Efficient ELM-Based Techniques for the Classification of Hyperspectral Remote Sensing Images on Commodity GPUs

TítuloEfficient ELM-Based Techniques for the Classification of Hyperspectral Remote Sensing Images on Commodity GPUs
AutoresJavier López-Fandiño, Pablo Quesada-Barriuso, Dora B. Heras, and Francisco Argüello
TipoArtículo de revista
Fonte IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, IEEE, Vol. 8, No. 6, pp. 2884-2893 , 2015.
RankRanked Q1 in Computers in Earth Sciences by CiteScore
DOI10.1109/JSTARS.2014.2384133
AbstractExtreme 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 spectral-spatial 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 support vector machine (SVM) strategies with significantly lower execution times. The best accuracy results are obtained with the spectral-spatial scheme based on applying watershed and a spatially regularized ELM.
Palabras chaveCompute unified device architecture (CUDA), extreme learning machine, graphical processing unit (GPU), hyperspectral images, remote sensing, spectral-spatial classification, support vector machine (SVM), watershed