Ceylan, O.Taskin, G.2023-10-192023-10-19202119781665403696https://doi.org/10.1109/IGARSS47720.2021.9553491https://hdl.handle.net/20.500.12469/4939The Institute of Electrical and Electronics Engineers Geoscience and Remote Sensing Society (GRSS)2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 --12 July 2021 through 16 July 2021 -- --176845Hyperspectral images are the multidimensional matrices consisting of hundreds of spectral feature vectors. Thanks to these large number of features, the objects on the Earth having similar spectral characteristics can easily be distinguished from each other. However, the high correlation and the noise between these features cause a significant decrease in the classification performances, especially in the supervised classification tasks. In order to overcome these problems, which is known in the literature as Hughes's effects or curse of dimensionality, dimensionality reduction techniques have frequently been used. Feature selection and feature extraction methods are the ones used for this purpose. The feature selection methods aim to remove the features, including high correlation and noise, out of the original feature set. In other words, a subset of relevant features that have the ability to distinguish the objects is determined. The feature extraction methods project the high dimensional space into a lower-dimensional feature space based on some optimization criterion, and hence they distort the original characteristic of the dataset. Therefore, the feature selection methods are more preferred than the feature extraction methods since they preserve the originality of the dataset. Based on this motivation, an evolutionary based optimization algorithm utilizing self organizing map was accordingly modified to provide a new feature selection method for the classification of hyperspectral images. The proposed method was compared to well-known feature selection methods in the classification of two hyperspectral datasets: Botswana and Indian Pines. According to the preliminary results, the proposed method achieves higher performance over other feature selection methods with a very less number of features. © 2021 IEEE.eninfo:eu-repo/semantics/closedAccessEvolutionary methodsFeature selectionHyperspectral image classificationOptimizationSelf organizing mapsConference Object400340062021-July10.1109/IGARSS47720.2021.95534912-s2.0-85126023541