{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T16:54:00Z","timestamp":1777568040899,"version":"3.51.4"},"reference-count":44,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2020,9,8]],"date-time":"2020-09-08T00:00:00Z","timestamp":1599523200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100009592","name":"Beijing Municipal Science and Technology Commission","doi-asserted-by":"publisher","award":["No.Z191100001419002"],"award-info":[{"award-number":["No.Z191100001419002"]}],"id":[{"id":"10.13039\/501100009592","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Building extraction from high-resolution remote sensing images plays a vital part in urban planning, safety supervision, geographic databases updates, and some other applications. Several researches are devoted to using convolutional neural network (CNN) to extract buildings from high-resolution satellite\/aerial images. There are two major methods, one is the CNN-based semantic segmentation methods, which can not distinguish different objects of the same category and may lead to edge connection. The other one is CNN-based instance segmentation methods, which rely heavily on pre-defined anchors, and result in the highly sensitive, high computation\/storage cost and imbalance between positive and negative samples. Therefore, in this paper, we propose an improved anchor-free instance segmentation method based on CenterMask with spatial and channel attention-guided mechanisms and improved effective backbone network for accurate extraction of buildings in high-resolution remote sensing images. Then we analyze the influence of different parameters and network structure on the performance of the model, and compare the performance for building extraction of Mask R-CNN, Mask Scoring R-CNN, CenterMask, and the improved CenterMask in this paper. Experimental results show that our improved CenterMask method can successfully well-balanced performance in terms of speed and accuracy, which achieves state-of-the-art performance at real-time speed.<\/jats:p>","DOI":"10.3390\/rs12182910","type":"journal-article","created":{"date-parts":[[2020,9,8]],"date-time":"2020-09-08T09:03:48Z","timestamp":1599555828000},"page":"2910","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Improved Anchor-Free Instance Segmentation for Building Extraction from High-Resolution Remote Sensing Images"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5538-5118","authenticated-orcid":false,"given":"Tong","family":"Wu","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuan","family":"Hu","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ling","family":"Peng","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruonan","family":"Chen","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"283","DOI":"10.5194\/isprs-archives-XLII-3-283-2018","article-title":"Study on Building Extraction from High-Resolution Images Using Mbi","volume":"42","author":"Ding","year":"2018","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Shinohara, T., Xiu, H., and Matsuoka, M. (2020). FWNet: Semantic Segmentation for Full-Waveform LiDAR Data Using Deep Learning. Sensors, 20.","DOI":"10.3390\/s20123568"},{"key":"ref_3","unstructured":"Colaninno, N., Roca, J., and Pfeffer, K. (September, January 30). An automatic classification of urban texture: Form and compactness of morphological homogeneous structures in Barcelona. Proceedings of the 51st Congress of the European Regional Science Association: New Challenges for European Regions and Urban Areas in a Globalised World, Barcelona, Spain."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.compenvurbsys.2013.12.002","article-title":"Using street based metrics to characterize urban typologies","volume":"44","author":"Hermosilla","year":"2014","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.landurbplan.2011.03.017","article-title":"Mapping form and function in urban areas: An approach based on urban metrics and continuous impervious surface data","volume":"102","author":"Jacquet","year":"2011","journal-title":"Landsc. Urban Plan."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/0734-189X(88)90016-3","article-title":"Detecting buildings in aerial images","volume":"41","author":"Huertas","year":"1988","journal-title":"Comput. Vision Graph. Image Process."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1564","DOI":"10.1109\/21.44071","article-title":"Methods for exploiting the relationship between buildings and their shadows in aerial imagery","volume":"19","author":"Irvin","year":"1989","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.isprsjprs.2007.05.011","article-title":"Automatic recognition of man-made objects in high resolution optical remote sensing images by SVM classification of geometric image features","volume":"62","author":"Inglada","year":"2007","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Meng, Y., and Peng, S. (2009, January 19\u201320). Object-oriented building extraction from high-resolution imagery based on fuzzy SVM. Proceedings of the 2009 International Conference on Information Engineering and Computer Science, Wuhan, China.","DOI":"10.1109\/ICIECS.2009.5366011"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2013.05.006","article-title":"Automatic extraction of building roofs using LIDAR data and multispectral imagery","volume":"83","author":"Awrangjeb","year":"2013","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3716","DOI":"10.3390\/rs6053716","article-title":"Automatic segmentation of raw LiDAR data for extraction of building roofs","volume":"6","author":"Awrangjeb","year":"2014","journal-title":"Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1080\/15481603.2017.1361509","article-title":"Segmentation of airborne point cloud data for automatic building roof extraction","volume":"55","author":"Gilani","year":"2018","journal-title":"Gisci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Gilani, S.A.N., Awrangjeb, M., and Lu, G. (2016). An automatic building extraction and regularisation technique using lidar point cloud data and orthoimage. Remote Sens., 8.","DOI":"10.3390\/rs8030258"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","unstructured":"Xu, Y., Wu, L., Xie, Z., and Chen, Z. (2018). Building Extraction in Very High Resolution Remote Sensing Imagery Using Deep Learning and Guided Filters. Remote Sens., 10.","DOI":"10.3390\/rs10010144"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Shrestha, S., and Vanneschi, L. (2018). Improved fully convolutional network with conditional random fields for building extraction. Remote Sens., 10.","DOI":"10.3390\/rs10071135"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3680","DOI":"10.1109\/JSTARS.2018.2865187","article-title":"Building-A-Nets: Robust Building Extraction from High-Resolution Remote Sensing Images with Adversarial Networks","volume":"11","author":"Li","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_20","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_21","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_22","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 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach City, CA, USA.","DOI":"10.1109\/CVPR.2019.00657"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Cai, Z., and Vasconcelos, N. (2018, January 18\u201322). Cascade R-CNN: Delving Into High Quality Object Detection. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00644"},{"key":"ref_24","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 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, Y., Chen, Y., Wang, N., and Zhang, Z.X. (November, January 27). Scale-Aware Trident Networks for Object Detection. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00615"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S.E., Fu, C.Y., and Berg, A.C. (2016). SSD: Single Shot MultiBox Detector. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_27","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 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Bolya, D., Zhou, C., Xiao, F., and Lee, Y.J. (November, January 27). YOLACT: Real-Time Instance Segmentation. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00925"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Potlapally, A., Chowdary, P.S.R., Shekhar, S.R., Mishra, N., Madhuri, C.S.V.D., and Prasad, A. (2019, January 12\u201314). Instance Segmentation in Remote Sensing Imagery using Deep Convolutional Neural Networks. Proceedings of the 2019 International Conference on contemporary Computing and Informatics (IC3I), Singapore.","DOI":"10.1109\/IC3I46837.2019.9055569"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ji, S., Shen, Y., Lu, M., and Zhang, Y. (2019). Building instance change detection from large-scale aerial images using convolutional neural networks and simulated samples. Remote Sens., 11.","DOI":"10.3390\/rs11111343"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Li, Q., Mou, L., Hua, Y., Sun, Y., Jin, P., Shi, Y., and Zhu, X.X. (2020). Instance segmentation of buildings using keypoints. arXiv.","DOI":"10.1109\/IGARSS39084.2020.9324457"},{"key":"ref_32","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_33","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_34","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_35","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_36","doi-asserted-by":"crossref","unstructured":"Chen, H., Sun, K., Tian, Z., Shen, C., Huang, Y., and Yan, Y. (2020, January 14\u201319). BlendMask: Top-Down Meets Bottom-Up for Instance Segmentation. Proceedings of the CVPR 2020: Computer Vision and Pattern Recognition, Virtual, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00860"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Lee, Y., and Park, J. (2020, January 14\u201319). CenterMask: Real-Time Anchor-Free Instance Segmentation. Proceedings of the CVPR 2020: Computer Vision and Pattern Recognition, Virtual, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01392"},{"key":"ref_38","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_39","doi-asserted-by":"crossref","unstructured":"Lee, Y., Hwang, J.W., Lee, S., Bae, Y., and Park, J. (2019, January 16\u201320). An energy and gpu-computation efficient backbone network for real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Long Beach City, CA, USA.","DOI":"10.1109\/CVPRW.2019.00103"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","article-title":"Focal Loss for Dense Object Detection","volume":"42","author":"Lin","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_42","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 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00075"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1109\/TGRS.2018.2858817","article-title":"Fully Convolutional Networks for Multisource Building Extraction From an Open Aerial and Satellite Imagery Data Set","volume":"57","author":"Ji","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/0734-189X(85)90016-7","article-title":"Topological Structural Analysis of Digitized Binary Images by Border Following","volume":"30","author":"Suzuki","year":"1985","journal-title":"Graph. Model. Graph. Model. Image Process. Comput. Vis. Graph. Image Process."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/18\/2910\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:07:59Z","timestamp":1760177279000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/18\/2910"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,8]]},"references-count":44,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["rs12182910"],"URL":"https:\/\/doi.org\/10.3390\/rs12182910","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,8]]}}}