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Multisensor Earth Observation Image Classification Based on a Multimodal Latent Dirichlet Allocation Model

Bahmanyar, Reza and Espinoza-Molina, Daniela and Datcu, Mihai (2018) Multisensor Earth Observation Image Classification Based on a Multimodal Latent Dirichlet Allocation Model. IEEE Geoscience and Remote Sensing Letters, 15 (3), pp. 459-463. IEEE - Institute of Electrical and Electronics Engineers. DOI: 10.1109/LGRS.2018.2794511 ISSN 1545-598X

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Official URL: http://ieeexplore.ieee.org/document/8278834/


Many previous researches have already shown the advantages of multisensor land-cover classification. Here, we propose an innovative land-cover classification approach based on learning a joint latent model of synthetic aperture radar (SAR) and multispectral satellite images using multimodal latent Dirichlet allocation (mmLDA), a probabilistic generative model. It has already been successfully applied to various other problems dealing with multimodal data. For our experiments, we chose overlapping SAR and multispectral images of two regions of interest. The images were tiled into patches and their local primitive features were extracted. Then each image patch is represented by SAR and multispectral bag-of-words (BoW) models. The BoW values are both fed to the mmLDA, resulting in a joint latent data model. A qualitative and quantitative validation of the topics based on ground-truth data demonstrate that the land-cover categories of the regions are correctly classified, outperforming the topics obtained using individual single modality data.

Item URL in elib:https://elib.dlr.de/119199/
Document Type:Article
Title:Multisensor Earth Observation Image Classification Based on a Multimodal Latent Dirichlet Allocation Model
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Bahmanyar, Rezareza.bahmanyar (at) dlr.deUNSPECIFIED
Espinoza-Molina, Danieladaniela.espinozamolina (at) dlr.deUNSPECIFIED
Datcu, Mihaimihai.datcu (at) dlr.deUNSPECIFIED
Date:March 2018
Journal or Publication Title:IEEE Geoscience and Remote Sensing Letters
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:Yes
DOI :10.1109/LGRS.2018.2794511
Page Range:pp. 459-463
Publisher:IEEE - Institute of Electrical and Electronics Engineers
Keywords:Image fusion, Land-cover classification, Multimodal latent Dirichlet allocation (mmLDA), Multispectral images, Synthetic aperture radar (SAR) images
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 > Photogrammetry and Image Analysis
Deposited By: Bahmanyar, Gholamreza
Deposited On:06 Mar 2018 12:24
Last Modified:31 Jul 2019 20:16

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