CLASSIFIER FUSION OF POLSAR, HYPERSPECTRAL AND PAN REMOTE SENSING DATA FOR IMPROVING LAND USE CLASSIFICATION

Abstract. The combined use of PolSAR and hyperspectral data can improve the classification accuracy. This paper proposes a new classification approach for combining use of PolSAR and hyperspectral image data sets. At the first step, polarization signature is generated from coherency matrix of PolSAR image data. In the second step, in order to improve spatial resolution, the Hyperion image was pan-sharped with the ALI Pan image. In the third step, the Random Forest (RF) classifier is used for classifying PolSAR and hyperspectral data sets in five different classes including: Water (Wa), urban area (Ur), vegetation (Vg), road (Ro), and soil (So). Then, in order to fuse the output of RF for incorporated two data sets, simple majority voting (MV) and weighted majority voting (WMV) methods are used. Three UAVSAR, Hyperion and ALI images that acquired on April 2015 was chosen for this study. The results showed the ability of the polarimetric data for classifying urban and vegetation, and hyperspectral images for water, soil and road classes. Also, the combination of two data sets by using of WMV method causes the improvements of the classification performance.