Supervised Classification of RADARSAT-2 Polarimetric Data for Different Land Features
This work addresses land feature classification for remote sensing applications, but it is incremental as it compares existing methods on specific data.
The study tackled land feature classification using RADARSAT-2 polarimetric data, comparing supervised Wishart and SVM classifiers, and found that RADARSAT-2's full polarimetry offers better contrast than conventional methods.
The pixel percentage belonging to the user defined area that are assigned to cluster in a confusion matrix for RADARSAT-2 over Vancouver area has been analysed for classification. In this study, supervised Wishart and Support Vector Machine (SVM) classifiers over RADARSAT-2 (RS2) fine quadpol mode Single Look Complex (SLC) product data is computed and compared. In comparison with conventional single channel or dual channel polarization, RADARSAT-2 is fully polarimetric, making it to offer better land feature contrast for classification operation.