Extraction And Analysis Of City Green Space Information Based On RS Image

Nowadays, with the rapid development of remote sensing technology, especially the improvement of remote image processing, remote sensing has be applied in the various social fields more and more widely increasingly. In the way of city planning, remote sensing can be used in dynamic supervision of land utilizing, monitoring and management of atmosphere quality, programming and construction of city entironment and so on. In the recent years, many cities of domestic and overseas have applied remote sensing to green space information extraction, in order to find out area green cover dynamic and optimize the spatial structure of green space. This not only can do the holistic planning of green space, but also has the effective meaning for ameliorating the entironment benefit and increasing the city's potential of continuable development.ETM RS image of shanghai of 2003 were taken as the data resources in this thesis. In the first place, do the emendation, subset, spectral enhancement and so on. Then clustering was done by unsupervised classification, supervised classification and Fuzzy C means, by which the areas of shanghai were classified and information of green space were extracted. Thirdly, take example for Putuo district, comparison and analysis of three class means were taken. The research result shows the whole effect of three mean are as follows: the FCM is the best of these three and the supervised classification is better than supervised classification.After that, analyzing of actuality of shanghai green structure was done, and development goal and construct measure were put forward on the accordance of analysis result. Finally, the shortcomings of FCM were analyzed at the end of the thesis.The characteristic and innovation of this thesis is that: Fuzzy C means, which is one of the most popular classified method at recent, is a fuzzy method suitable for soft clustering on the accordance of fuzzy set and K-means clustering. FCM is good for mixed pixel because it can compartmentalize by degree of membership. Using the method can gain the exacter result based on the factual condition of land.