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An analysis of the gap between hybrid and real data for volcanic deformation detection

Beker, Teo and Song, Qian and Zhu, Xiao Xiang (2023) An analysis of the gap between hybrid and real data for volcanic deformation detection. In: International Geoscience and Remote Sensing Symposium (IGARSS), pp. 825-828. IGARSS 2023, 16-21 Jul 2023, Pasadena, USA. doi: 10.1109/IGARSS52108.2023.10281964.

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Official URL: https://ieeexplore.ieee.org/document/10281964


Recently deep learning models were applied to detect fast short-term volcanic deformations using interferometric synthetic aperture radar (InSAR) data. However, volcanic deformation detection is limited by the availability of real positive samples. In previous work, we used hybrid synthetic-real InSAR deformation maps set to train an InceptionResNet v2 model capable of detecting deformations down to 5 mm/year in real set. However, our model also reported false positive detections. One possible reason is the data distribution gap between the real and hybrid sets. In this paper, an experiment is conducted to analyze the gap between the hybrid and real sets that resulted in false positives. Three subsets of the fine-tuning set are created based on t-SNE analysis using different sampling strategies. The classification model is fine-tuned using these subsets. The results show that the strategy of removing only the most confusing examples and keeping the larger data set size reduces the false positive rate from 32.29% to 27.01%.

Item URL in elib:https://elib.dlr.de/195827/
Document Type:Conference or Workshop Item (Speech)
Title:An analysis of the gap between hybrid and real data for volcanic deformation detection
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Beker, TeoUNSPECIFIEDhttps://orcid.org/0000-0003-1907-4045UNSPECIFIED
Song, QianUNSPECIFIEDhttps://orcid.org/0000-0003-2746-6858UNSPECIFIED
Zhu, Xiao XiangUNSPECIFIEDhttps://orcid.org/0000-0001-5530-3613UNSPECIFIED
Date:13 January 2023
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
Page Range:pp. 825-828
Keywords:Volcano Deformation Detection, t-SNE, XAI, Data Gap
Event Title:IGARSS 2023
Event Location:Pasadena, USA
Event Type:international Conference
Event Dates:16-21 Jul 2023
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: Beker, Teo
Deposited On:07 Jul 2023 10:07
Last Modified:24 Nov 2023 18:20

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