Ss-Mla: a Semisupervised Method for Multi-Label Annotation of Remotely Sensed Images

dc.authorid Üstünkök, Tolga/0000-0002-0464-8803
dc.authorscopusid 57205573451
dc.authorscopusid 16637174900
dc.authorwosid KARAKAYA, Murat/A-4952-2013
dc.authorwosid Üstünkök, Tolga/AAX-8090-2021
dc.contributor.author Üstünkök,T.
dc.contributor.author Karakaya,M.
dc.contributor.other Software Engineering
dc.contributor.other Computer Engineering
dc.date.accessioned 2024-07-05T15:19:50Z
dc.date.available 2024-07-05T15:19:50Z
dc.date.issued 2021
dc.department Atılım University en_US
dc.department-temp Üstünkök T., Atlllm University, Faculty of Engineering, Department of Software Engineering, Ankara, Turkey; Karakaya M., Atlllm University, Faculty of Engineering, Department of Computer Engineering, Ankara, Turkey en_US
dc.description Üstünkök, Tolga/0000-0002-0464-8803 en_US
dc.description.abstract Recent technological advancements in satellite imagery have increased the production of remotely sensed images. Therefore, developing efficient methods for annotating these images has gained popularity. Most of the current state-of-the-art methods are based on supervised machine learning techniques. We propose a method called semisupervised multi-label annotizer (SS-MLA) that adapts vector-quantized temporal associative memory to annotate remotely sensed images. One of the advantages of SS-MLA over the supervised methods is that it extracts features not only from the given sample but also from similar samples that are previously seen without using an explicit attention mechanism. Thus SS-MLA enhances the learning efficiency of the training process. We conduct extensive performance comparisons with five different methods in the literature over four datasets. The comparison results indicate the success of the proposed method over the existing ones: SS-MLA generates the best results in 7 out of 11 comparisons. © 2021 Society of Photo-Optical Instrumentation Engineers (SPIE). en_US
dc.identifier.citationcount 0
dc.identifier.doi 10.1117/1.JRS.15.036509
dc.identifier.issn 1931-3195
dc.identifier.issue 3 en_US
dc.identifier.scopus 2-s2.0-85116340141
dc.identifier.scopusquality Q2
dc.identifier.uri https://doi.org/10.1117/1.JRS.15.036509
dc.identifier.volume 15 en_US
dc.identifier.wos WOS:000703527600001
dc.identifier.wosquality Q4
dc.institutionauthor Üstünkök, Tolga
dc.institutionauthor Karakaya, Kasım Murat
dc.institutionauthor Üstünkök, Tolga
dc.institutionauthor Karakaya, Kasım Murat
dc.language.iso en en_US
dc.publisher SPIE en_US
dc.relation.ispartof Journal of Applied Remote Sensing en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.scopus.citedbyCount 1
dc.subject image classification en_US
dc.subject multi-label en_US
dc.subject remote sensing en_US
dc.subject semisupervised en_US
dc.subject vector-quantized temporal associative memory en_US
dc.title Ss-Mla: a Semisupervised Method for Multi-Label Annotation of Remotely Sensed Images en_US
dc.type Article en_US
dc.wos.citedbyCount 1
dspace.entity.type Publication
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