Integration of Heterogeneous Features from Co-registered Hyperspectral and LiDAR Data for Land Cover Classification

Exploiting the multi-source data is an effective but challenging problem for land cover classification. Popular remote sensor data, e.g., hyperspectral (HS) and light detection and ranging (LiDAR), contain complementary information for land cover if they are co-registered. In this paper, we aim to integrate information extracted from these data sources for land cover classification. At first, we propose a novel feature extraction method by calculating the inverse coefficient of variation (ICV) using the Gaussian probability of neighbourhood between every pair of bands in HS data. This is calculated for each band with respect to every other band to form an ICV cube. We reduce the number of planes in the cube by applying principal component analysis (PCA) on it and spatial features are then extracted for significant principal components. The spectral information from HS data, their ICV responses, and spatial information from ICV responses have complementary information; that is why we fuse them together by layer stacking to generate discriminant features. Secondly, we also derive height and spatial features from LiDAR Digital Elevation Model (DSM), which are later concatenated with the HS derived features. Finally, these features are classified using linear discriminant analysis (LDA) classifier. The classification results prove the effectiveness of the derived features from both data sources.