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- W345840855 abstract "In comparison to traditional pixel-based classifiers object-oriented classification approaches can include information of shape, context and multiple scales for improving classification results. In this study polarimetric and multifrequency high-resolution E-SAR data and hyperspectral HyMapTM data were used to map an agricultural area in southern Germany with the object-oriented techniques provided by eCognition. An advanced rule base was defined to derive different basic land use classes and map a high number of several crop types in both datasets. The overall accuracy of the E-SAR classification was determined with 88% . In comparison, the accuracy of the HyMapTM classification is 5% lower, but more classes with a higher separability could be distinguished. In a fused data analysis the different advantages of each dataset were combined for an improved classification result. In contrast to conventional pixel-based classifications the presented object-based results show comparable accuracies but a more homogenous mapping product with advances in their further processing and analysis in Geographic Information Systems (GIS)." @default.
- W345840855 created "2016-06-24" @default.
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- W345840855 date "2003-12-29" @default.
- W345840855 modified "2023-09-23" @default.
- W345840855 title "Object-oriented land use classification of polarimetric high-resolution SAR and hyperspectral HymapReg. data" @default.
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