{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T15:30:55Z","timestamp":1760369455545,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2018,3,15]],"date-time":"2018-03-15T00:00:00Z","timestamp":1521072000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Program for New Century Excellent Talents in the University of China","award":["NCET-11-0866"],"award-info":[{"award-number":["NCET-11-0866"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41601487"],"award-info":[{"award-number":["41601487"]}],"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>Partially occluded object detection (POOD) has been an important task for both civil and military applications that use high-resolution remote sensing images (HR-RSIs). This topic is very challenging due to the limited object evidence for detection. Recent partial configuration model (PCM) based methods deal with occlusion yet suffer from the problems of massive manual annotation, separate parameter learning, and low training and detection efficiency. To tackle this, a unified PCM framework (UniPCM) is proposed in this paper. The proposed UniPCM adopts a part sharing mechanism which directly shares the root and part filters of a deformable part-based model (DPM) among different partial configurations. It largely reduces the convolution overhead during both training and detection. In UniPCM, a novel DPM deformation deviation method is proposed for spatial interrelationship estimation of PCM, and a unified weights learning method is presented to simultaneously obtain the weights of elements within each partial configuration and the weights between partial configurations. Experiments on three HR-RSI datasets show that the proposed UniPCM method achieves a much higher training and detection efficiency for POOD compared with state-of-the-art PCM-based methods, while maintaining a comparable detection accuracy. UniPCM obtains a training speedup of maximal 10\u00d7 and 2.5\u00d7 for airplane and ship, and a detection speedup of maximal 7.2\u00d7, 4.1\u00d7 and 2.5\u00d7 on three test sets, respectively.<\/jats:p>","DOI":"10.3390\/rs10030464","type":"journal-article","created":{"date-parts":[[2018,3,15]],"date-time":"2018-03-15T16:07:17Z","timestamp":1521130037000},"page":"464","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Unified Partial Configuration Model Framework for Fast Partially Occluded Object Detection in High-Resolution Remote Sensing Images"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4018-5826","authenticated-orcid":false,"given":"Shaohua","family":"Qiu","sequence":"first","affiliation":[{"name":"Science and Technology on Automatic Target Recognition Laboratory, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gongjian","family":"Wen","sequence":"additional","affiliation":[{"name":"Science and Technology on Automatic Target Recognition Laboratory, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jia","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Meteorology and Oceanology, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8582-2040","authenticated-orcid":false,"given":"Zhipeng","family":"Deng","sequence":"additional","affiliation":[{"name":"College of Electronic Science, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaxiang","family":"Fan","sequence":"additional","affiliation":[{"name":"Science and Technology on Automatic Target Recognition Laboratory, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,3,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Han, X., Zhong, Y., and Zhang, L. (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_2","doi-asserted-by":"crossref","first-page":"1797","DOI":"10.1109\/LGRS.2014.2309695","article-title":"Vehicle detection in satellite images by hybrid deep convolutional neural networks","volume":"11","author":"Chen","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Cai, B., Jiang, Z., Zhang, H., Zhao, D., and Yao, Y. (2017). Airport detection using end-to-end convolutional neural network with hard example mining. Remote Sens., 9.","DOI":"10.3390\/rs9111198"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2037","DOI":"10.1109\/LGRS.2017.2749478","article-title":"Object detection using convolutional neural networks in a coarse-to-fine manner","volume":"14","author":"Li","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1109\/LGRS.2016.2542358","article-title":"Convolutional neural network based automatic object detection on aerial images","volume":"13","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_6","first-page":"1","article-title":"Toward fast and accurate vehicle detection in aerial images using coupled region-based convolutional neural networks","volume":"PP","author":"Deng","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_7","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":"Cheng","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","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_9","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1080\/2150704X.2016.1258127","article-title":"Vehicle detection in remote sensing images using denoizing-based convolutional neural networks","volume":"8","author":"Li","year":"2017","journal-title":"Remote Sens. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Tang, T., Zhou, S., Deng, Z., Lei, L., and Zou, H. (2017). Arbitrary-oriented vehicle detection in aerial imagery with single convolutional neural networks. Remote Sens., 9.","DOI":"10.3390\/rs9111170"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., and Wei, Y. (arXiv, 2017). Deformable convolutional networks, arXiv.","DOI":"10.1109\/ICCV.2017.89"},{"key":"ref_12","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). Imagenet Classification with Deep Convolutional Neural Networks. Proceedings of the NIPS\u201912, 25th International Conference on Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1730","DOI":"10.1109\/LGRS.2017.2731863","article-title":"Automatic and fast pcm generation for occluded object detection in high-resolution remote sensing images","volume":"14","author":"Qiu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1109\/LGRS.2013.2246538","article-title":"Object detection in high-resolution remote sensing images using rotation invariant parts based model","volume":"11","author":"Zhang","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1109\/TPAMI.2009.167","article-title":"Object detection with discriminatively trained part-based models","volume":"32","author":"Felzenszwalb","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.isprsjprs.2013.08.001","article-title":"Object detection in remote sensing imagery using a discriminatively trained mixture model","volume":"85","author":"Cheng","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1909","DOI":"10.1109\/JSTARS.2017.2655098","article-title":"Occluded object detection in high-resolution remote sensing images using partial configuration object model","volume":"10","author":"Qiu","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_18","first-page":"1","article-title":"Feature extraction by rotation-invariant matrix representation for object detection in aerial image","volume":"PP","author":"Wang","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"744","DOI":"10.1109\/LGRS.2017.2677954","article-title":"An effective method based on acf for aircraft detection in remote sensing images","volume":"14","author":"Zhao","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.isprsjprs.2010.11.001","article-title":"Support vector machines in remote sensing: A review","volume":"66","author":"Mountrakis","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1206","DOI":"10.1109\/TGRS.2011.2166966","article-title":"Rotation-invariant object detection of remotely sensed images based on texton forest and hough voting","volume":"50","author":"Lei","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.isprsjprs.2014.10.007","article-title":"A generic discriminative part-based model for geospatial object detection in optical remote sensing images","volume":"99","author":"Zhang","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Bi, F., Chen, J., Zhuang, Y., Bian, M., and Zhang, Q. (2017). A decision mixture model-based method for inshore ship detection using high-resolution remote sensing images. Sensors, 17.","DOI":"10.3390\/s17071470"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zia, M., Stark, M., and Schindler, K. (2013, January 23\u201328). Explicit occlusion modeling for 3d object class representations. Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.427"},{"key":"ref_26","unstructured":"Ouyang, W., and Wang, X. (2012, January 16\u201321). A discriminative deep model for pedestrian detection with occlusion handling. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI, USA."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Niknejad, H.T., Kawano, T., Oishi, Y., and Mita, S. (2013, January 23\u201326). Occlusion handling using discriminative model of trained part templates and conditional random field. Proceedings of the 2013 IEEE Intelligent Vehicles Symposium (IV), Gold Coast, QLD, Australia.","DOI":"10.1109\/IVS.2013.6629557"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1007\/BF00977785","article-title":"Two algorithms for constructing a delaunay triangulation","volume":"9","author":"Lee","year":"1980","journal-title":"Int. J. Comput. Inf. Sci."},{"key":"ref_29","unstructured":"West, D.B. (2001). Introduction to Graph Theory, Prentice Hall."},{"key":"ref_30","unstructured":"Grant, M., and Boyd, S. (2018, March 14). Cvx: Matlab Software for Disciplined Convex Programming, Version 2.0 Beta. Available online: http:\/\/cvxr.com\/cvx."},{"key":"ref_31","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":"2010","journal-title":"Int. J. Comput. Vis."},{"key":"ref_32","unstructured":"Felzenszwalb, P.F., Girshick, R.B., and McAllester, D. (2018, March 14). Discriminatively Trained Deformable Part Models, Release 4. Available online: http:\/\/people.cs.uchicago.edu\/~pff\/latent-release4\/."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Cai, Z., Fan, Q., Feris, R., and Vasconcelos, N. (2016, January 8\u201316). A unified multi-scale deep convolutional neural network for fast object detection. Proceedings of the European Conference on Computer Vision 2016, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46493-0_22"},{"key":"ref_34","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_35","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L., Li, K., and Li, F.F. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Xu, F., Liu, J., Dong, C., and Wang, X. (2017). Ship detection in optical remote sensing images based on wavelet transform and multi-level false alarm identification. Remote Sens., 9.","DOI":"10.3390\/rs9100985"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/3\/464\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T14:57:16Z","timestamp":1760194636000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/3\/464"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,3,15]]},"references-count":36,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2018,3]]}},"alternative-id":["rs10030464"],"URL":"https:\/\/doi.org\/10.3390\/rs10030464","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2018,3,15]]}}}