An Analysis of Dimension Reduction Methods Applicable for Out Of Sample Problem in Hyperspectral Images
Küçük Resim Yok
Tarih
2019
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IEEE
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Dimension 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.
Açıklama
9th International Conference on Recent Advances in Space Technologies (RAST) -- JUN 11-14, 2019 -- Istanbul, TURKEY
Anahtar Kelimeler
dimension reduction, hyperspectral imaging, classification
Kaynak
2019 9th International Conference On Recent Advances In Space Technologies (Rast)
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
