{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:08:06Z","timestamp":1760238486422,"version":"build-2065373602"},"reference-count":21,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,7,14]],"date-time":"2022-07-14T00:00:00Z","timestamp":1657756800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41771457"],"award-info":[{"award-number":["41771457"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The geometric features of ground objects can reflect the shape, contour, length, width, and pixel distribution of ground objects and have important applications in the process of object detection and recognition. However, the geometric features of objects usually present irregular geometric shapes. In order to fit the irregular geometry accurately, this paper proposes the mark clustering point process. Firstly, the random points in the parent process are used to determine the location of the ground object, and the irregular graph constructed by the clustering points in the sub-process is used as the identification to fit the geometry of the ground object. Secondly, assuming that the spectral measurement values of ground objects obey the independent and unified multivalued Gaussian distribution, the spectral measurement model of remote sensing image data is constructed. Then, the geometric extraction model of the ground object is constructed under the framework of Bayesian theory and combined with the reversible jump Markov chain Monte Carlo (RJMCMC) algorithm to simulate the posterior distribution and estimate the parameters. Finally, the optimal object extraction model is solved according to the maximum a posteriori (MAP) probability criterion. This paper experiments on color remote sensing images. The experimental results show that the proposed method can not only determine the position of the object but also fit the geometric features of the object accurately.<\/jats:p>","DOI":"10.3390\/ijgi11070402","type":"journal-article","created":{"date-parts":[[2022,7,15]],"date-time":"2022-07-15T00:17:10Z","timestamp":1657844230000},"page":"402","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Accurate Extraction of Ground Objects from Remote Sensing Image Based on Mark Clustering Point Process"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3736-5196","authenticated-orcid":false,"given":"Hongyun","family":"Zhang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China"}]},{"given":"Jin","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China"}]},{"given":"Jie","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Marine Technology and Geomatics, Jiangsu Ocean University, Lianyungang 222005, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1023\/A:1008889222784","article-title":"On limits of wireless communications in a fading environment when using multiple antennas","volume":"6","author":"Foschini","year":"1998","journal-title":"Wirel. Pers. Commun."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Masuda, R., and Inoue, R. (2022). Point event cluster detection via the Bayesian generalized fused lasso. ISPRS Int. J. Geo-Inf., 11.","DOI":"10.3390\/ijgi11030187"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3032","DOI":"10.1109\/JSTARS.2020.3000317","article-title":"Multisized object detection using spaceborne optical imagery","volume":"13","author":"Haroon","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1109","DOI":"10.1016\/j.patrec.2010.01.013","article-title":"How to involve structural modeling for cartographic object recognition tasks in high-resolution satellite images","volume":"31","author":"Erus","year":"2010","journal-title":"Pattern Recognit. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Xu, Y., Xiong, W., and Liu, J. (2017, January 25\u201327). A new ship target detection algorithm based on SVM in high resolution SAR images. Proceedings of the International Conference on Advances in Image Processing, Bangkok, Thailand.","DOI":"10.1145\/3133264.3133273"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2053","DOI":"10.1109\/JSTARS.2015.2404578","article-title":"Object detection based on sparse representation and Hough voting for optical remote sensing imagery","volume":"8","author":"Yokoya","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3503","DOI":"10.1109\/JSTARS.2020.3003137","article-title":"Tensored generalized hough transform for object detection in remote sensing images","volume":"13","author":"Chen","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1109\/LGRS.2011.2161569","article-title":"Automatic target detection in high-resolution remote sensing images using spatial sparse coding bag-of-words model","volume":"9","author":"Sun","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1080\/02664769300000065","article-title":"Stochastic geometry models in high-level vision","volume":"20","author":"Baddeley","year":"1993","journal-title":"J. Appl. Stat."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1111\/1467-9868.00143","article-title":"Boundary detection through dynamic polygons","volume":"60","author":"Pievatolo","year":"1998","journal-title":"J. R. Stat. Soc.: Ser. B (Stat. Methodol.)"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"774","DOI":"10.1080\/01431161.2018.1519278","article-title":"Detecting dark spots from SAR intensity images by a point process with irregular geometry marks","volume":"40","author":"Zhao","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1109\/MSP.2002.1028354","article-title":"Marked point process in image analysis","volume":"19","author":"Descombes","year":"2002","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1007\/s11263-005-5033-7","article-title":"Building outline extraction from digital elevation models using marked point processes","volume":"72","author":"Ortner","year":"2007","journal-title":"Int. J. Comput. Vis."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Perrin, G., Descombes, X., and Zerubia, J. (2005, January 14). A marked point process model for tree crown extraction in plantations. Proceedings of the IEEE International Conference on Image Processing, Genova, Italy.","DOI":"10.1109\/ICIP.2005.1529837"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1568","DOI":"10.1109\/TPAMI.2005.206","article-title":"Point processes for unsupervised line network extraction in remote sensing","volume":"27","author":"Lacoste","year":"2005","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1109\/TPAMI.2007.1159","article-title":"A marked point process of rectangles and segments for automatic analysis of digital elevation models","volume":"30","author":"Ortner","year":"2007","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1597","DOI":"10.1109\/TPAMI.2009.152","article-title":"Geometric feature extraction by a multimarked point process","volume":"32","author":"Lafarge","year":"2009","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.artmed.2008.08.012","article-title":"Agentification of Markov model-based segmentation: Application to magnetic resonance brain scans","volume":"46","author":"Scherrer","year":"2009","journal-title":"Artif. Intell. Med."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1590","DOI":"10.1016\/j.rse.2010.02.013","article-title":"Oil spill detection from SAR intensity imagery using a marked point process","volume":"114","author":"Li","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.patrec.2018.01.019","article-title":"Active contours driven by local pre-fitting energy for fast image segmentation","volume":"104","author":"Ding","year":"2018","journal-title":"Pattern Recognit. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1865","DOI":"10.1109\/JPROC.2017.2675998","article-title":"Remote sensing image scene classification: Benchmark and state of the art","volume":"105","author":"Cheng","year":"2017","journal-title":"Proc. IEEE"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/11\/7\/402\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:50:39Z","timestamp":1760140239000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/11\/7\/402"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,14]]},"references-count":21,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["ijgi11070402"],"URL":"https:\/\/doi.org\/10.3390\/ijgi11070402","relation":{},"ISSN":["2220-9964"],"issn-type":[{"type":"electronic","value":"2220-9964"}],"subject":[],"published":{"date-parts":[[2022,7,14]]}}}