Spectral–Spatial Hyperspectral Image Classification via Non-local Means Filtering Feature Extraction

Spectral–Spatial Hyperspectral Image Classification via Non-local Means Filtering Feature... Hyperspectral image (HSI) classification has been a hot topic of research in recent years. The integration of spectral and spatial context is an effective method for HSI classification. This paper proposes a classification method of HSI based on non-local means (NLM) filtering. Firstly, the classification result of HSI is obtained by adopting the support vector machines. Then, the optimization probability image of spatial structure is obtained by using the spatial context information in the first principal component or the first three principal components of HSI to optimize the initial probability map through the NLM filtering. Finally, the final classification results are calculated based on the maximum probability. Experiment results on three real hyperspectral data demonstrate that the proposed NLM filtering based classification method can improve the classification accuracy significantly. Classification results show the effectiveness and superiority of the proposed methods when compared with other methods. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Sensing and Imaging: An International Journal Springer Journals

Spectral–Spatial Hyperspectral Image Classification via Non-local Means Filtering Feature Extraction

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Publisher
Springer US
Copyright
Copyright © 2018 by Springer Science+Business Media, LLC, part of Springer Nature
Subject
Engineering; Electrical Engineering; Microwaves, RF and Optical Engineering; Imaging / Radiology
ISSN
1557-2064
eISSN
1557-2072
D.O.I.
10.1007/s11220-018-0196-9
Publisher site
See Article on Publisher Site

Abstract

Hyperspectral image (HSI) classification has been a hot topic of research in recent years. The integration of spectral and spatial context is an effective method for HSI classification. This paper proposes a classification method of HSI based on non-local means (NLM) filtering. Firstly, the classification result of HSI is obtained by adopting the support vector machines. Then, the optimization probability image of spatial structure is obtained by using the spatial context information in the first principal component or the first three principal components of HSI to optimize the initial probability map through the NLM filtering. Finally, the final classification results are calculated based on the maximum probability. Experiment results on three real hyperspectral data demonstrate that the proposed NLM filtering based classification method can improve the classification accuracy significantly. Classification results show the effectiveness and superiority of the proposed methods when compared with other methods.

Journal

Sensing and Imaging: An International JournalSpringer Journals

Published: Mar 13, 2018

References

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