Suárez Garea, Jorge AlbertoBlanco Heras, DoraArgüello Pedreira, Francisco2025-01-132025-01-132018-03-09Garea, A. S., Heras, D. B., & Argüello, F. (2019). Caffe CNN-based classification of hyperspectral images on GPU. The Journal of Supercomputing, 75, 1065-1077.1573-0484https://hdl.handle.net/10347/38536This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s11227-018-2300-2Deep learning techniques based on Convolutional Neural Networks (CNNs) are extensively used for the classification of hyperspectral images. These techniques present high computational cost. In this paper, a GPU (Graphics Processing Unit) implementation of a spatial-spectral supervised classification scheme based on CNNs and applied to remote sensing datasets is presented. In particular, two deep learning libraries, Caffe and CuDNN, are used and compared. In order to achieve an efficient GPU projection, different techniques and optimizations have been applied. The implemented scheme comprises Principal Component Analysis (PCA) to extract the main features, a patch extraction around each pixel to take the spatial information into account, one convolutional layer for processing the spectral information, and fully connected layers to perform the classification. To improve the initial GPU implementation accuracy, a second convolutional layer has been added. High speedups are obtained together with competitive classification accuracies.engAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Superpixel segmentationWatershed transformWaterpixel segmentationHyperspectral imageRemote sensingCUDA120317 Informáticajournal article10.1007/s11227-018-2300-2open access