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

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