Hyperspectral Image Analysis Using Cloud-Based Support Vector Machines

Author:

Haut Juan M.ORCID,Franco-Valiente Jose M.,Paoletti Mercedes E.,Moreno-Álvarez Sergio,Pardo-Diaz Alfonso

Abstract

AbstractHyperspectral image processing techniques involve time-consuming calculations due to the large volume and complexity of the data. Indeed, hyperspectral scenes contain a wealth of spatial and spectral information thanks to the hundreds of narrow and continuous bands collected across the electromagnetic spectrum. Predictive models, particularly supervised machine learning classifiers, take advantage of this information to predict the pixel categories of images through a training set of real observations. Most notably, the Support Vector Machine (SVM) has demonstrate impressive accuracy results for image classification. Notwithstanding the performance offered by SVMs, dealing with such a large volume of data is computationally challenging. In this paper, a scalable and high-performance cloud-based approach for distributed training of SVM is proposed. The proposal address the overwhelming amount of remote sensing (RS) data information through a parallel training allocation. The implementation is performed over a memory-efficient Apache Spark distributed environment. Experiments are performed on a benchmark of real hyperspectral scenes to show the robustness of the proposal. Obtained results demonstrate efficient classification whilst optimising data processing in terms of training times.

Funder

European Regional Development Funding

Spanish Ministerio de Ciencia e Innovacion

European Regional Development Funding de la Universidad Nacional del Comahue para el Desarrollo Regional

Junta de Extremadura

European Regional Development Fund

Universidad de Extremadura

Publisher

Springer Science and Business Media LLC

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