Land-use classification using ASTER data and self-organized neutral networks |
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| Affiliation: | 1. Civil Protection Agency, Castilla y León Government, Valladolid, Spain;2. Agrarian Engineering and Sciences Department, University of León, Campus of Ponferrada, León, Spain;3. Electronic Technology Department, University of Valladolid, Spain;4. Sustainable Forest Management Research Institute, University of Valladolid-INIA, Spain;1. State Key Laboratory of Lithospheric Evolution, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China;2. College of Earth Sciences, University of Chinese Academy of Sciences, Beijing 100049, China;3. Department of Earth Sciences, University of Yaoundé I, P.O. Box 812, Yaoundé, Cameroon;4. CAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing 100101, China |
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| Abstract: | Operationally AVHRR and TM/TM+ data were used and a supervised maximum likelihood classification (MLH) was applied to depict land use changes in Beijing, providing basic maps for planning and development. With rapid growth of the city these are helpful to deal with higher resolution data, whereas new classification algorithms produce land use maps more accurate. In the paper, new sensor ASTER data and the Kohonen self-organized neural network feature map (KSOM) were tested.The TSOM classified 7% more accurately than the maximum likelihood algorithm in general, and 50% more accurately for the classes ‘residential area’ and ‘roads’. The results suggest that ASTER data and the Kohonen self-organized neural network classification can be used as an alternative data and method in a land use update operational system. |
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| Keywords: | Land use Operationally system ASTER data KSOM |
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