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《Geospatial Information》 2014-01
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Research on Forestland Information Extraction Based on the Thematic Overlap

WANG Jing;  
This paper studied on the subcompartment division and the object-oriented forestland information extraction combining with SPOT5 image date of forestland dividing boundaries of Hunan province. Original vector data was involved in image segmentation as the thematic layer which improved the precision of automatical subcompartment extraction. And then, this paper established a generic feature-based database relative to the data under the best segmentation scales, and realized the automation of forest classii cation. Considering the limitations of the amount of image information, this paper used the information of thematic layer to expand the feature-based database and completed the more detailed classii cation of forest.
【Fund】: 国家自然科学基金资助项目(41201474)
【CateGory Index】: P237
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【Citations】
Chinese Journal Full-text Database 5 Hits
1 GAO Wei1,2,LIU Xiu-guo1,2,PENG Pan1,2,CHEN Qi-hao1,2 1.Faculty of Information Engineering,China University of Geosciences,Wuhan 430074,China 2.Engineering Research Center for GIS Software and Applications,Ministry of Education,Wuhan 430074,China;An Improved Method of High-Resolution Remote Sense Image Segmentation[J];Earth Science(Journal of China University of Geosciences);2010-03
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5 CHEN Yunhao~(1,2) FENG Tong~(2) SHI Peijun~(1) WANG Jinfei~(3)(1 College of Resources Science and Technology,Beijing Normal University,19 Xinjiekou Street,Beijing 100875,China)(2 Department of Geography & Resource Management,Chinese University of Hong Kong,Shatin,N.T.,Hong Kong)(3 Department of Geography,The University of Western Ontario,Canada,N6A 5C2);Classification of Remot Sensing Image Based on Object Oriented and Class Rules[J];Geomatics and Information Science of Wuhan University;2006-04
【Co-citations】
Chinese Journal Full-text Database 10 Hits
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【Secondary Citations】
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8 LUO Jian-cheng 1, WANG Qin-min 2, ZHOU Cheng-hu 1, LEUNG Yee 3 (1. Institute of Geographical Science and Natural Resources Research, CAS, Beijing 100101, China; 2. Fuzhou University,Fuzhou 350002,China; 3. Department of Geography, The Chinese Univers;Adaptive Resonance Theory for Classification of Remotely Sensed Image[J];Acta Geodaetica Et Cartographic Sinica;2002-02
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