Dense Cloud Classification on Multispectral Satellite Imagery

Abstract

In this paper we explore the capabilities of two state-of-the-art machine learning techniques, transfer learning with convolutional neural networks (CNN) and support vector machines (SVM) to distinguish between 10 cloud genera. We will evaluate these methods using images acquired by the satellite Landsat 8. The classification of cloud genera is of high general relevance for remote sensing applications such as the surveillance of atmospheric or meteorological processes. Transfer learning is of advantage because it exploits neural networks, which are known to perform well, and enables the adaption to a specific problem with relatively small training data size. We will utilize Landsat 8 images for evaluating the examined machine learning approaches because these image data are freely available in large amounts. Downscaling the Landsat 8 images utilized for training to a resolution of about 300 meters per pixel will allow for keeping the CNN and SVM size reasonably low, such that our training data set can be restricted to a moderate size.