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| Remote Sensing Classification of Land Use Based on Image Fusion in the Loess Hilly Region of Northern Shaanxi Province |
| Liu Yongmei1,2,Li Rui1,Yang Qinke1 |
| 1 Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, 712100, Yangling, Shaanxi; 2 Department of Urban and Resource Sciences, Northwest University, 710069, Xi'an:China |
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Abstract The land use classification accuracy is unsatisfactory based on single remotely sensed data and supervised classification in the land use investigation of loess hill and gully area. Taking the watershed of WuDing River of Northern Shaanxi Province as a test area, the TM multi-spectral data and SPOT pan data are merged by the method of Principal Components Analysis. Then, based on the merged image, the land use categories are extracted by applying an integration of supervised classification and unsupervised classification, which improved sampling method remarkably. The total accuracy increased from 82.0% to 89.2%, especially the accuracy of city and town area, paddy field, water area increased over 10%, the mixture of sloping field and forest (grassland) decreased remarkably and the accuracy of the two categories increased over 5% respectively by the combination of two methods, compared to the classification based on the single TM multi-spectral data and supervised classification. The result is of critical significance in land use dynamic monitoring in the area.
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Received: 27 May 2004
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