Fusion of hyperspectral and LiDAR data using random feature selection and morphological attribute profiles

Hyperspectral imagery provides detailed information about land-cover materials over a wide spectral range. Land-cover classification using hyperspectral data has been an active topic of research. Elevation data from light detection and ranging (LiDAR) can aid the classification process in discriminating complex classes. Fusion of hyperspectral and LiDAR data has been investigated in the past where the goal was to extract features from both sources and combine them to improve the accuracy of land-cover classification. In this paper, we present a new fusion approach based on random feature selection (RFS) and morphological attribute profiles (AP). Our experimental study, conducted on a hyperspectral image and digital surface model (DSM) derived from first return LiDAR data collected over the Samford ecological research facility, Queensland, Australia indicate that the proposed approach yields excellent classification results.