Cloud Implementation of Extreme Learning Machine for Hyperspectral Image Classification
Juan Mario Haut, Sergio Moreno‐Álvarez, Enrique Moreno-Ávila, V. A. Ayma, Rafael Vargas, Mercedes Eugenia Paoletti · IEEE Geoscience and Remote Sensing Letters · 2023
Classifying remotely sensed hyperspectral images (HSIs) became a computationally demanding task given the extensive information contained throughout the spectral dimension. Furthermore, burgeoning data volumes compound inherent computational and store challenges for data processing and classification purposes. Given their distributed processing capabilities, cloud environments have emerged as feasible solutions to handle these hurdles. This encourages the development of innovative distributed classification algorithms that take full advantage of the processing capabilities of such environments. Recently, computational-efficient methods have been implemented to boost network convergence by reducing the required training calculations. This paper develops a novel cloud-based distributed implementation of the Extreme Learning Machine (CC-ELM) algorithm for efficient HSI classification. The proposal implements a fault-tolerant and scalable computing design, whilst avoiding traditional batch-based back-propagation. CC-ELM has been evaluated over state-of-the-art HSI classification benchmarks, yielding promising results and proving the feasibility of cloud environments for large remote sensing and HSI data volumes processing. Code available on: https://github.com/mhaut/scalable-ELM-HSI.