Project title

Hyperspectral Image Classification and Parameter optimization

Submitted to:

  • Dr. Simranjit Singh

Submitted By:

  1. Harshit Kawdia Srajan Shetty Nitin Dwivedi Nishit Raghuvanshi Mahesh Thalluri

Project Description

Hyperspectral images are very different from normal RGB images as RGB images constitute only three energy bands whereas a hyperspectral image contains hundreds of energy bands, therefore hyperspectral image classification predictive models are very complex and require specific parameter tuning for efficient working of the model. This paper proposes a hyperspectral classification framework that uses optimized parameters obtained via genetic algorithm. Different feature extraction and selection techniques like PCA is also used to reduce the excess dimensions of images. The proposed framework is implemented on the various machines and deep learning models to show the effectiveness of the proposed work.

Project Poster

Get Latest Notification about

Please ignore if you have already signed up.

Announcements, news and innovations!

From in your inbox.

By submitting this form, you are consenting to receive marketing emails from: Bennett University. You can revoke your consent to receive emails at any time by using the SafeUnsubscribe® link, found at the bottom of every email.