Unlocking the potential of edge computing for hyperspectral image classification: An efficient low-energy strategy

Despite recent improvements, the computing capability of Edge Computing devices is still inferior to high-end servers, so special methodologies are required to consider the computing environment while developing algorithms. In the present work, we propose a hybrid technique to make the classification of Hyperspectral Images feasible and effective through a Convolutional Neural Network on low-power and high-performance sensor devices. More specifically, we combine two strategies: we initially use the Principal Component Analysis method to discard non-significant wavelengths and shrink the dataset; then, we apply a process acceleration method to boost performance by implementing a form of GPU-based parallelism. The experiments demonstrate the technique’s effectiveness in terms of performance and energy consumption: it enables correct classifications even with low-power devices often deployed on Unmanned Aerial Vehicles, where the network connection is unpredictable or erratic.