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Open AccessArticle

Towards a Framework for Noctilucent Cloud Analysis

1
Department of Automation & Process Engineering (IAP), The Arctic University of Norway, 9037 Tromsø, Norway
2
The Swedish Institute of Space Physics, SE-98128 Kiruna, Sweden
3
Department of Physics and Technology, The Arctic University of Norway, 9037 Tromsø, Norway
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(23), 2743; https://doi.org/10.3390/rs11232743
Received: 4 October 2019 / Revised: 14 November 2019 / Accepted: 20 November 2019 / Published: 22 November 2019
In this paper, we present a framework to study the spatial structure of noctilucent clouds formed by ice particles in the upper atmosphere at mid and high latitudes during summer. We studied noctilucent cloud activity in optical images taken from three different locations and under different atmospheric conditions. In order to identify and distinguish noctilucent cloud activity from other objects in the scene, we employed linear discriminant analysis (LDA) with feature vectors ranging from simple metrics to higher-order local autocorrelation (HLAC), and histogram of oriented gradients (HOG). Finally, we propose a convolutional neural networks (CNN)-based method for the detection of noctilucent clouds. The results clearly indicate that the CNN-based approach outperforms the LDA-based methods used in this article. Furthermore, we outline suggestions for future research directions to establish a framework that can be used for synchronizing the optical observations from ground-based camera systems with echoes measured with radar systems like EISCAT in order to obtain independent additional information on the ice clouds. View Full-Text
Keywords: noctilucent clouds; linear discriminant analysis; convolutional neural networks noctilucent clouds; linear discriminant analysis; convolutional neural networks
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MDPI and ACS Style

Sharma, P.; Dalin, P.; Mann, I. Towards a Framework for Noctilucent Cloud Analysis. Remote Sens. 2019, 11, 2743.

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