AN IMPROVEMENT ON LAND USE AND LAND COVER CLASSIFICATION USING RADARSAT-2 POLARIMETRIC SAR DATA

This study proposes an Improvement on land use and land cover (LULC) classification using RADARSAT-2 polarimetric SAR (PolSAR) data. First, polarimetric decomposition is used to support the classification of PolSAR data. It is aimed at extracting polarimetric parameters related to the physical scattering mechanisms of the observed objects. Second, object-oriented image analysis is delineating image objects, as well as extracting various textural and spatial features from image objects to improve classification accuracy. Finally, a decision tree algorithm provides an efficient way to select features and implement classification. Polarimetric information has significant implications for identifying different vegetation types and distinguishing between vegetation and urban areas. Object-oriented image analysis is very helpful in reducing the effect of speckle in PolSAR images by implementing classification based on image objects, and the textural information extracted from image objects is helpful in distinguishing between water and grass. Compared with the nearest neighbor, Wishart and support vector classification, the decision tree algorithm is more efficient to select features and implement classification. Furthermore, the decision tree algorithm can provide clear classification rules that can be easily interpreted based on the physical meaning of the features used in the classification. This can provide physical insight for LULC classification using PolSAR data.