Investigation of band selection techniques to enable classification of hyperspectral imagery at the edge

Samantha Butler; Stanton R. Price; Ali Gurbuz; Stephanie J. Price; Joshua R. Fairley

doi:10.1117/12.2585591

12 April 2021 Investigation of band selection techniques to enable classification of hyperspectral imagery at the edge

Samantha Butler, Stanton R. Price, Ali Gurbuz, Stephanie J. Price, Joshua R. Fairley

Author Affiliations +

Proceedings Volume 11736, Real-Time Image Processing and Deep Learning 2021; 117360L (2021) https://doi.org/10.1117/12.2585591
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

Processing hyperspectral image data can be computationally expensive and difficult to employ for real-time applications due to its extensive spatial and spectral information. Further, applications in which computational resources may be limited, such as those requiring artificial intelligence at the edge, can be hindered by the volume of data that is common with airborne hyperspectral image data. This paper proposes utilizing band selection to down-select the number of spectral bands considering a given classification task so that classification can be done at the edge with lower computational complexity. Specifically, we consider popular techniques for band selection and investigate their feasibility to identify discriminative bands such that classification performance is not drastically hindered. This would greatly benefit applications where time-sensitive solutions are needed to ensure optimal outcomes (this could be related to defense, natural disaster relief/response, agriculture, etc.). Performance of the proposed approach is measured in terms of classification accuracy and run time.

Conference Presentation

Citation Download Citation

Samantha Butler, Stanton R. Price, Ali Gurbuz, Stephanie J. Price, and Joshua R. Fairley "Investigation of band selection techniques to enable classification of hyperspectral imagery at the edge", Proc. SPIE 11736, Real-Time Image Processing and Deep Learning 2021, 117360L (12 April 2021); https://doi.org/10.1117/12.2585591

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE