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Appl. Sci. 2017, 7(12), 1212; https://doi.org/10.3390/app7121212

The Deep Belief and Self-Organizing Neural Network as a Semi-Supervised Classification Method for Hyperspectral Data

1
School of Economics and Management, Beihang University, Beijing 100191, China
2
Fundamental Science on Ergonomics and Environment Control Laboratory, School of Aeronautics Science and Engineering, Beihang University, Beijing 100191, China
*
Author to whom correspondence should be addressed.
Received: 12 September 2017 / Accepted: 8 November 2017 / Published: 24 November 2017
(This article belongs to the Section Computer Science and Electrical Engineering)
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Abstract

Hyperspectral data is not linearly separable, and it has a high characteristic dimension. This paper proposes a new algorithm that combines a deep belief network based on the Boltzmann machine with a self-organizing neural network. The primary features of the hyperspectral image are extracted with a deep belief network. The weights of the network are fine-tuned using the labeled sample. Feature vectors extracted by the deep belief network are classified by a self-organizing neural network. The method reduces the spectral dimension of the data while preserving the large amount of original information in the data. The method overcomes the long training time required when using self-organizing neural networks for clustering, as well as the training difficulties of Deep Belief Networks (DBN) when the labeled sample size is small, thereby improving the accuracy and robustness of the semi-supervised classification. Simulation results show that the structure of the network can achieve higher classification accuracy when the labeled sample is deficient. View Full-Text
Keywords: DBN; data compression; hyperspectral; SOM; pattern classification DBN; data compression; hyperspectral; SOM; pattern classification
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Lan, W.; Li, Q.; Yu, N.; Wang, Q.; Jia, S.; Li, K. The Deep Belief and Self-Organizing Neural Network as a Semi-Supervised Classification Method for Hyperspectral Data. Appl. Sci. 2017, 7, 1212.

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