Kernel extreme learning machines for PolSAR image classification using spatial features

In this study, the impacts of polarimetrie and spatial features on the classification accuracy of full polarimetrie SAR (PolSAR) RADARSAT-2 data was investigated. Since PolSAR systems have the advantage of providing day-and-night and weather-independent images could provide the geo/bio-physical and structural information about the target objects hence are an important data source for remote sensing. PolSAR data includes geophysical(roughness and moisture), geometric(rotation, shape, size) and polarimetric as well as spatial information, as these information can be considered complementary. In this study, morphological features (opening and closing) were implemented to extract spatial features. Kernel based extreme learning machines (kELM) was used for data classification. Our results demonstrated that the classification accuracy is increased by 9.2% via inclusion of polarimetric and spatial features with highest classification accuracy was obtained as 82.61%.