Hyperspectral Image Classification Using Iterative Auto-Weighted Dimension Reduction

SAKARYA U.

2022 IEEE Mediterranean and Middle-East Geoscience and Remote Sensing Symposium, M2GARSS 2022, Virtual, Online, Türkiye, 7 - 09 Mart 2022, ss.94-97 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/m2garss52314.2022.9840287
  • Basıldığı Şehir: Virtual, Online
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.94-97
  • Anahtar Kelimeler: Hyperspectral image classification, dimension reduction, auto-weighted local discriminant analysis
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

© 2022 IEEE.In hyperspectral image classification task, achieving suitable dimension reduction is important to obtain desired classification performance. There are dozens of approaches to achieve this process. In this paper, a supervised auto-weighted dimension reduction method is applied on hyperspectral images for classification purposes. The proposed method examines auto-weighted condition with a view to analyzing the effects on hyperspectral images. Comparative experimental studies are realized in order to demonstrate the advantage and disadvantage of the used method.