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Extraction and Analysis of City Green Space Information Based on RS Image

Author: LiZuo
Tutor: GuoZhongYang
School: East China Normal University
Course: Cartography and Geographic Information Systems
Keywords: Remote sensing image clustering Supervised classification Unsupervised classification Fuzzy C-Means Clustering Shanghai Greenland
CLC: P237
Type: Master's thesis
Year: 2007
Downloads: 1008
Quote: 14
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Abstract


With the rapid development of remote sensing technology , remote sensing image processing in particular, raising the level of the application of remote sensing in all areas of society increasingly widespread . The use of remote sensing in urban planning , land use dynamic monitoring , supervision and control of air quality , urban ecological environment planning construction . In recent years , many cities at home and abroad will be applied to the remote sensing technology extracting vegetation information , covering an area of ??green space to the dynamic control to optimize the structure of green space , which can be achieved not only the overall planning of urban green space , and at the same time improve the ecological environment , improve the urban sustainable development potential has important practical significance . Papers ETM remote sensing images to Shanghai in 2003 as a data source , image correction , cropping , spectral enhancements , such as a series of pre-processing , then supervised classification , unsupervised classification and fuzzy C -means clustering three methods of Shanghai the Local Taxation object type classification , to extract green space information ; Putuo District , for example , a comparative analysis of three classifications . The results show that , from the the classification overall effect of view , fuzzy C-means classification method is better than the supervised classification , supervised classification superior to unsupervised classification . Then, according to the Shanghai greenbelt information obtained by the three methods , the Shanghai Landscaping Construction Status is analyzed based on future development goals and building measures . Finally, the Fuzzy C-Means method surviving some inadequacies . Features and innovations of papers : for mixed pixels in remote sensing image the introduced fuzzy C-means clustering method is currently the most popular , it is a combination of fuzzy set theory and K- means clustering method to fit soft by fuzzy cluster analysis method . FCM method for remote sensing image fuzziness and uncertainty can be better in accordance with the degree of membership of the mixed pixel future results , which reflect the actual situation of the surface features , to get more accurate results .

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CLC: > Astronomy,Earth Sciences > Surveying and Mapping > Photogrammetry and Surveying, Mapping and Remote Sensing > Surveying, Mapping and Remote Sensing technology
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