{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T10:16:41Z","timestamp":1783160201532,"version":"3.54.6"},"reference-count":27,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2016,9,21]],"date-time":"2016-09-21T00:00:00Z","timestamp":1474416000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the ShenZhen Science and Technology Foundation","award":["JCYJ20160229172932237"],"award-info":[{"award-number":["JCYJ20160229172932237"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61672429"],"award-info":[{"award-number":["61672429"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61502364"],"award-info":[{"award-number":["61502364"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61272288"],"award-info":[{"award-number":["61272288"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61231016"],"award-info":[{"award-number":["61231016"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Northwestern Polytechnical University (NPU) New AoXiang Star","award":["G2015KY0301"],"award-info":[{"award-number":["G2015KY0301"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["3102015AX007"],"award-info":[{"award-number":["3102015AX007"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NPU New People and Direction","award":["13GH014604"],"award-info":[{"award-number":["13GH014604"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Vehicle surveillance of a wide area allows us to learn much about the daily activities and traffic information. With the rapid development of remote sensing, satellite video has become an important data source for vehicle detection, which provides a broader field of surveillance. The achieved work generally focuses on aerial video with moderately-sized objects based on feature extraction. However, the moving vehicles in satellite video imagery range from just a few pixels to dozens of pixels and exhibit low contrast with respect to the background, which makes it hard to get available appearance or shape information. In this paper, we look into the problem of moving vehicle detection in satellite imagery. To the best of our knowledge, it is the first time to deal with moving vehicle detection from satellite videos. Our approach consists of two stages: first, through foreground motion segmentation and trajectory accumulation, the scene motion heat map is dynamically built. Following this, a novel saliency based background model which intensifies moving objects is presented to segment the vehicles in the hot regions. Qualitative and quantitative experiments on sequence from a recent Skybox satellite video dataset demonstrates that our approach achieves a high detection rate and low false alarm simultaneously.<\/jats:p>","DOI":"10.3390\/s16091528","type":"journal-article","created":{"date-parts":[[2016,9,21]],"date-time":"2016-09-21T10:11:26Z","timestamp":1474452686000},"page":"1528","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":82,"title":["Small Moving Vehicle Detection in a Satellite Video of an Urban Area"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5180-2316","authenticated-orcid":false,"given":"Tao","family":"Yang","sequence":"first","affiliation":[{"name":"ShaanXi Provincial Key Lab of Speech and Image Infromation Processing, School of Computer Science, Northwestern Polytechnical University, Xi\u2019an 710129, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiwen","family":"Wang","sequence":"additional","affiliation":[{"name":"ShaanXi Provincial Key Lab of Speech and Image Infromation Processing, School of Computer Science, Northwestern Polytechnical University, Xi\u2019an 710129, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bowei","family":"Yao","sequence":"additional","affiliation":[{"name":"ShaanXi Provincial Key Lab of Speech and Image Infromation Processing, School of Computer Science, Northwestern Polytechnical University, Xi\u2019an 710129, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Li","sequence":"additional","affiliation":[{"name":"School of Telecommunications Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanning","family":"Zhang","sequence":"additional","affiliation":[{"name":"ShaanXi Provincial Key Lab of Speech and Image Infromation Processing, School of Computer Science, Northwestern Polytechnical University, Xi\u2019an 710129, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhannan","family":"He","sequence":"additional","affiliation":[{"name":"ShaanXi Provincial Key Lab of Speech and Image Infromation Processing, School of Computer Science, Northwestern Polytechnical University, Xi\u2019an 710129, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wencheng","family":"Duan","sequence":"additional","affiliation":[{"name":"ShaanXi Provincial Key Lab of Speech and Image Infromation Processing, School of Computer Science, Northwestern Polytechnical University, Xi\u2019an 710129, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2016,9,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gueguen, L., and Hamid, R. (2015, January 7\u201312). Large-scale damage detection using satellite imagery. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298737"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.rse.2012.02.022","article-title":"Near real-time disturbance detection using satellite image time series","volume":"123","author":"Verbesselt","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_3","unstructured":"Greidanus, H. (2008). Remote Sensing of the European Seas, Springer."},{"key":"ref_4","unstructured":"Min, H.S., Jung, H., Kim, K.S., and Kim, J.W. (2014). Method and Terminal for Providing a Route in a Navigation System Using Satellite Image. (8,762,052), U.S. Patent."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1109\/TGRS.2015.2451002","article-title":"Vehicle Detection in High-Resolution Aerial Images via Sparse Representation and Superpixels","volume":"54","author":"Chen","year":"2016","journal-title":"IEEE Trans Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Teutsch, M., and Kruger, W. (2015, January 7\u201312). Robust and fast detection of moving vehicles in aerial videos using sliding windows. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Boston, MA, USA.","DOI":"10.1109\/CVPRW.2015.7301396"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Teng, X., Cao, L., Wang, C., and Chen, Z. (2014, January 10\u201312). Vehicle detection from remote sensing image based on superpixel segmentation and image enhancement. Proceedings of the International Conference on Internet Multimedia Computing and Service, Xiamen, China.","DOI":"10.1145\/2632856.2632949"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Wang, X., Zhou, G., and Jiang, L. (2012, January 22\u201327). Vehicle detection based on morphology from highway aerial images. Proceedings of the 2012 IEEE International on Geoscience and Remote Sensing Symposium, Munich, Germany.","DOI":"10.1109\/IGARSS.2012.6352241"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Su, A., Zhu, X., Zhang, X., and Shang, Y. (2013, January 26). Salient object detection approach in UAV video. Proceedings of the Eighth International Symposium on Multispectral Image Processing and Pattern Recognition, Wuhan, China.","DOI":"10.1117\/12.2032141"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ouyang, Y., Duval, P.L., Sheng, Y., and Lavigne, D.A. (2011, January 25). Robust component-based car detection in aerial imagery with new segmentation techniques. Proceedings of the Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications VIII, Orlando, FL, USA.","DOI":"10.1117\/12.884356"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4850","DOI":"10.1080\/01431161.2013.782708","article-title":"Automatic system for operational traffic monitoring using very-high-resolution satellite imagery","volume":"34","author":"Larsen","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1080\/01431160500238901","article-title":"Vehicle detection in 1-m resolution satellite and airborne imagery","volume":"27","author":"Sharma","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1422","DOI":"10.1016\/j.imavis.2006.12.011","article-title":"Vehicle detection from high-resolution satellite imagery using morphological shared-weight neural networks","volume":"25","author":"Jin","year":"2007","journal-title":"Image Vis. Comput."},{"key":"ref_14","first-page":"67","article-title":"An artificial immune approach for vehicle detection from high resolution space imagery","volume":"7","author":"Zheng","year":"2007","journal-title":"Int. J. Comput. Sci. Netw. Secur."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2795","DOI":"10.1109\/TGRS.2010.2043109","article-title":"Vehicle detection in very high resolution satellite images of city areas","volume":"48","author":"Leitloff","year":"2010","journal-title":"IEEE Trans Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1016\/j.patrec.2005.08.013","article-title":"Car detection in aerial thermal images by local and global evidence accumulation","volume":"27","author":"Hinz","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1109\/JSTARS.2010.2069555","article-title":"Automated vehicle extraction and speed determination from QuickBird satellite images","volume":"4","author":"Liu","year":"2011","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Barnich, O., and Van Droogenbroeck, M. (2009, January 19\u201324). ViBe: A powerful random technique to estimate the background in video sequences. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Taipei, China.","DOI":"10.1109\/ICASSP.2009.4959741"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhai, Y., and Shah, M. (2006, January 23\u201327). Visual attention detection in video sequences using spatiotemporal cues. Proceedings of the 14th Annual ACM International Conference on Multimedia, Santa Barbara, CA, USA.","DOI":"10.1145\/1180639.1180824"},{"key":"ref_20","unstructured":"Achanta, R., Estrada, F., Wils, P., and S\u00fcsstrunk, S. (2008). Computer Vision Systems, Springer."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1109\/TPAMI.2014.2345401","article-title":"Global contrast based salient region detection","volume":"37","author":"Cheng","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Achanta, R., Hemami, S., Estrada, F., and Susstrunk, S. (2009, January 20\u201325). Frequency-tuned salient region detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPRW.2009.5206596"},{"key":"ref_23","unstructured":"A Video Sequence Captured in Dubai by Skysat-1. Available online: http:\/\/www.firstimagery.skybox.com."},{"key":"ref_24","unstructured":"KaewTraKulPong, P., and Bowden, R. (2002). Video-Based Surveillance Systems, Springer."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1007\/BF01215814","article-title":"Segmentation and tracking of piglets in images","volume":"8","author":"McFarlane","year":"1995","journal-title":"Mach. Vision Appl."},{"key":"ref_26","unstructured":"Stauffer, C., and Grimson, W.E.L. (1999, January 23\u201325). Adaptive background mixture models for real-time tracking. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Fort Collins, CO, USA."},{"key":"ref_27","unstructured":"Sobral, A. BGS Library: An OpenCV C++ Background Subtraction Library. Available online: https:\/\/github.com\/andrewssobral\/bgslibrary."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/16\/9\/1528\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:31:28Z","timestamp":1760211088000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/16\/9\/1528"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,9,21]]},"references-count":27,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2016,9]]}},"alternative-id":["s16091528"],"URL":"https:\/\/doi.org\/10.3390\/s16091528","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,9,21]]}}}