Improving the Accuracy of Land Cover Classification Using Fusion of Polarimetric SAR and Hyperspectral Images

In this study, various fusion methods based on feature-level and decision-level fusion of Polarimetric SAR (PolSAR) and hyperspectral images are investigated for the classification. In feature fusion, parallel feature combination method is used for classification. In decision fusion, first the individual data sources are classified separately and then several decision fusion methods are applied to fuse the classified images from each data. The results show that feature fusion has a better performance than the various decision fusion methods. The overall accuracy of feature fusion was 98.92 % which has illustrated improvement of 8 % compared to the obtained using hyperspectral data and 10 % compared to the acquired using PolSAR data.

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