SS-MLA: a semisupervised method for multi-label annotation of remotely sensed images
dc.authorid | Üstünkök, Tolga/0000-0002-0464-8803 | |
dc.authorwosid | KARAKAYA, Murat/A-4952-2013 | |
dc.authorwosid | Üstünkök, Tolga/AAX-8090-2021 | |
dc.contributor.author | Üstünkök, Tolga | |
dc.contributor.author | Karakaya, Murat | |
dc.contributor.author | Karakaya, Kasım Murat | |
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 | [Ustunkok, Tolga] Atilim Univ, Fac Engn, Dept Software Engn, Ankara, Turkey; [Karakaya, Murat] Atilim Univ, Fac Engn, Dept Comp Engn, 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. (C) 2021 Society of Photo-Optical Instrumentation Engineers (SPIE) | en_US |
dc.identifier.citation | 0 | |
dc.identifier.doi | 10.1117/1.JRS.15.036509 | |
dc.identifier.issn | 1931-3195 | |
dc.identifier.issue | 3 | en_US |
dc.identifier.scopusquality | Q2 | |
dc.identifier.uri | https://doi.org/10.1117/1.JRS.15.036509 | |
dc.identifier.uri | https://hdl.handle.net/20.500.14411/2024 | |
dc.identifier.volume | 15 | en_US |
dc.identifier.wos | WOS:000703527600001 | |
dc.identifier.wosquality | Q4 | |
dc.language.iso | en | en_US |
dc.publisher | Spie-soc Photo-optical instrumentation Engineers | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | semisupervised | en_US |
dc.subject | vector-quantized temporal associative memory | en_US |
dc.subject | remote sensing | en_US |
dc.subject | multi-label | en_US |
dc.subject | image classification | en_US |
dc.title | SS-MLA: a semisupervised method for multi-label annotation of remotely sensed images | en_US |
dc.type | Article | en_US |
dspace.entity.type | Publication | |
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