Towards resource-frugal deep convolutional neural networks for hyperspectral image segmentation

Hyperspectral image analysis has been gaining research attention thanks to the current advances in sensor design which have made acquiring such imagery much more affordable. Although there exist various approaches for segmenting hyperspectral images, deep learning has become the mainstream. However, such large-capacity learners are characterized by significant memory footprints. This is a serious obstacle in employing deep neural networks on board a satellite for Earth observation. In this paper, we introduce resource-frugal quantized convolutional neural networks, and greatly reduce their size without adversely affecting the classification capability. Our experiments performed over two hyperspectral benchmarks showed that the quantization process can be seamlessly applied during the training, and it leads to much smaller and still well-generalizing deep models.