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Fusion of Hyperspectral and LiDAR Data Using Sparse and Low-Rank Component Analysis

Rasti, Behnood and Ghamisi, Pedram and Plaza, Javier and Plaza, Antonio (2017) Fusion of Hyperspectral and LiDAR Data Using Sparse and Low-Rank Component Analysis. IEEE Transactions on Geoscience and Remote Sensing, 55 (11), pp. 6354-6365. IEEE - Institute of Electrical and Electronics Engineers. DOI: 10.1109/TGRS.2017.2726901 ISSN 0196-2892

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Official URL: http://ieeexplore.ieee.org/document/8000656/

Abstract

The availability of diverse data captured over the same region makes it possible to develop multisensor data fusion techniques to further improve the discrimination ability of classifiers. In this paper, a new sparse and low-rank technique is proposed for the fusion of hyperspectral and light detection and ranging (LiDAR)-derived features. The proposed fusion technique consists of two main steps. First, extinction profiles are used to extract spatial and elevation information from hyperspectral and LiDAR data, respectively. Then, the sparse and low-rank technique is utilized to estimate the low-rank fused features from the extracted ones that are eventually used to produce a final classification map. The proposed approach is evaluated over an urban data set captured over Houston, USA, and a rural one captured over Trento, Italy. Experimental results confirm that the proposed fusion technique outperforms the other techniques used in the experiments based on the classification accuracies obtained by random forest and support vector machine classifiers. Moreover, the proposed approach can effectively classify joint LiDAR and hyperspectral data in an ill-posed situation when only a limited number of training samples are available.

Item URL in elib:https://elib.dlr.de/115362/
Document Type:Article
Title:Fusion of Hyperspectral and LiDAR Data Using Sparse and Low-Rank Component Analysis
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Rasti, Behnoodkeilir institute of technologyUNSPECIFIED
Ghamisi, Pedramdlr-imf/tum-lmfUNSPECIFIED
Plaza, Javieruniversity of extremaduraUNSPECIFIED
Plaza, Antonioaplaza (at) unex.esUNSPECIFIED
Date:3 August 2017
Journal or Publication Title:IEEE Transactions on Geoscience and Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:55
DOI :10.1109/TGRS.2017.2726901
Page Range:pp. 6354-6365
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:0196-2892
Status:Published
Keywords:Extinction profiles (EPs), feature fusion, hyperspectral, light detection and ranging (LiDAR), sparse and low-rank component analysis (SLRCA)
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > SAR Signal Processing
Deposited By: Ghamisi, Pedram
Deposited On:17 Nov 2017 14:59
Last Modified:31 Jul 2019 20:12

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