Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Towards a framework for Noctilucent Cloud Analysis

Version 1 : Received: 4 October 2019 / Approved: 7 October 2019 / Online: 7 October 2019 (11:07:56 CEST)

A peer-reviewed article of this Preprint also exists.

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

Journal reference: Remote Sens. 2019, 11, 2743
DOI: 10.3390/rs11232743


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 latitude during summer. We study 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 employ 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 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.


noctilucent clouds; linear discriminant analysis; convolutional neural networks



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