{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T11:15:40Z","timestamp":1762341340829,"version":"build-2065373602"},"reference-count":41,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2019,11,7]],"date-time":"2019-11-07T00:00:00Z","timestamp":1573084800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"NSF of China","doi-asserted-by":"publisher","award":["11301414","11226173"],"award-info":[{"award-number":["11301414","11226173"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Many techniques have been developed for computer vision in the past years. Features extraction and matching are the basis of many high-level applications. In this paper, we propose a multi-level features extraction for discontinuous target tracking in remote sensing image monitoring. The features of the reference image are pre-extracted at different levels. The first-level features are used to roughly check the candidate targets and other levels are used for refined matching. With Gaussian weight function introduced, the support of matching features is accumulated to make a final decision. Adaptive neighborhood and principal component analysis are used to improve the description of the feature. Experimental results verify the efficiency and accuracy of the proposed method.<\/jats:p>","DOI":"10.3390\/s19224855","type":"journal-article","created":{"date-parts":[[2019,11,7]],"date-time":"2019-11-07T11:17:25Z","timestamp":1573125445000},"page":"4855","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Multi-Level Features Extraction for Discontinuous Target Tracking in Remote Sensing Image Monitoring"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3642-2048","authenticated-orcid":false,"given":"Bin","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Sciences, Southwest Petroleum University, Chengdu 610500, China"},{"name":"Institute of Artificial Intelligence, Southwest Petroleum University, Chengdu 610500, China"},{"name":"Research Center of Mathematical Mechanics, Southwest Petroleum University, Chengdu 610500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuemei","family":"Duan","sequence":"additional","affiliation":[{"name":"School of Sciences, Southwest Petroleum University, Chengdu 610500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongjun","family":"Ye","sequence":"additional","affiliation":[{"name":"School of Sciences, Southwest Petroleum University, Chengdu 610500, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9073-5347","authenticated-orcid":false,"given":"Marcin","family":"Wo\u017aniak","sequence":"additional","affiliation":[{"name":"Institute of Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1972-5979","authenticated-orcid":false,"given":"Dawid","family":"Po\u0142ap","sequence":"additional","affiliation":[{"name":"Institute of Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9990-1084","authenticated-orcid":false,"given":"Robertas","family":"Dama\u0161evi\u010dius","sequence":"additional","affiliation":[{"name":"Institute of Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,7]]},"reference":[{"key":"ref_1","first-page":"301","article-title":"Remote Sensing Image Monitoring and Recognition Technology for the Conservation of Rare Wild Animals","volume":"29","author":"Chen","year":"2019","journal-title":"Rev. Cient."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Roopa, D., and Chaudhari, S. (2019, January 15\u201316). A survey on Geographic Multipath Routing Techniques in Wireless Sensor Networks. Proceedings of the IEEE 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS), Coimbatore, India.","DOI":"10.1109\/ICACCS.2019.8728555"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.bdr.2017.09.003","article-title":"Big data model simulation on a graph database for surveillance in wireless multimedia sensor networks","volume":"11","year":"2018","journal-title":"Big Data Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.apgeog.2011.10.010","article-title":"Optimizing land cover classification accuracy for change detection, a combined pixel-based and object-based approach in a mountainous area in Mexico","volume":"34","author":"Seijmonsbergen","year":"2012","journal-title":"Appl. Geogr."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1080\/15481603.2018.1426092","article-title":"Geographic Object-based Image Analysis (GEOBIA): Emerging trends and future opportunities","volume":"55","author":"Chen","year":"2018","journal-title":"GIScie. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"921","DOI":"10.1016\/j.comnet.2006.10.002","article-title":"A survey on wireless multimedia sensor networks","volume":"51","author":"Akyildiz","year":"2007","journal-title":"Comput. Netw."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Abbas, N., Yu, F., and Fan, Y. (2018). Intelligent Video Surveillance Platform for Wireless Multimedia Sensor Networks. Appl. Sci., 8.","DOI":"10.3390\/app8030348"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1056","DOI":"10.1109\/COMST.2018.2794358","article-title":"Wireless multimedia cognitive radio networks: A comprehensive survey","volume":"20","author":"Amjad","year":"2018","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_9","unstructured":"Usman, M., Jan, M.A., He, X., and Chen, J. (2018). A mobile multimedia data collection scheme for secured wireless multimedia sensor networks. IEEE Trans. Netw. Sci. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Sun, Y., Zheng, L., Yang, Y., Tian, Q., and Wang, S. (2018, January 8\u201314). Beyond part models: Person retrieval with refined part pooling (and a strong convolutional baseline). Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01225-0_30"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Yu, H.X., Zheng, W.S., Wu, A., Guo, X., Gong, S., and Lai, J.H. (2019, January 16\u201320). Unsupervised Person Re-identification by Soft Multilabel Learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00225"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Li, M., Zhu, X., and Gong, S. (2019). Unsupervised Tracklet Person Re-Identification. IEEE Trans. Pattern Anal. Mach. Intell.","DOI":"10.1109\/TPAMI.2019.2903058"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4766","DOI":"10.1109\/TIP.2015.2467315","article-title":"Bit-scalable deep hashing with regularized similarity learning for image retrieval and person re-identification","volume":"24","author":"Zhang","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"K\u00fc\u00e7\u00fckke\u00e7eci, C., and Yazici, A. (2019). Multilevel Object Tracking in Wireless Multimedia Sensor Networks for Surveillance Applications Using Graph-based Big Data. IEEE Access.","DOI":"10.1109\/ACCESS.2019.2918765"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"729","DOI":"10.1007\/s11042-014-2177-x","article-title":"Multiple human detection and tracking based on head detection for real-time video surveillance","volume":"74","author":"Xu","year":"2015","journal-title":"Multimed. Tools Appl."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"12003","DOI":"10.1007\/s11042-017-4846-z","article-title":"Trajectroy prediction for target tracking using acoustic and image hybrid wireless multimedia sensors networks","volume":"77","author":"Xiao","year":"2018","journal-title":"Multimed. Tools Appl."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2534","DOI":"10.1109\/LCOMM.2016.2612652","article-title":"Wireless sensor network for wildlife tracking and behavior classification of animals in Do\u00f1ana","volume":"20","year":"2016","journal-title":"IEEE Commun. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"5942","DOI":"10.1109\/JSEN.2016.2574989","article-title":"A novel energy efficient object detection and image transmission approach for wireless multimedia sensor networks","volume":"16","author":"Rehman","year":"2016","journal-title":"IEEE Sens. J."},{"key":"ref_19","first-page":"1150","article-title":"Object recognition from local scale-invariant features","volume":"Volume 2","author":"Lowe","year":"1999","journal-title":"Proceedings of the 1999 7th IEEE International Conference on Computer Vision"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive image features from scale-invariant keypoints","volume":"60","author":"Lowe","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1007\/s11263-015-0824-y","article-title":"A comprehensive performance evaluation of 3D local feature descriptors","volume":"116","author":"Guo","year":"2016","journal-title":"Int. J. Comput. Vis."},{"key":"ref_22","first-page":"10","article-title":"A combined corner and edge detector","volume":"Volume 15","author":"Harris","year":"1988","journal-title":"Alvey Vision Conference"},{"key":"ref_23","unstructured":"F\u00f6rstner, W., and G\u00fclch, E. (1987, January 2\u20134). A fast operator for detection and precise location of distinct points, corners and centres of circular features. Proceedings of the ISPRS Intercommission Conference on Fast Processing of Photogrammetric Data, Interlaken, Switzerland."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Li, Y., Li, Q., Liu, Y., and Xie, W. (2018). A spatial-spectral SIFT for hyperspectral image matching and classification. Pattern Recognition Letters, Elsevier.","DOI":"10.1016\/j.patrec.2018.08.032"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"251","DOI":"10.5201\/ipol.2018.225","article-title":"Fast Affine Invariant Image Matching","volume":"8","author":"Delon","year":"2018","journal-title":"Image Process. Line"},{"key":"ref_26","unstructured":"Ke, Y., and Sukthankar, R. (July, January 27). PCA-SIFT: A more distinctive representation for local image descriptors. Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition\u2014CVPR 2004, Washington, DC, USA."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Bay, H., Tuytelaars, T., and Van Gool, L. (2006). Surf: Speeded up robust features. European Conference on Computer Vision, Springer.","DOI":"10.1007\/11744023_32"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1007\/s11263-007-0077-5","article-title":"Region-based hierarchical image matching","volume":"78","author":"Todorovic","year":"2008","journal-title":"Int. J. Comput. Vis."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"875","DOI":"10.1109\/TPAMI.2015.2474356","article-title":"Dense correspondences across scenes and scales","volume":"38","author":"Tau","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"111338","DOI":"10.1016\/j.rse.2019.111338","article-title":"Remote sensing monitoring of multi-scale watersheds impermeability for urban hydrological evaluation","volume":"232","author":"Shao","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Long, N., Millescamps, B., Guillot, B., Pouget, F., and Bertin, X. (2016). Monitoring the topography of a dynamic tidal inlet using UAV imagery. Remote Sens., 8.","DOI":"10.3390\/rs8050387"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"5048","DOI":"10.1109\/TSP.2012.2204257","article-title":"Dynamic bit allocation for object tracking in wireless sensor networks","volume":"60","author":"Masazade","year":"2012","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.patrec.2012.07.005","article-title":"Intelligent multi-camera video surveillance: A review","volume":"34","author":"Wang","year":"2013","journal-title":"Pattern Recognit. Lett."},{"key":"ref_34","unstructured":"Zheng, L., Yang, Y., and Hauptmann, A.G. (2016). Person re-identification: Past, present and future. arXiv."},{"key":"ref_35","unstructured":"Zajdel, W., Zivkovic, Z., and Krose, B. (2005, January 18\u201322). Keeping track of humans: Have I seen this person before?. Proceedings of the 2005 IEEE International Conference on Robotics and Automation, Barcelona, Spain."},{"key":"ref_36","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). Imagenet classification with deep convolutional neural networks. Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Xu, Y., Ma, B., Huang, R., and Lin, L. (2014, January 3\u20137). Person search in a scene by jointly modeling people commonness and person uniqueness. Proceedings of the 22nd ACM International Conference on Multimedia, Orlando, FL, USA.","DOI":"10.1145\/2647868.2654965"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1016\/j.imavis.2004.02.006","article-title":"Robust wide-baseline stereo from maximally stable extremal regions","volume":"22","author":"Matas","year":"2004","journal-title":"Image Vis. Comput."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Alahi, A., Ortiz, R., and Vandergheynst, P. (2012, January 16\u201321). Freak: Fast retina keypoint. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA.","DOI":"10.1109\/CVPR.2012.6247715"},{"key":"ref_40","unstructured":"Gheissari, N., Sebastian, T.B., and Hartley, R. (2006, January 17\u201322). Person reidentification using spatiotemporal appearance. Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201906), New York, NY, USA."},{"key":"ref_41","unstructured":"(2019, July 30). Visual Tracker Benchmark. Available online: http:\/\/www.visual-tracking.net."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/22\/4855\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:32:43Z","timestamp":1760189563000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/22\/4855"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,7]]},"references-count":41,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2019,11]]}},"alternative-id":["s19224855"],"URL":"https:\/\/doi.org\/10.3390\/s19224855","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2019,11,7]]}}}