Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W1, 61-66, 2013
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-7-W1/61/2013/
doi:10.5194/isprsarchives-XL-7-W1-61-2013
© Author(s) 2013. This work is distributed
under the Creative Commons Attribution 3.0 License.
 
12 Jul 2013
STUDY ON THE TECHNOLOGY AND METHOD OF LAND COVER CLASSIFICATION FOR GEOGRAPHIC NATIONAL CONDITIONS SURVEYING
Y. Jia1,2, H. T. Li2, H. Y. Gu2, and Y. S. Han2 1Institute of Surveying and Geography Science, Liaoning University of Engineering and Technology, Fuxin, China
2Chinese Academy of Surveying and Mapping, Beijing, China
Keywords: Land cover, Classification technology,Geographic national conditions surveying,Classification strategy Abstract. Land Cover is the basis of geographic national conditions monitoring, extracting land cover information timely and accurately has become one of important tasks in the geographic national conditions surveying project. For the current situation of complex land cover type and large amount of data, there has emerged various new classification techniques and methods. However, the big difficult of classification,the large amount of data, the heavy workload of post-editing and other factors have seriously hampered the progress of the project. In this paper, it chooses high-resolution remote sensing image as original data, comprehensivly elaborates present research situation of oriented land cover classification. By the systematical analysis and summary of the basic and key problems of the land cover classification technology, relying on the geographic national information classification and standard system, discusses the available methods preliminarily to improve the accuracy of land cover classification which based on geographic national conditions surveying.
Conference paper (PDF, 521 KB)


Citation: Jia, Y., Li, H. T., Gu, H. Y., and Han, Y. S.: STUDY ON THE TECHNOLOGY AND METHOD OF LAND COVER CLASSIFICATION FOR GEOGRAPHIC NATIONAL CONDITIONS SURVEYING, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W1, 61-66, doi:10.5194/isprsarchives-XL-7-W1-61-2013, 2013.

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