, Y.F., and Zhang, L.P. (2017). An Efficient and Robust Integrated Geospatial Object Detection Framework for High Spatial Resolution Remote Sensing Imagery. Remote Sens., 9.","DOI":"10.3390\/rs9070666"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1470","DOI":"10.3390\/rs10091470","article-title":"Deformable Faster R-CNN with Aggregating Multi-Layer Features for Partially Occluded Object Detection in Optical Remote Sensing Images","volume":"10","author":"Yun","year":"2018","journal-title":"Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Xu, Z., Xu, X., Wang, L., Yang, R., and Pu, F. (2017). Deformable ConvNet with Aspect Ratio Constrained NMS for Object Detection in Remote Sensing Imagery. Remote Sens., 9.","DOI":"10.3390\/rs9121312"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2337","DOI":"10.1109\/TGRS.2017.2778300","article-title":"Rotation-Insensitive and Context-Augmented Object Detection in Remote Sensing Images","volume":"56","author":"Li","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Guo, W., Yang, W., Zhang, H., and Hua, G. (2018). Geospatial Object Detection in High Resolution Satellite Images Based on Multi-Scale Convolutional Neural Network. Remote Sens., 10.","DOI":"10.3390\/rs10010131"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Chen, S.Q., Zhan, R.H., and Zhang, J. (2018). Geospatial object detection in remote sensing imagery based on multiscale single-shot detector with activated semantics. Remote Sens., 10.","DOI":"10.3390\/rs10060820"},{"key":"ref_28","first-page":"3377\u22123390","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_29","doi-asserted-by":"crossref","unstructured":"Fu, Y., Wu, F., and Zhao, J. (2018, January 20\u201324). Context-Aware and Depthwise-based Detection on Orbit for Remote Sensing Image. Proceedings of the 2018 24th International Conference on Pattern Recognition (ICPR), Beijing, China.","DOI":"10.1109\/ICPR.2018.8545815"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1109\/LGRS.2018.2872355","article-title":"Multiscale Visual Attention Networks for Object Detection in VHR Remote Sensing Images","volume":"16","author":"Wang","year":"2019","journal-title":"IEEE Geoence Remote Sens. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5512","DOI":"10.1109\/TGRS.2019.2899955","article-title":"R2-CNN: Fast Tiny Object Detection in Large-Scale Remote Sensing Images","volume":"57","author":"Pang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens. A Publ. IEEE Geosci. Remote Sens. Soc."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"5146","DOI":"10.1109\/TGRS.2019.2897139","article-title":"ORSIm Detector: A Novel Object Detection Framework in Optical Remote Sensing Imagery Using Spatial-Frequency Channel Features","volume":"57","author":"Wu","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens. A Publ. IEEE Geosci. Remote Sens. Soc."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Dollar, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (July, January 21). Feature Pyramid Networks for Object Detection. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_34","first-page":"1083","article-title":"Review on Image Interpolation Techniques","volume":"31","author":"Zhong","year":"2016","journal-title":"J. Data Acquis. Process."},{"key":"ref_35","unstructured":"Dumoulin, V., and Visin, F. (2016). A guide to convolution arithmetic for deep learning. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Jo, Y., Oh, S.W., Kang, J., and Kim, S.J. (June, January 18). Deep Video Super-Resolution Network Using Dynamic Upsampling Filters Without Explicit Motion Compensation. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00340"},{"key":"ref_37","doi-asserted-by":"crossref","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 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00310"},{"key":"ref_38","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. (June, January 18). DOTA: A Large-Scale Dataset for Object Detection in Aerial Images. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00418"},{"key":"ref_39","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 IEEE International Conference on Image Processing, Quebec City, QC, Canada.","DOI":"10.1109\/ICIP.2015.7351502"},{"key":"ref_40","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_41","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1080\/01431161.2014.999881","article-title":"Elliptic Fourier transformation-based histograms of oriented gradients for rotationally invariant object detection in remote-sensing images","volume":"36","author":"Xiao","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2486","DOI":"10.1109\/TGRS.2016.2645610","article-title":"Accurate Object Localization in Remote Sensing Images Based on Convolutional Neural Networks","volume":"55","author":"Long","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Maggiori, E., Tarabalka, Y., Charpiat, G., and Alliez, P. (2017, January 23\u201328). Can Semantic Labeling Methods Generalize to Any City? The Inria Aerial Image Labeling Benchmark. Proceedings of the Igarss IEEE International Geoscience & Remote Sensing Symposium, Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127684"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"5535","DOI":"10.1109\/TGRS.2019.2900302","article-title":"Hierarchical and Robust Convolutional Neural Network for Very High-Resolution Remote Sensing Object Detection","volume":"57","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","first-page":"2048","article-title":"Cross-layer Attention Network for Small Object Detection in Remote Sensing Imagery","volume":"14","author":"Li","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_46","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_47","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.isprsjprs.2018.04.003","article-title":"Multi-scale object detection in remote sensing imagery with convolutional neural networks","volume":"145","author":"Deng","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"864","DOI":"10.1109\/LGRS.2018.2888887","article-title":"Scale adaptive proposal network for object detection in remote sensing images","volume":"16","author":"Zhang","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Pham, M.T., Courtrai, L., Friguet, C., Lef\u00e8vre, S., and Baussard, A. (2020). YOLO-Fine: One-stage detector of small objects under various backgrounds in remote sensing images. Remote Sens., 12.","DOI":"10.3390\/rs12152501"},{"key":"ref_50","first-page":"6004205","article-title":"An Automatic Cloud Detection Neural Network for High-Resolution Remote Sensing Imagery with Cloud\u2013Snow Coexistence","volume":"19","author":"Chen","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"112045","DOI":"10.1016\/j.rse.2020.112045","article-title":"Accurate cloud detection in high-resolution remote sensing imagery by weakly supervised deep learning","volume":"250","author":"Li","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"8447","DOI":"10.1109\/JSTARS.2021.3104726","article-title":"Dealing with Clouds and Seasonal Changes for Center Pivot Irrigation Systems Detection Using Instance Segmentation in Sentinel-2 Time Series","volume":"14","author":"Luiz","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"8350","DOI":"10.1109\/JSTARS.2021.3102218","article-title":"Multichannel Object Detection for Detecting Suspected Trees with Pine Wilt Disease Using Multispectral Drone Imagery","volume":"14","author":"Park","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Carvalho, O.L.F.D., de Carvalho J\u00fanior, O.A., Albuquerque, A.O.D., Bem, P.P.D., Silva, C.R., Ferreira, P.H.G., Moura, R.D.S.D., Gomes, R.A.T., Guimar\u00e3es, R.F., and Borges, D.L. (2021). Instance segmentation for large, multi-channel remote sensing imagery using Mask-RCNN and a Mosaicking approach. Remote Sens., 13.","DOI":"10.3390\/rs13010039"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"de Carvalho, O.L.F., de Moura, R.D.S., de Albuquerque, A.O., de Bem, P.P., Pereira, R.D.C., Weigang, L., Borges, D.L., Guimar\u00e3es, R.F., Gomes, R.A.T., and de Carvalho J\u00fanior, O.A. (2021). Instance Segmentation for Governmental Inspection of Small Touristic Infrastructure in Beach Zones Using Multispectral High-Resolution WorldView-3 Imagery. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10120813"},{"key":"ref_56","first-page":"101","article-title":"Salient object detection from multi-spectral remote sensing images with deep residual network","volume":"2","author":"Dai","year":"2019","journal-title":"J. Geod. Geoinf. Sci."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/3\/579\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:07:54Z","timestamp":1760134074000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/3\/579"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,26]]},"references-count":56,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["rs14030579"],"URL":"https:\/\/doi.org\/10.3390\/rs14030579","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,26]]}}}