Yilmaz, AtincOzturk, Umit2024-03-132024-03-132019978-1-5386-9448-0https://doi.org/10.1109/rast.2019.8767826https://hdl.handle.net/20.500.12662/33689th International Conference on Recent Advances in Space Technologies (RAST) -- JUN 11-14, 2019 -- Istanbul, TURKEYDimension reduction (DR) techniques provide advantages in terms of reducing computational time and data storage requirements. These methods are widely used for Hyper-spectral Imaging (HSI) data classification purposes. However, the need of extention to out of sample (OOS) data during the learning procedure is a main drawback for traditional manifold learning (ML) algorithms. There are a number of DR methods applicable for OOS problem. This study aims at focusing a comprehensive analysis of these DR methods with HSI data classification. In this context, we made experiments on HSI data by applying LPP, NPE, OLPP, ONPE and Nystrom (for LE and LLE) algorithms to observe classification performance. Firstly, parametric graphs are obtained to understand the behaviour and then, classification performance is measured upon reduced dimensionality. Support Vector Machines (SVM) and K-Nearest Neighbor (KNN) are used as classifier. All experiments are conducted on Kennedy Space Center (KSC) and Indian Pines hyperspectral data.eninfo:eu-repo/semantics/closedAccessdimension reductionhyperspectral imagingclassificationConference Object10.1109/rast.2019.87678262-s2.0-85073913908386N/A381WOS:000492052500060N/A