Segmentation of PMSE data using random forests
Permanent link
https://hdl.handle.net/10037/25561Date
2022-06-22Type
Journal articleTidsskriftartikkel
Peer reviewed
Abstract
EISCAT VHF radar data are used for observing, monitoring, and understanding Earth’s
upper atmosphere. This paper presents an approach to segment Polar Mesospheric Summer Echoes
(PMSE) from datasets obtained from EISCAT VHF radar data. The data consist of 30 observations
days, corresponding to 56,250 data samples. We manually labeled the data into three different
categories: PMSE, Ionospheric background, and Background noise. For segmentation, we employed
random forests on a set of simple features. These features include: altitude derivative, time derivative,
mean, median, standard deviation, minimum, and maximum values corresponding to neighborhood
sizes ranging from 3 by 3 to 11 by 11 pixels. Next, in order to reduce the model bias and variance,
we employed a method that decreases the weight applied to pixel labels with large uncertainty. Our
results indicate that, first, it is possible to segment PMSE from the data using random forests. Second,
the weighted-down labels technique improves the performance of the random forests method.
Publisher
MDPICitation
Jozwicki, D.; Sharma, P.; Mann, I.; Hoppe U.-P. Segmentation of PMSE Data Using Random Forests. Remote Sens. 2022, 14, 2976Metadata
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