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Multi-Feature Classification Approach for High Spatial Resolution Hyperspectral Images

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Abstract

High spatial resolution hyperspectral images not only contain abundant radiant and spectral information, but also display rich spatial information. In this paper, we propose a multi-feature high spatial resolution hyperspectral image classification approach based on the combination of spectral information and spatial information. Three features are derived from the original high spatial resolution hyperspectral image: the spectral features that are acquired from the auto subspace partition technique and the band index technique; the texture features that are obtained from GLCM analysis of the first principal component after principal component analysis is performed on the original image; and the spatial autocorrelation features that contain spatial band X and spatial band Y, with the grey level of spatial band X changing along columns and the grey level of spatial band Y changing along rows. The three features are subsequently combined together in Support Vector Machine to classify the high spatial resolution hyperspectral image. The experiments with a high spatial resolution hyperspectral image prove that the proposed multi-feature classification approach significantly increases classification accuracies.

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Acknowledgements

This work is cofunded by State Grid Scientific Project 2016 (No. GCB17201600036) “Research on data processing theory and methods of the auxiliary lines selection based on satellite remote sensing image” and Key Science &Technology Project 2016 on phase II scientific control for Three Gorges Reservoir. We acknowledge two anonymous reviewers for their detailed and very constructive remarks.

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Authors and Affiliations

  1. Department of Civil Engineering, Beihang University, Beijing, 100191, China

    Yumin Tan & Wei Xia

  2. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China

    Wei Xia

  3. Department of Geography and Environmental Studies, California State University San Bernardino, San Bernardino, CA, USA

    Bo Xu

  4. State Grid Corporation of China, Shijiazhuang, China

    Linjie Bai

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  1. Yumin Tan
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  2. Wei Xia
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Correspondence to Yumin Tan.

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Tan, Y., Xia, W., Xu, B. et al. Multi-Feature Classification Approach for High Spatial Resolution Hyperspectral Images. J Indian Soc Remote Sens 46, 9–17 (2018). https://doi.org/10.1007/s12524-017-0663-0

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