GPU-Based Extreme Learning Machine for the Classification of Hyperspectral Remote Sensing Images

Experimental results related to the paper Efficient ELM-based Techniques for the Classification of Hyperspectral Remote Sensing Images on Commodity GPUs by Javier López-Fandiño, Pablo Quesada-Barriuso, Dora B. Heras, and Francisco Argüello, published in the IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

Abstract

Extreme Learning Machine (ELM) is an efficient learning algorithm that has been recently applied to hyperspectral image classification. In this paper, the first implementation of the ELM algorithm fully developed for Graphical Processing Unit (GPU) is presented. ELM can be expressed in terms of matrix operations so as to take advantage of the Single Instruction Multiple Data (SIMD) computing paradigm of the GPU architecture. Additionally, several techniques like the use of ensembles, a spatial regularization algorithm, and a spectralspatial classification scheme are applied and projected to GPU in order to improve the accuracy results of the ELM classifier. In the last case, the spatial processing is based on the segmentation of the hyperspectral image through a watershed transform. The experiments are performed on remote sensing data for land cover applications achieving competitive accuracy results compared to analogous SVM strategies with significantly lower execution times. The best accuracy results are obtained with the spectralspatial scheme based on applying watershed and a spatially regularized ELM.