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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| Published in | Journal of sensors Vol. 2015; no. 2015; pp. 1 - 12 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Cairo, Egypt
Hindawi Publishing Corporation
01.01.2015
John Wiley & Sons, Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1687-725X 1687-7268 |
| DOI | 10.1155/2015/258619 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Huang, Yangyu Hu, Wei Li, Heng-Chao Zhang, Fan Wei, Li |
| Author_xml | – sequence: 1 fullname: Zhang, Fan – sequence: 2 fullname: Wei, Li – sequence: 3 fullname: Huang, Yangyu – sequence: 4 fullname: Hu, Wei – sequence: 5 fullname: Li, Heng-Chao |
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| ContentType | Journal Article |
| Copyright | Copyright © 2015 Wei Hu et al. Copyright © 2015 Wei Hu et al. Wei Hu et al. 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. |
| Copyright_xml | – notice: Copyright © 2015 Wei Hu et al. – notice: Copyright © 2015 Wei Hu et al. Wei Hu et al. 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. |
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| SubjectTerms | Classification Image classification Neural networks Spectra Spectral signatures Support vector machines Two dimensional |
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| Title | Deep Convolutional Neural Networks for Hyperspectral Image Classification |
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