DLR-Logo -> http://www.dlr.de
DLR Portal Home | Imprint | Privacy Policy | Contact | Deutsch
Fontsize: [-] Text [+]

An Overview of Multimodal Remote Sensing Data Fusion: From Image to Feature, from Shallow to Deep

Hong, Danfeng and Chanussot, Jocelyn and Zhu, Xiao Xiang (2021) An Overview of Multimodal Remote Sensing Data Fusion: From Image to Feature, from Shallow to Deep. In: International Geoscience and Remote Sensing Symposium (IGARSS), pp. 1245-1248. IGARSS 2021, 11.-16.7.21, Brussels, virtuell. doi: 10.1109/IGARSS47720.2021.9554255.

[img] PDF - Only accessible within DLR bis July 2022

Official URL: https://ieeexplore.ieee.org/document/9554255


With the ever-growing availability of different remote sens-ing (RS) products from both satellite and airborne platforms,simultaneous processing and interpretation of multimodal RSdata have shown increasing significance in the RS field. Dif-ferent resolutions, contexts, and sensors of multimodal RSdata enable the identification and recognition of the materialslying on the earth’s surface at a more accurate level by de-scribing the same object from different points of the view. Asa result, the topic on multimodal RS data fusion has graduallyemerged as a hotspot research direction in recent years.This paper aims at presenting an overview of multimodalRS data fusion in several mainstream applications, which canbe roughly categorized by 1) image pansharpening, 2) hyper-spectral and multispectral image fusion, 3) multimodal fea-ture learning, and (4) crossmodal feature learning. For eachtopic, we will briefly describe what is the to-be-addressed re-search problem related to multimodal RS data fusion and givethe representative and state-of-the-art models from shallow todeep perspectives.

Item URL in elib:https://elib.dlr.de/146240/
Document Type:Conference or Workshop Item (Speech)
Title:An Overview of Multimodal Remote Sensing Data Fusion: From Image to Feature, from Shallow to Deep
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Hong, DanfengDanfeng.Hong (at) dlr.deUNSPECIFIED
Chanussot, JocelynInstitute Nationale Polytechnique de GrenobleUNSPECIFIED
Zhu, Xiao Xiangxiaoxiang.zhu (at) dlr.dehttps://orcid.org/0000-0001-5530-3613
Journal or Publication Title:International Geoscience and Remote Sensing Symposium (IGARSS)
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:No
DOI :10.1109/IGARSS47720.2021.9554255
Page Range:pp. 1245-1248
Keywords:Classification, crossmodal, data fusion,deep learning, feature learning, multimodal, pansharpening,remote sensing, shallow models
Event Title:IGARSS 2021
Event Location:Brussels, virtuell
Event Type:international Conference
Event Dates:11.-16.7.21
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 - Artificial Intelligence
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Rösel, Anja
Deposited On:29 Nov 2021 08:02
Last Modified:01 Dec 2021 11:12

Repository Staff Only: item control page

Help & Contact
electronic library is running on EPrints 3.3.12
Copyright © 2008-2017 German Aerospace Center (DLR). All rights reserved.