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/ | |||||||||||||||
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Document Type: | Article | |||||||||||||||
Title: | Fusion of Hyperspectral and LiDAR Data Using Sparse and Low-Rank Component Analysis | |||||||||||||||
Authors: |
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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 - Earth Observation | |||||||||||||||
DLR - Research theme (Project): | R - Vorhaben hochauflösende Fernerkundungsverfahren (old) | |||||||||||||||
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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