Semantic-Fusion Gans for Semi-Supervised Satellite Image Classification

dc.contributor.authorRoy, Subhankar
dc.contributor.authorSangineto, Enver
dc.contributor.authorDemir, Begüm
dc.contributor.authorSebe, Nicu
dc.date.accessioned2020-12-28T15:26:47Z
dc.date.available2020-12-28T15:26:47Z
dc.date.issued2018-09-06
dc.description© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en
dc.description.abstractMost of the public satellite image datasets contain only a small number of annotated images. The lack of a sufficient quantity of labeled data for training is a bottleneck for the use of modern deep-learning based classification approaches in this domain. In this paper we propose a semi -supervised approach to deal with this problem. We use the discriminator (D) of a Generative Adversarial Network (GAN) as the final classifier, and we train D using both labeled and unlabeled data. The main novelty we introduce is the representation of the visual information fed to D by means of two different channels: the original image and its “semantic” representation, the latter being obtained by means of an external network trained on ImageNet. The two channels are fused in D and jointly used to classify fake images, real labeled and real unlabeled images. We show that using only 100 labeled images, the proposed approach achieves an accuracy close to 69% and a significant improvement with respect to other GAN-based semi-supervised methods. Although we have tested our approach only on satellite images, we do not use any domain-specific knowledge. Thus, our method can be applied to other semi-supervised domains.en
dc.description.sponsorshipEC/H2020/759764/EU/Accurate and Scalable Processing of Big Data in Earth Observation/BigEarthen
dc.identifier.eissn2381-8549
dc.identifier.isbn978-1-4799-7061-2
dc.identifier.isbn978-1-4799-7062-9
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/10385.2
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-9345.2
dc.language.isoenen
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subject.ddc006 Spezielle Computerverfahrenen
dc.subject.othersemi-supervised learningen
dc.subject.othergenerative adversarial networksen
dc.subject.othersatellite image classificationen
dc.titleSemantic-Fusion Gans for Semi-Supervised Satellite Image Classificationen
dc.typeConference Objecten
dc.type.versionacceptedVersionen
dcterms.bibliographicCitation.doi10.1109/ICIP.2018.8451836en
dcterms.bibliographicCitation.originalpublishernameInstitute of Electrical and Electronics Engineers (IEEE)en
dcterms.bibliographicCitation.originalpublisherplaceNew York, NYen
dcterms.bibliographicCitation.pageend688en
dcterms.bibliographicCitation.pagestart684en
dcterms.bibliographicCitation.proceedingstitle2018 25th IEEE International Conference on Image Processing (ICIP)en
tub.accessrights.dnbfreeen
tub.affiliationFak. 4 Elektrotechnik und Informatik>Inst. Technische Informatik und Mikroelektronik>FG Remote Sensing Image Analysis Groupde
tub.affiliation.facultyFak. 4 Elektrotechnik und Informatikde
tub.affiliation.groupFG Remote Sensing Image Analysis Groupde
tub.affiliation.instituteInst. Technische Informatik und Mikroelektronikde
tub.publisher.universityorinstitutionTechnische Universität Berlinen
Files
Original bundle
Now showing 1 - 1 of 1
Loading…
Thumbnail Image
Name:
roy_etal_ICIP2018.pdf
Size:
1.23 MB
Format:
Adobe Portable Document Format
Description:
Accepted manuscript
Collections

Version History

Now showing 1 - 2 of 2
VersionDOIDateSummary
10.14279/depositonce-9345.2 2020-12-28 09:25:46
include funding acknowledgement
10.14279/depositonce-9345 2019-11-25 21:11:22