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Hyperspectral and LiDAR Data Fusion Using Extinction Profiles and Deep Convolutional Neural Network

Ghamisi, Pedram and Höfle, Bernhard and Zhu, Xiao Xiang (2017) Hyperspectral and LiDAR Data Fusion Using Extinction Profiles and Deep Convolutional Neural Network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10 (6), pp. 3011-3024. IEEE - Institute of Electrical and Electronics Engineers. DOI: 10.1109/JSTARS.2016.2634863 ISSN 1939-1404

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Official URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7786851

Abstract

This paper proposes a novel framework for the fusion of hyperspectral and light detection and ranging-derived rasterized data using extinction profiles (EPs) and deep learning. In order to extract spatial and elevation information from both the sources, EPs that include different attributes (e.g., height, area, volume, diagonal of the bounding box, and standard deviation) are taken into account. Then, the derived features are fused via either Feature stacking or graph-based feature fusion. Finally, the fused Features are fed to a deep learning-based classifier (convolutional neural network with logistic regression) to ultimately produce the classification map. The proposed approach is applied to two datasets acquired in Houston, TX, USA, and Trento, Italy. Results indicate that the proposed approach can achieve accurate classification results compared to other approaches. It should be noted that, in this paper, the concept of deep learning has been used for the first time to fuse LiDAR and hyperspectral features, which provides new opportunities for further research.

Item URL in elib:https://elib.dlr.de/109123/
Document Type:Article
Title:Hyperspectral and LiDAR Data Fusion Using Extinction Profiles and Deep Convolutional Neural Network
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Ghamisi, Pedramdlr-imf/tum-lmfUNSPECIFIED
Höfle, Bernharduniversität heidelbergUNSPECIFIED
Zhu, Xiao Xiangtum,dlrUNSPECIFIED
Date:June 2017
Journal or Publication Title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:10
DOI :10.1109/JSTARS.2016.2634863
Page Range:pp. 3011-3024
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:1939-1404
Status:Published
Keywords:Convolutional neural network, deep learning, extinction profile, graph-based feature fusion, hyperspectral, LiDAR, random forest, support vector machines
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:07 Dec 2016 12:29
Last Modified:31 Jul 2019 20:06

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