Date of Award
2024
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Computer Science
Committee Chair
Huaming Zhang
Committee Member
Jacob Hauenstein
Committee Member
Deepak Acharya
Research Advisor
Huaming Zhang
Subject(s)
Radar meteorology, image processing, Neural networks (Computer Science), Machine learning
Abstract
In the field of radar meteorology, a perpetual problem is removal of so-called anomalous propagation (AP), i.e. non-precipitation echoes, from the produced images. Much work has been done in this area already, including conventional heuristic algorithms as well as machine-learning systems such as neural networks. Often the focus is on certain familiar radar architectures such as WSR-88D, also known as NEXRAD. However, a large number of radars exist which are not identical to NEXRAD; and there are also environmental differences such as RF interference which can affect the success rate of existing AP removal strategies. The focus of this paper is to present a flexible machine-learning system for this task which provides a convenient training interface, so it can be adapted to the specific conditions present at any given radar site to create a customized filter. We have named this system ClearScan.
Recommended Citation
Jones, Erick, "ClearScan : a machine learning system for customized, site-specific radar image filters" (2024). Theses. 683.
https://louis.uah.edu/uah-theses/683