Üstünkök, TolgaUstunkok, TolgaKarakaya, MuratKarakaya, Kasım MuratSoftware EngineeringComputer Engineering2024-07-052024-07-05202101931-319510.1117/1.JRS.15.036509https://doi.org/10.1117/1.JRS.15.036509https://hdl.handle.net/20.500.14411/2024Üstünkök, Tolga/0000-0002-0464-8803Recent 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)eninfo:eu-repo/semantics/closedAccesssemisupervisedvector-quantized temporal associative memoryremote sensingmulti-labelimage classificationArticleQ4Q2153WOS:000703527600001