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.
@article{arxiv.1707.02642,
title = {Integration of LiDAR and Hyperspectral Data for Land-cover Classification: A Case Study},
author = {Pedram Ghamisi and Gabriele Cavallaro and Dan and Wu and Jon Atli Benediktsson and Antonio Plaza},
journal= {arXiv preprint arXiv:1707.02642},
year = {2017}
}