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In this paper, an approach is proposed to fuse LiDAR and hyperspectral data,which considers both spectral and spatial information in a single framework.Here, an extended self-dual attribute profile (ESDAP) is investigated toextract spatial information from a hyperspectral data set. To extract spectralinformation, a few well-known classifiers have been used such as support vectormachines (SVMs), random forests (RFs), and artificial neural networks (ANNs).The proposed method accurately classify the relatively volumetric data set in afew CPU processing time in a real ill-posed situation where there is no balancebetween the number of training samples and the number of features. Theclassification part of the proposed approach is fully-automatic.

Integration of LiDAR and Hyperspectral Data for Land-cover Classification: A Case Study