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Spatial Context Awareness for Unsupervised Change Detection in Optical Satellite Images

Kondmann, Lukas and Toker, Aysim and Saha, Sudipan and Schölkopf, Bernhard and Leal-Taixé, Laura and Zhu, Xiao Xiang (2022) Spatial Context Awareness for Unsupervised Change Detection in Optical Satellite Images. IEEE Transactions on Geoscience and Remote Sensing. IEEE - Institute of Electrical and Electronics Engineers. ISSN 0196-2892. (In Press)

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Abstract

Detecting changes on the ground in multitemporal Earth observation data is one of the key problems in remote sensing. In this paper, we introduce Sibling Regression for Optical Change detection (SiROC), an unsupervised method for change detection in optical satellite images with medium and high resolution. SiROC is a spatial context-based method that models a pixel as a linear combination of its distant neighbors. It uses this model to analyze differences in the pixel and its spatial context-based predictions in subsequent time periods for change detection. We combine this spatial context-based change detection with ensembling over mutually exclusive neighborhoods and transitioning from pixel to object-level changes with morphological operations. SiROC achieves competitive performance for change detection with medium-resolution Sentinel-2 and high-resolution Planetscope imagery on four datasets. Besides accurate predictions without the need for training, SiROC also provides a well-calibrated uncertainty of its predictions.

Item URL in elib:https://elib.dlr.de/145630/
Document Type:Article
Title:Spatial Context Awareness for Unsupervised Change Detection in Optical Satellite Images
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Kondmann, LukasLukas.Kondmann (at) dlr.deUNSPECIFIED
Toker, AysimTechnical University of MunichUNSPECIFIED
Saha, SudipanTechnical University of MunichUNSPECIFIED
Schölkopf, BernhardMax Planck Institute for Intelligent SystemsUNSPECIFIED
Leal-Taixé, LauraTechnical University of MunichUNSPECIFIED
Zhu, Xiao Xiangxiao.zhu (at) dlr.deUNSPECIFIED
Date:2022
Journal or Publication Title:IEEE Transactions on Geoscience and Remote Sensing
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:0196-2892
Status:In Press
Keywords:Change Detection, unsupervised, optical images, multitemporal, urban analysis
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: Kondmann, Lukas
Deposited On:19 Nov 2021 09:21
Last Modified:23 Nov 2021 14:33

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