Abstract
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.
Keywords
Hyperspectral imaging
Dimensionality reduction
Dimension (graph theory)
Artificial intelligence
Pattern recognition (psychology)
Computer science
Reduction (mathematics)
Process (computing)
Contextual image classification
Image (mathematics)
Computer vision
Mathematics
Subject Areas
Remote-Sensing Image Classification
·Media Technology ·Physical Sciences
Face and Expression Recognition
·Computer Vision and Pattern Recognition ·Physical Sciences
Remote Sensing and Land Use
·Atmospheric Science ·Physical Sciences
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