Optimizing Convolutional Neural Networks for Cloud Detection

Abstract

Deep convolutional neural networks (CNNs) have become extremely popular and successful at a number of machine learning tasks. One of the great challenges of successfully deploying a CNN is designing the network: specifying the network topology (sequence of layer types) and configuring the network (setting all the internal layer hyper-parameters). There are a number of techniques which are commonly used to design the network. One of the most successful is a simple (but lengthy) random search. In this paper we demonstrate how a random search can be dramatically improved by a two-phase search. The first phase is a traditional random search on n network configurations. The second phase exploits a support vector machine to guide a second random search on N network configurations. We apply this technique to a dataset containing satellite imagery and demonstrate that we can, with very high accuracy, identify regions containing clouds which obscure the landscape below.