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Proceedings Paper

Research on land use/cover change of Wuhan based on object oriented image interpretation method
Author(s): Haiyan Zhu; Aiwen Lin
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Paper Abstract

Higher resolution remote sensor has already become the significant instrument to obtain the change information quickly, veraciously, and comprehensively. Two different remote sensing images are used to obtain the change information by using an object oriented analyzing method in Land Use/Cover Change. Data is gathered according to the images' resolution, characteristic, size and texture property of the earth surface objects. Meanwhile, the color, shape, smoothness, and compactness of the images are reviewed to form many-adjacent-pixel objects which contain more semantic information. Combined with the knowledge of land use classification and loading classifier of discriminate class, a characteristic space is defined to establish a knowledge base. Two or three different typical features are chosen to serve as the training samples, and a data base could be completed successfully. Non-segmented or error-segmented objects are adjusting into correct ones favorably. Analyzing the data, only the areas of the lands for construction purpose and reservoir and bottomland have been increased in the whole city, while the areas of arable land, river and lake land, woodland, grassland, and not exploited or developed land have been decreased in more or less degree, which means that a tendency to expansion has become more and more significant.

Paper Details

Date Published: 3 November 2008
PDF: 8 pages
Proc. SPIE 7144, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: The Built Environment and Its Dynamics, 71440A (3 November 2008); doi: 10.1117/12.812700
Show Author Affiliations
Haiyan Zhu, Wuhan Univ. (China)
Aiwen Lin, Wuhan Univ. (China)

Published in SPIE Proceedings Vol. 7144:
Geoinformatics 2008 and Joint Conference on GIS and Built Environment: The Built Environment and Its Dynamics
Lin Liu; Xia Li; Kai Liu; Xinchang Zhang; Xinhao Wang, Editor(s)

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