Hyperspectral Image Classification Using Iterative Auto-Weighted Dimension Reduction
2022 IEEE Mediterranean and Middle-East Geoscience and Remote Sensing Symposium, M2GARSS 2022, Virtual, Online, Turkey, 7 - 09 March 2022, pp.94-97, (Full Text)
- Publication Type: Conference Paper / Full Text
- Doi Number: 10.1109/m2garss52314.2022.9840287
- City: Virtual, Online
- Country: Turkey
- Page Numbers: pp.94-97
- Keywords: Hyperspectral image classification, dimension reduction, auto-weighted local discriminant analysis
- Yıldız Technical University Affiliated: Yes
Abstract
© 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.