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Hyperspectral and LiDAR Fusion Using Extinction Profiles and Total Variation Component Analysis

Rasti, Behnood and Ghamisi, Pedram and Gloaguen, Richard (2017) Hyperspectral and LiDAR Fusion Using Extinction Profiles and Total Variation Component Analysis. IEEE Transactions on Geoscience and Remote Sensing, 55 (7), pp. 3997-4007. IEEE - Institute of Electrical and Electronics Engineers. DOI: 10.1109/TGRS.2017.2686450 ISSN 0196-2892

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Official URL: http://ieeexplore.ieee.org/document/7902153/

Abstract

The classification accuracy of remote sensing data can be increased by integrating ancillary data provided by multisource acquisition of the same scene. We propose to merge the spectral and spatial content of hyperspectral images (HSI) with elevation information from LIDAR measurements. In the present paper, we propose to fuse the datasets using Orthogonal Total Variation Component Analysis (OTVCA). Extinction profiles (EPs) are used to automatically extract spatial and Elevation information from HSI and rasterized LiDAR features. The extracted spatial and elevation information are then fused with spectral information using the OTVCA-based feature fusion method to produce the final classification map. The extracted features have high dimension and therefore OTVCA estimates the fused features in a lower dimensional space. OTVCA also promotes piece-wise smoothness while maintaining the spatial structures. Both attributes are important to provide homogeneous regions in the final classification maps. We benchmark the proposed approach (OTVCA-fusion) with an urban datasets captured over an urban area in Houston/USA and a rural region acquired in Trento/Italy. In the experiments, OTVCA-fusion is evaluated using random forest (RF) and support vector machines (SVM) classifiers. Our experiments demonstrate the ability of OTVCA-fusion to produce accurate classification maps while using fewer features compared to other approaches investigated in this study.

Item URL in elib:https://elib.dlr.de/112025/
Document Type:Article
Title:Hyperspectral and LiDAR Fusion Using Extinction Profiles and Total Variation Component Analysis
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Rasti, BehnoodKeilir Institute of TechnologyUNSPECIFIED
Ghamisi, Pedramdlr-imf/tum-lmfUNSPECIFIED
Gloaguen, RichardHelmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource TechnologyUNSPECIFIED
Date:2017
Journal or Publication Title:IEEE Transactions on Geoscience and Remote Sensing
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:55
DOI :10.1109/TGRS.2017.2686450
Page Range:pp. 3997-4007
Editors:
EditorsEmail
Plaza, Antonio J.aplaza@unex.es
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:0196-2892
Status:Published
Keywords:Feature Fusion; Orthogonal Total Variation Component Analysis; Extinction Profiles; 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:02 May 2017 13:46
Last Modified:08 Mar 2018 18:35

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