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Parallel supervised land-cover classification system for hyperspectral and multispectral images

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Abstract

Hyperspectral and multispectral imagery allows remote-sensing applications such as the land-cover mapping, which is a significant baseline to understand and to monitor the Earth. Furthermore, it is a relevant process for socio-economic activities. For that reason, high land-classification accuracies are imperative, and minor image processing time is essential. In addition, the process of gathering classes’ documented samples is complicated. This implies that the classification system is required to perform with a limited number of training observations. Another point worth mentioning is that there are hardly any methods that can be used analogously for hyperspectral or multispectral images. This paper aims to propose a novel classification system that can be used for both types of images. The designed classification system is composed of a novel parallel feature extraction algorithm, which utilises a cluster of two graphics processing units in combination with a multicore central processing unit (CPU), and an artificial neural network (ANN) particularly devised for the classification of the features ensued by the implemented feature extraction method. To prove the performance of the proposed classification system, it is compared with non-parallel and CPU-only-parallel implementations employing multispectral and hyperspectral databases. Moreover, experiments with different number of samples for training the classifier are performed. Finally, the proposed ANN is compared with a state-of-the-art support vector machine in classification and processing time results.

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Acknowledgements

The authors would like to thank Consejo Nacional de Ciencias y Tecnologia (CONACyT, Grant 220347) and Instituto Politecnico Nacional (IPN) for their support. In addition, they would like to thank the Computational Intelligence Group from the Basque University and Michele Volpi from University of Zurich for making, online available, the hyperspectral and multispectral databases correspondingly.

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Authors and Affiliations

  1. Instituto Politecnico Nacional, Mexico City, Mexico

    Beatriz P. Garcia-Salgado, Volodymyr I. Ponomaryov & Marco Robles-Gonzalez

  2. Instituto Mexicano del Petroleo, Mexico City, Mexico

    Sergiy Sadovnychiy

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  1. Beatriz P. Garcia-Salgado
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  2. Volodymyr I. Ponomaryov
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  4. Marco Robles-Gonzalez
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Correspondence to Beatriz P. Garcia-Salgado.

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Garcia-Salgado, B.P., Ponomaryov, V.I., Sadovnychiy, S. et al. Parallel supervised land-cover classification system for hyperspectral and multispectral images. J Real-Time Image Proc 15, 687–704 (2018). https://doi.org/10.1007/s11554-018-0828-2

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