Classification of Interfering Elements in the Meteorological Images by Deep Neural Networks

Abstract

Presented work summarizes selected Convolutional Neural Networks classification of interfering elements in the meteorological images. Interfering elements, such as raindrops and insect adhered to camera lens, bright sun and other elements limit the process of automatic remote estimation of visibility at airports. We have experimented with three groups of pretrained neural networks. Namely we used AlexNet, DenseNet and ResNet. DenseNet169 classification appears to be a suitable solution. All the examined classification metrics, under the conditions of a classification threshold of 99% and above, indicated values above 90%. The paper also presents real deployment of classification models for full high definition camera images.