Reducing false alarms in hyperspectral images using a covariance matrix based on preliminary false detections

Idan Ben Shabat; Lihi Zinger; Stanley R. Rotman

doi:10.1117/12.2585168

12 April 2021 Reducing false alarms in hyperspectral images using a covariance matrix based on preliminary false detections

Idan Ben Shabat, Lihi Zinger, Stanley R. Rotman

Author Affiliations +

Proceedings Volume 11727, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXVII; 1172709 (2021) https://doi.org/10.1117/12.2585168
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

In image processing, the Matched Filter algorithm uses the estimated covariance matrix to give each pixel a score based on the similarity between the pixel and the signature of the target. While using this target detection algorithm, false alarms are inevitable. In order to solve this problem, a method using an iterative process to produce a second covariance matrix which only uses the most likely false alarms was presented [6]. In this paper, we test this method, attempt to improve it, and expand on the cases in which it is the most effective. In all cases, the new method showed a decrease in false alarms, and in some cases a decrease of over 85%.

Conference Presentation

Citation Download Citation

Idan Ben Shabat, Lihi Zinger, and Stanley R. Rotman "Reducing false alarms in hyperspectral images using a covariance matrix based on preliminary false detections", Proc. SPIE 11727, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXVII, 1172709 (12 April 2021); https://doi.org/10.1117/12.2585168

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