Hyperspectral and lidar data integration and classification

Abstract

Light Detection and Ranging (LiDAR) is a technology used in different topic (mapping, urban land cover, agriculture, forestry, etc.). The great potential of LiDAR data lies in its high accuracy in the measurement of heights. Hyperspectral images, which comprise hundreds of (nearly contiguous) spectral channels, can also have spatial resolution of up to 1–5 meters per pixel. In this work, we propose to integrate both hyperspectral and LiDAR data by adding the LiDAR information to the hyperspectral data cube and correcting the geometric distortions. After arranging both data sets in the same format, we analyzed the errors obtained for each data source in order to determine if the final resolution adopted was the most appropriate one for performing data fusion. Our experimental results, in an area of Extremadura, indicate improvements in the classification after integrating the hyperspectral and LiDAR data.