Segmentation of hyperspectral images using quantized convolutional neural networks

Image segmentation is a pivotal task in hyperspectral image processing. Usually, it is performed in a setting where neither the environment nor the hardware pose an obstacle. If segmentation methods are deployed on embedded hardware, constraints in electrical power or computational resources can lead to the degradation of their performance, as well as to an increase in their inference time. This paper presents a supervised classification algorithm for hyperspectral image segmentation based on quantized convolutional neural networks. Compared with its non-quantized variant and other state-of-the-art approaches, the proposed method obtains accurate segmentation with a much lower memory size. By exploiting hardware instructions that are well suited to embedded devices (with or without GPU), energy requirements are reduced while high accuracy is maintained.