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Fusing Multi-modal Data for Supervised Change Detection

Ebel, Patrick and Saha, Sudipan and Zhu, Xiao Xiang (2021) Fusing Multi-modal Data for Supervised Change Detection. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII, pp. 243-249. ISPRS. ISPRS 2021, 04 - 10 July 2021, Nice, France / Virtual. doi: 10.5194/isprs-archives-XLIII-B3-2021-243-2021. ISSN 1682-1750.

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Official URL: https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2021/243/2021/isprs-archives-XLIII-B3-2021-243-2021.pdf

Abstract

With the rapid development of remote sensing technology in the last decade, different modalities of remote sensing data recorded via a variety of sensors are now easily accessible. Different sensors often provide complementary information and thus a more detailed and accurate Earth observation is possible by integrating their joint information. While change detection methods have been traditionally proposed for homogeneous data, combining multi-sensor multi-temporal data with different characteristics and resolution may provide a more robust interpretation of spatio-temporal evolution. However, integration of multi-temporal information from disparate sensory sources is challenging. Moreover, research in this direction is often hindered by a lack of available multi-modal data sets. To resolve these current shortcomings we curate a novel data set for multi-modal change detection. We further propose a novel Siamese architecture for fusion of SAR and optical observations for multi-modal change detection, which underlines the value of our newly gathered data. An experimental validation on the aforementioned data set demonstrates the potentials of the proposed model, which outperforms common mono-modal methods compared against.

Item URL in elib:https://elib.dlr.de/142284/
Document Type:Conference or Workshop Item (Other)
Title:Fusing Multi-modal Data for Supervised Change Detection
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Ebel, PatrickTUMUNSPECIFIED
Saha, SudipanTU MünchenUNSPECIFIED
Zhu, Xiao Xiangxiao.zhu (at) dlr.dehttps://orcid.org/0000-0001-5530-3613
Date:July 2021
Journal or Publication Title:The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:No
Volume:XLIII
DOI :10.5194/isprs-archives-XLIII-B3-2021-243-2021
Page Range:pp. 243-249
Publisher:ISPRS
ISSN:1682-1750
Status:Published
Keywords:multi-data, fusion, supervised, change detection
Event Title:ISPRS 2021
Event Location:Nice, France / Virtual
Event Type:international Conference
Event Dates:04 - 10 July 2021
Organizer:ISPRS
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: Bratasanu, Ion-Dragos
Deposited On:21 May 2021 16:17
Last Modified:25 Aug 2021 09:47

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