Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/66759
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dc.contributorDepartment of Land Surveying and Geo-Informaticsen_US
dc.creatorLi, ZBen_US
dc.creatorShi, WZen_US
dc.date.accessioned2017-05-22T02:26:43Z-
dc.date.available2017-05-22T02:26:43Z-
dc.identifier.issn1001-9014en_US
dc.identifier.urihttp://hdl.handle.net/10397/66759-
dc.language.isoenen_US
dc.publisher中國學術期刊 (光盤版) 電子雜誌社en_US
dc.rights© 2016 China Academic Journal Electronic Publishing House. It is to be used strictly for educational and research use.en_US
dc.rights© 2016 中国学术期刊电子杂志出版社。本内容的使用仅限于教育、科研之目的。en_US
dc.subjectActive contouren_US
dc.subjectBuilding extractionen_US
dc.subjectLevel set methoden_US
dc.subjectObject extractionen_US
dc.subjectPartial differential equationen_US
dc.subjectNonlinear diffusionen_US
dc.subjectRoad extractionen_US
dc.titlePartial differential equation-based object extraction from remote sensing imageryen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage257en_US
dc.identifier.epage262en_US
dc.identifier.volume35en_US
dc.identifier.issue3en_US
dc.identifier.doi10.11972/j.issn.1001-9014.2016.03.001en_US
dcterms.abstractObject extraction is an essential task in remote sensing and geographical sciences. Previous studies mainly focused on the accuracy of object extraction method while little attention has been paid to improving their computational efficiency. For this reason,a partial differential equation( PDE)-based framework for semi-automated extraction of multiple types of objects from remote sensing imagery was proposed. The mathematical relationships among the traditional PDE-based methods,i. e.,level set method( LSM),nonlinear diffusion( NLD),and active contour( AC) were explored. It was found that both edge-and region-based PDEs are equally important for object extraction and they are generalized into a unified framework based on the derived relationships. For computational efficiency,the widely used curvature-based regularizing term is replaced by a scale space filtering. The effectiveness and efficiency of the proposed methods were corroborated by a range of promising experiments.en_US
dcterms.abstract從遙感圖像中提取感興趣的目標是遙感和地學領域的一個重要任務.先前的研究主要集中于目標提取的精度,而很少關注目標提取的效率.因此,作者提出一個基于偏微分方程的框架來進行半自動多類目標提取.首先,作者對水平集方法,非線性擴散,以及活動輪廓之間的數學關系進行了深入的探究.從探究的結果作者發現基于邊緣和基于區域的偏微分方程在目標提取中同等重要,因此作者把它們概括成一個統一的框架.接著,為了使計算更加高效,作者用尺度空間濾波替換傳統的曲率歸一項.最后,作者通過一系列實驗證明了該方法的有效性.en_US
dcterms.accessRightsopen accessen_US
dcterms.alternative基于偏微分方程的遙感圖像目標提取en_US
dcterms.bibliographicCitation紅外與毫米波學報 (Journal of infrared and millimeter waves), Jun. 2016, v. 35, no. 3, p. 257-262en_US
dcterms.isPartOf紅外與毫米波學報 (Journal of infrared and millimeter waves)en_US
dcterms.issued2016-
dc.identifier.isiWOS:000379629300001-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRA-
dc.description.pubStatusPublisheden_US
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