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Patch-level unsupervised planetary change detection

Saha, Sudipan and Zhu, Xiao Xiang (2022) Patch-level unsupervised planetary change detection. IEEE Geoscience and Remote Sensing Letters. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/LGRS.2021.3130862. ISSN 1545-598X. (In Press)

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Official URL: https://ieeexplore.ieee.org/document/9627685

Abstract

Change detection (CD) is critical for analyzing data collected by planetary exploration missions, e.g., for identification of new impact craters. However, CD is still a relatively new topic in the context of planetary exploration. Sheer variation of planetary data makes CD much more challenging than in the case of Earth observation (EO). Unlike CD for EO, patch-level decision is preferred in planetary exploration as it is difficult to obtain perfect pixelwise alignment/co-registration between the bi-temporal planetary images. Lack of labeled bi-temporal data impedes supervised CD. To overcome these challenges, we propose an unsupervised CD method that exploits a pre-trained feature extractor to obtain bi-temporal deep features that are further processed using global max-pooling to obtain patch-level feature description. Bi-temporal patch-level features are further analyzed based on difference to determine whether a patch is changed. Additionally, a self-supervised method is proposed to estimate the decision boundary between the changed and unchanged patches. Experimental results on three planetary CD datasets from two different planetary bodies (Mars and Moon) demonstrate that the proposed method often outperforms supervised planetary CD methods. Code is available at https://gitlab.lrz.de/ai4eo/cd/-/tree/main/planetaryCDUnsup.

Item URL in elib:https://elib.dlr.de/145750/
Document Type:Article
Title:Patch-level unsupervised planetary change detection
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Saha, Sudipansudipan.saha (at) tum.deUNSPECIFIED
Zhu, Xiao Xiangxiaoxiang.zhu (at) dlr.dehttps://orcid.org/0000-0001-5530-3613
Date:2022
Journal or Publication Title:IEEE Geoscience and Remote Sensing Letters
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
DOI :10.1109/LGRS.2021.3130862
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:1545-598X
Status:In Press
Keywords:unsupervised learning, earth Observation, change detection ai4eo
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:19 Nov 2021 09:19
Last Modified:20 Dec 2021 18:29

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