Regional Land Cover Image Classification and AccuracyEvaluation Using MODIS Data
基于MODIS遥感数据的宏观土地覆盖特征分类方法与精度分析研究

Based on the advantage of MODIS multi-spectrum data, this research explored a classificationmethod of feature selection and extraction, which combines the multi-spectrum data with multi-temporarydata in order to improve the classification accuracy. The classification accuracy was tested using 250 mMODIS data in Shandong province of China. The classification features were selected and extractedthrough the measures of the fractional cover, moisture, soil brightness, land surface temperature per day,and textures of various land cover types.The result indicates that it has higher classification accuracy using EVI (Enhanced Vegetation Index)as input than NDVI (Normalized Difference Vegetation Index), and NDWI (Normalized Difference WaterIndex) is superior to NDMI (Normalized Difference Moisture Index).The homogeneity of texture is thebest one for feature selection among the eight textures, and the optimal window size of texture is 11×11pixels. Whereas, NDSI (Normalized Difference Soil Index) almost has no effect for improving theclassification accuracy. For the contribution on improving the classification accuracy, EVI is the most, andthe following are homogeneity, NDWI and Tday(land surface temperature of day). The overall accuracyincreased about 10% through the method. The result shows that the feature selection and extraction canobviously improve classification accuracy, and the relatively high classification accuracy can also beacquired using the MODIS data sets without accessorial knowledge by this method.