Add like
Add dislike
Add to saved papers

Feature Selection Based on High Dimensional Model Representation for Hyperspectral Images.

In hyperspectral image analysis, the classification task has generally been addressed jointly with dimensionality reduction due to both the high correlation between the spectral features and the noise present in spectral bands which might significantly degrade classification performance. In supervised classification, limited training instances in proportion to the number of spectral features have negative impacts on the classification accuracy, which has known as Hughes effects or curse of dimensionality in the literature. In this paper, we focus on dimensionality reduction problem, and propose a novel feature-selection algorithm which is based on the method called High Dimensional Model Representation. The proposed algorithm is tested on some toy examples and hyperspectral datasets in comparison to conventional feature-selection algorithms in terms of classification accuracy, stability of the selected features and computational time. The results showed that the proposed approach provides both high classification accuracy and robust features with a satisfactory computational time.

Full text links

We have located links that may give you full text access.
Can't access the paper?
Try logging in through your university/institutional subscription. For a smoother one-click institutional access experience, please use our mobile app.

For the best experience, use the Read mobile app

Mobile app image

Get seemless 1-tap access through your institution/university

For the best experience, use the Read mobile app

All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.

By using this service, you agree to our terms of use and privacy policy.

Your Privacy Choices Toggle icon

You can now claim free CME credits for this literature searchClaim now

Get seemless 1-tap access through your institution/university

For the best experience, use the Read mobile app