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Self-Supervised Multisensor Change Detection

Saha, Sudipan and Ebel, Patrick and Zhu, Xiao Xiang (2022) Self-Supervised Multisensor Change Detection. IEEE Transactions on Geoscience and Remote Sensing. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/TGRS.2021.3109957. ISSN 0196-2892. (In Press)

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Official URL: https://ieeexplore.ieee.org/document/9538396

Abstract

Most change detection (CD) methods assume that prechange and postchange images are acquired by the same sensor. However, in many real-life scenarios, e.g., natural disasters, it is more practical to use the latest available images before and after the occurrence of incidence, which may be acquired using different sensors. In particular, we are interested in the combination of the images acquired by optical and synthetic aperture radar (SAR) sensors. SAR images appear vastly different from the optical images even when capturing the same scene. Adding to this, CD methods are often constrained to use only target image-pair, no labeled data, and no additional unlabeled data. Such constraints limit the scope of traditional supervised machine learning and unsupervised generative approaches for multisensor CD. The recent rapid development of self-supervised learning methods has shown that some of them can even work with only few images. Motivated by this, in this work, we propose a method for multisensor CD using only the unlabeled target bitemporal images that are used for training a network in a self-supervised fashion by using deep clustering and contrastive learning. The proposed method is evaluated on four multimodal bitemporal scenes showing change, and the benefits of our self-supervised approach are demonstrated. Code is available at https://gitlab.lrz.de/ai4eo/cd/-/tree/main/sarOpticalMultisensorTgrs2021.

Item URL in elib:https://elib.dlr.de/145752/
Document Type:Article
Title:Self-Supervised Multisensor Change Detection
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Saha, Sudipansudipan.saha (at) tum.deUNSPECIFIED
Ebel, Patrickpatrick.ebel (at) tum.deUNSPECIFIED
Zhu, Xiao Xiangxiao.zhu (at) dlr.dehttps://orcid.org/0000-0001-5530-3613
Date:2022
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
DOI :10.1109/TGRS.2021.3109957
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:0196-2892
Status:In Press
Keywords:Optical sensors , Optical imaging , Training , Earth , Synthetic aperture radar , Deep learning , Spatial resolution
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 - Remote Sensing and Geo Research
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Rösel, Anja
Deposited On:18 Nov 2021 13:15
Last Modified:14 Jan 2022 15:26

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