A GPU Accelerated Hyperspectral 3D Convolutional Neural Network Classification at the Edge with Principal Component Analysis Preprocessing

The Edge Computing paradigm promises to transfer decision-making processes based on artificial intelligence algorithms to the edge of the network without the need to query servers far from the data collection point. Hyperspectral image classification is one of the application fields that can benefit most from the close relationship between Edge Computing and Artificial Intelligence. It consists of a framework of techniques and methodologies for collecting and processing images related to objects or scenes on the Earth's surface, employing cameras or other sensors mounted on Unmanned Aerial Vehicles. However, the computing performance of the edge devices is not comparable with those of high-end servers, so specific approaches are required to consider the influence of the computing environment on the algorithm development methodology. In the present work, we propose a hybrid technique to make the Hyperspectral Image classification through Convolutional Neural Network affordable on low-power and high-performance sensor devices. We first use the Principal Component Analysis to filter insignificant wavelengths to reduce the dataset dimension; then, we use a process acceleration strategy to improve the performance by introducing a GPU-based form of parallelism.