Deep Convolutional Neural Networks for Hyperspectral Image Classification

Recently, convolutional neural networks have demonstrated excellent performance on various visual tasks, including the classification of common two-dimensional images. In this paper, deep convolutional neural networks are employed to classify hyperspectral images directly in spectral domain. More sp...

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Bibliographic Details
Published in:Journal of sensors 2015-01, Vol.2015 (2015), p.1-12
Main Authors: Zhang, Fan, Wei, Li, Huang, Yangyu, Hu, Wei, Li, Heng-Chao
Format: Article
Language:English
Subjects:
Classification
Image classification
Neural networks
Spectra
Spectral signatures
Support vector machines
Two dimensional
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Summary:Recently, convolutional neural networks have demonstrated excellent performance on various visual tasks, including the classification of common two-dimensional images. In this paper, deep convolutional neural networks are employed to classify hyperspectral images directly in spectral domain. More specifically, the architecture of the proposed classifier contains five layers with weights which are the input layer, the convolutional layer, the max pooling layer, the full connection layer, and the output layer. These five layers are implemented on each spectral signature to discriminate against others. Experimental results based on several hyperspectral image data sets demonstrate that the proposed method can achieve better classification performance than some traditional methods, such as support vector machines and the conventional deep learning-based methods.
ISSN:1687-725X
1687-7268
DOI:10.1155/2015/258619