Classification of Modis Spectral and Angular Signatures Using Decision Tree Algorithm
نویسندگان
چکیده
It is important to explore the ability of Bidirectional Reflectance Distribution Function (BRDF) to improve classification accuracy using MODerate Resolution Imaging Spectroradiometer (MODIS) BRDF and albedo products. This paper examines the utility of including MODIS BRDF products as additional input features to a decision tree classifier (c4.5). Our results show that supplementing MODIS BRDF parameters (f_vol and f_geo) with Nadir BRDF-Adjusted Reflectances (NBAR) and Enhanced Vegetation Indexes (EVI) increases overall classification accuracy by 3.02% to 4.72%, and reduces misclassification rates by 15% to 22%, depending on how many BRDF parameters are added (fourteen vs. four), and if these BRDF parameters are normalized by their isotropic parameters (f_iso). The greatest improvements are seen for Wetland shrub with user and producer’s accuracy increased by up to 15.05% and 8.18% respectively. Increases on the order of 5% to 10% are encountered for the Wetland tree, Coniferous dense and Coniferous open with no detriments to other candidate classes. However, adding MODIS BRDF shape indicators produces little improvements in classification accuracy in this study.
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