Publication:
Self-Supervised Calibration of the Denoising Networks for HSI

dc.contributor.coauthorTorun O., Yuksel S.E., Erdem E.
dc.contributor.departmentDepartment of Computer Engineering
dc.contributor.kuauthorErdem, Aykut
dc.contributor.schoolcollegeinstituteCollege of Engineering
dc.date.accessioned2025-03-06T20:58:53Z
dc.date.issued2024
dc.description.abstractTypically, neural networks are trained using supervised learning (SL) and evaluated on unseen data. This type of training relies on a substantial amount of data, including clean images. However, in the case of hyperspectral images (HSIs), acquiring a large number of images along with clean versions can be challenging and expensive. This study proposes a two-stage learning strategy to train the model for HSI data with previously unseen noise patterns. The first stage involves supervised learning to train the model on noisy and clean data pairs. The second stage incorporates self-supervised calibration using only noisy data to adapt the model to specific noise patterns. For the latter, to estimate the middle spectral band, we leverage the information from its neighboring band as a target. To ensure the network learns meaningful relationships rather than merely copying the input, we strategically create a blind spot by excluding the target band from the input data. Therefore, our self-supervised learning technique is named as Blind Band Self-Supervised (BBSS) Learning. Our approach has been shown to improve the accuracy of the model for noisy HSIs, even when the network did not previously encounter the specific noise patterns in SL. © 2024 IEEE.
dc.description.indexedbyScopus
dc.description.publisherscopeInternational
dc.description.sponsoredbyTubitakEuTÜBİTAK
dc.description.sponsorshipThis project is funded by TÜBİTAK Project 123E385, and the BAGEP awards from the Science Academy to SEY.
dc.identifier.doi10.1109/IGARSS53475.2024.10640531
dc.identifier.grantnoTürkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK: 123E385
dc.identifier.isbn9798350360325
dc.identifier.quartileN/A
dc.identifier.scopus2-s2.0-85204900316
dc.identifier.urihttps://doi.org/10.1109/IGARSS53475.2024.10640531
dc.identifier.urihttps://hdl.handle.net/20.500.14288/27556
dc.keywordsCalibration
dc.keywordsHsi
dc.keywordsLearning
dc.keywordsSelf-supervised
dc.keywordsUnseen data
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofInternational Geoscience and Remote Sensing Symposium (IGARSS)
dc.subjectElectrical and electronics engineering
dc.titleSelf-Supervised Calibration of the Denoising Networks for HSI
dc.typeConference Proceeding
dspace.entity.typePublication
local.publication.orgunit1College of Engineering
local.publication.orgunit2Department of Computer Engineering
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relation.isOrgUnitOfPublication.latestForDiscovery89352e43-bf09-4ef4-82f6-6f9d0174ebae
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