Nowadays, the use of hyperspectral sensors has been extended to a variety of applications such as the classification of remote-sensing images. Recently, a spectral–spatial classification scheme (ELM-EMP) based on Extreme Learning Machine (ELM) and Extended Morphological Profiles
(EMPs) computed using Principal Component Analysis (PCA) and morphological operations has been introduced. In this work, an efficient implementation of this scheme over commodity Graphics Processing Units (GPUs) is shown. Additionally, several techniques and optimizations are introduced to
improve the accuracy of the classification. In particular, a scheme using an ELM classifier based on kernels (KELM) and EMP is presented (KELM-EMP). Similar schemes adding a spatial regularization process (KELM-EMP-S and ELM-EMP-S) are also proposed. Moreover, two PCA algorithms have been
compared in both accuracy and speed terms. Regarding the GPU projection, different techniques and optimizations have been applied such as the use of optimized Compute Unified Device Architecture (CUDA) libraries or a block-asynchronous execution technique. As a result, the accuracy obtained
by the two proposed schemes (ELM-EMP-S and KELM-EMP-S) is better than for the original scheme ELM-EMP and the execution time has been significantly reduced.
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Document Type: Research Article
Centro Singular de Investigaciόn en Tecnoloxías da Informaciόn (CiTIUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
Departamento de Electrόnica e Computaciόn, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
Publication date: December 16, 2016
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