Towards Automated Target Picking in Scalar Magnetic Unexploded Ordnance Surveys: An Unsupervised Machine Learning Approach for Defining Inversion Priors

Towards Automated Target Picking in Scalar Magnetic Unexploded Ordnance Surveys: An Unsupervised Machine Learning Approach for Defining Inversion Priors

With advancements in both the quality and collection speed of magnetic data captured by uncrewed aerial vehicle (UAV)-based systems, there is a growing need for robust and efficient systems to automatically interpret such data. Many existing conventional methods require manual inspection of the surv...

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Bibliographic Details
Main Authors: Claire McGinnity, Mick Emil Kolster, Arne Døssing
Format: Article
Language:English
Published: MDPI AG 2024-01-01
Series:Remote Sensing
Subjects:
target picking
hierarchical clustering
magnetic difference inversion
unexploded ordnance (UXO)
automated prior information gathering
Online Access:https://www.mdpi.com/2072-4292/16/3/507

Internet

https://www.mdpi.com/2072-4292/16/3/507

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