Hyperspectral unmixing-based anomaly detection

Mohammed Younis; Mazharul Hossain; Aaron Robinson; Lan Wang; Chrysanthe Preza

doi:10.1117/12.2664706

13 June 2023 Hyperspectral unmixing-based anomaly detection

Mohammed Younis, Mazharul Hossain, Aaron Robinson, Lan Wang, Chrysanthe Preza

Proceedings Volume 12523, Computational Imaging VII ; 1252302 (2023) https://doi.org/10.1117/12.2664706
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

Research supporting improved anomaly detection performance benefits a wide range of technical applications, and thus, the definition of what anomalies are and the subsequent means to detect them are wide ranging. In this treatment, an overview of the development of an anomaly detection approach based on spectral signatures obtained with hyperspectral unmixing is presented. The algorithm is designed to address some of the shortcomings of current techniques whose functionality is dependent upon normalized differences between discrete frequencies or spectral components, or those based on estimated distances between background spectra and pixels under test. Details about the extracted endmembers and their use for effective anomaly detection will be presented as well as, some thoughts on the expected requirements for future machine learning based implementations.

Conference Presentation

Citation Download Citation

Mohammed Younis, Mazharul Hossain, Aaron Robinson, Lan Wang, and Chrysanthe Preza "Hyperspectral unmixing-based anomaly detection", Proc. SPIE 12523, Computational Imaging VII , 1252302 (13 June 2023); https://doi.org/10.1117/12.2664706

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

Includes PDF, HTML & Video, when available