Fusion of Multispectral LiDAR and Hyperspectral Imagery

This paper presents a technique for the fusion of multispectral LiDAR and hyperspectral data. The proposed method is based on the fusion of the features of multispectral LiDAR and hyperspectral data projected in two different subspaces. First, the spatial features are extracted from both data using morphological filters. Then, the fused features are estimated by proposing a novel constraint penalized cost function. The estimated fused features are used for the purpose of mapping. The classification accuracies obtained by applying a random forest classifier on the fused data confirm considerable improvements compared with the other methods used in the experiments.