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

TL;DR

This paper presents a method that fuses LiDAR and hyperspectral data using an extended self-dual attribute profile to improve land-cover classification accuracy efficiently in complex scenarios.

Contribution

It introduces a novel framework combining spectral and spatial information with automatic classification, enhancing performance in unbalanced data situations.

Findings

Accurate classification of volumetric data sets

Efficient processing with reduced CPU time

Effective handling of unbalanced training data

Abstract

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.