{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:32:31Z","timestamp":1760239951236,"version":"build-2065373602"},"reference-count":45,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2019,1,30]],"date-time":"2019-01-30T00:00:00Z","timestamp":1548806400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2017YFC0803802"],"award-info":[{"award-number":["2017YFC0803802"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In this paper, the problem of multi-scale geospatial object detection in High Resolution Remote Sensing Images (HRRSI) is tackled. The different flight heights, shooting angles and sizes of geographic objects in the HRRSI lead to large scale variance in geographic objects. The inappropriate anchor size to propose the objects and the indiscriminative ability of features for describing the objects are the main causes of missing detection and false detection in multi-scale geographic object detection. To address these challenges, we propose a class-specific anchor based and context-guided multi-class object detection method with a convolutional neural network (CNN), which can be divided into two parts: a class-specific anchor based region proposal network (RPN) and a discriminative feature with a context information classification network. A class-specific anchor block providing better initial values for RPN is proposed to generate the anchor of the most suitable scale for each category in order to increase the recall ratio. Meanwhile, we proposed to incorporate the context information into the original convolutional feature to improve the discriminative ability of the features and increase classification accuracy. Considering the quality of samples for classification, the soft filter is proposed to select effective boxes to improve the diversity of the samples for the classifier and avoid missing or false detection to some extent. We also introduced the focal loss in order to improve the classifier in classifying the hard samples. The proposed method is tested on a benchmark dataset of ten classes to prove the superiority. The proposed method outperforms some state-of-the-art methods with a mean average precision (mAP) of 90.4% and better detects the multi-scale objects, especially when objects show a minor shape change.<\/jats:p>","DOI":"10.3390\/rs11030272","type":"journal-article","created":{"date-parts":[[2019,1,30]],"date-time":"2019-01-30T10:58:27Z","timestamp":1548845907000},"page":"272","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Class-Specific Anchor Based and Context-Guided Multi-Class Object Detection in High Resolution Remote Sensing Imagery with a Convolutional Neural Network"],"prefix":"10.3390","volume":"11","author":[{"given":"Nan","family":"Mo","sequence":"first","affiliation":[{"name":"School of Geodesy and Geomatics, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"}]},{"given":"Li","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Geodesy and Geomatics, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5006-0840","authenticated-orcid":false,"given":"Ruixi","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Geodesy and Geomatics, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"}]},{"given":"Hong","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Geodesy and Geomatics, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","article-title":"Deep learning in remote sensing: A comprehensive review and list of resources","volume":"5","author":"Zhu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1016\/j.cviu.2013.04.005","article-title":"50 years of object recognition: Directions forward","volume":"117","author":"Andreopoulos","year":"2013","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_3","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_4","doi-asserted-by":"crossref","unstructured":"Pham, I., Jalovecky, R., and Polasek, M. (2015, January 13\u201317). Using template matching for object recognition in infrared video sequences. Proceedings of the 2015 IEEE\/AIAA 34th Digital Avionics Systems Conference (DASC), Prague, Czech Republic. 8C5-1\u20138C5-9.","DOI":"10.1109\/DASC.2015.7311477"},{"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":"746","DOI":"10.1109\/LGRS.2014.2360887","article-title":"Rotation-Invariant Object Detection in Remote Sensing Images Based on Radial-Gradient Angle","volume":"12","author":"Lin","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_7","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_8","doi-asserted-by":"crossref","first-page":"1701","DOI":"10.1109\/TGRS.2012.2207123","article-title":"Automated detection of arbitrarily shaped buildings in complex environments from monocular VHR optical satellite imagery","volume":"51","author":"Ok","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhang, D., Han, J., Zhao, L., and Meng, D. (2018). Leveraging prior-knowledge for weakly supervised object detection under a collaborative self-paced curriculum learning framework. Int. J. Comput. Vis., 1\u201318.","DOI":"10.1007\/s11263-018-1112-4"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"9705","DOI":"10.3390\/rs70809705","article-title":"Identification of forested landslides using LiDar data, object-based image analysis, and machine learning algorithms","volume":"7","author":"Li","year":"2015","journal-title":"Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.isprsjprs.2015.04.010","article-title":"Scale parameter selection by spatial statistics for GeOBIA: Using mean-shift based multi-scale segmentation as an example","volume":"106","author":"Ming","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.isprsjprs.2017.11.023","article-title":"Object-based detection of vehicles using combined optical and elevation data","volume":"136","author":"Schilling","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1049\/iet-cvi.2014.0270","article-title":"Auto-encoder-based shared mid-level visual dictionary learning for scene classification using very high resolution remote sensing images","volume":"9","author":"Cheng","year":"2015","journal-title":"IET Comput. Vis."},{"key":"ref_14","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_15","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1109\/TIP.2018.2867198","article-title":"Learning rotation-invariant and fisher discriminative convolutional neural networks for object detection","volume":"28","author":"Cheng","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1134\/S1054661816010065","article-title":"A survey of deep learning methods and software tools for image classification and object detection","volume":"26","author":"Druzhkov","year":"2016","journal-title":"Pattern Recognit. Image Anal."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhou, X., Gong, W., Fu, W.L., and Du, F. (2017, January 10\u201313). Application of deep learning in object detection. Proceedings of the 2017 IEEE\/ACIS 16th International Conference on Computer and Information Science (ICIS), Seoul, Korea.","DOI":"10.1109\/ICIS.2017.7960069"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, A., Reed, S., Fu, C.Y.C., and Berg, A. (2016). Ssd: Single Shot Multibox Detector. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_19","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (July, January 26). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (arXiv, 2016). YOLO9000: Better, faster, stronger, arXiv.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_21","unstructured":"Redmon, J., and Farhadi, A. (arXiv, 2018). Yolov3: An incremental improvement, arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1109\/TPAMI.2015.2437384","article-title":"Region-based convolutional networks for accurate object detection and segmentation","volume":"38","author":"Girshick","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 13\u201316). Fast r-cnn. Proceedings of the IEEE Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_24","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_25","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_26","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_27","doi-asserted-by":"crossref","first-page":"923","DOI":"10.1080\/2150704X.2018.1492172","article-title":"Change detection based on Faster R-CNN for high-resolution remote sensing images","volume":"9","author":"Wang","year":"2018","journal-title":"Remote Sens. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1080\/2150704X.2017.1415473","article-title":"Accurate non-maximum suppression for object detection in high-resolution remote sensing images","volume":"9","author":"Qiu","year":"2018","journal-title":"Remote Sens. Lett."},{"key":"ref_29","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_30","doi-asserted-by":"crossref","unstructured":"Cai, Z., Fan, Q., Feris, R.S., and Vasconcelos, N. (2016). A unified multi-scale deep convolutional neural network for fast object detection. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-46493-0_22"},{"key":"ref_31","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_32","doi-asserted-by":"crossref","unstructured":"Chen, S., Zhan, R., 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_33","first-page":"99880N","article-title":"Generating object proposals for improved object detection in aerial images. Electro-Optical Remote Sensing, X","volume":"9988","author":"Sommer","year":"2016","journal-title":"Int. Soc. Opt. Photonics"},{"key":"ref_34","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":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","unstructured":"Shrivastava, A., Gupta, A., and Girshick, R. (July, January 26). Training region-based object detectors with online hard example mining. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Dollar, P. (2018). Focal loss for dense object detection. IEEE Trans. Pattern Anal. Mach. Intell.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"042621","DOI":"10.1117\/1.JRS.11.042621","article-title":"Deep convolutional neural network for classifying Fusarium wilt of radish from unmanned aerial vehicles","volume":"11","author":"Ha","year":"2017","journal-title":"J. Appl. Remote Sens."},{"key":"ref_38","unstructured":"Dang, L.M., Hassan, S.I., Suhyeon, I., Sangaiah, A.K., Mehmood, I., Rho, S., Seo, S., and Moon, H. (2018). UAV based wilt detection system via convolutional neural networks. Sustain. Comput. Inform. Syst."},{"key":"ref_39","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012). Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst., 1097\u20131105."},{"key":"ref_40","unstructured":"Simonyan, K., and Zisserman, A. (arXiv, 2014). Very deep convolutional networks for large-scale image recognition, arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., and Darrell, T. (2014, January 4\u20136). Caffe: Convolutional architecture for fast feature embedding. Proceedings of the 22nd ACM Conference on Multimedia, Orlando, FL, USA.","DOI":"10.1145\/2647868.2654889"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 8\u201310). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_43","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_44","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_45","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1109\/LGRS.2009.2035644","article-title":"Object classification of aerial images with bag-of-visual words","volume":"7","author":"Xu","year":"2010","journal-title":"IEEE Geosci. Remote Sens. Lett."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/3\/272\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:29:46Z","timestamp":1760185786000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/3\/272"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,30]]},"references-count":45,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2019,2]]}},"alternative-id":["rs11030272"],"URL":"https:\/\/doi.org\/10.3390\/rs11030272","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2019,1,30]]}}}