GPU classification of remote-sensing images using kernel ELM and extended morphological profiles

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.