  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 to
extract spatial information from a hyperspectral data set. To extract spectral
information, a few well-known classifiers have been used such as support vector
machines (SVMs), random forests (RFs), and artificial neural networks (ANNs).
The proposed method accurately classify the relatively volumetric data set in a
few CPU processing time in a real ill-posed situation where there is no balance
between the number of training samples and the number of features. The
classification part of the proposed approach is fully-automatic.
