Using dual-channel CNN to classify hyperspectral image based on spatial-spectral information

Haifeng Song; Weiwei Yang; Songsong Dai; Lei Du; Yongchen Sun; Haifeng Song; Weiwei Yang; Songsong Dai; Lei Du; Yongchen Sun

doi:10.3934/mbe.2020195

Mathematical Biosciences and Engineering

2020, Volume 17, Issue 4: 3450-3477. doi: 10.3934/mbe.2020195

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Research article Special Issues

Using dual-channel CNN to classify hyperspectral image based on spatial-spectral information

1.
School of Electronics and Information Engineering (School of Big Data Science), Taizhou University, Taizhou, China
2.
The Transportation Monitoring and Emergency Response Center of Shandong Province, Jinan, China

Received: 23 January 2020 Accepted: 22 April 2020 Published: 05 May 2020

In the field of remote sensing image processing, the classification of hyperspectral image (HSI) is a hot topic. There are two main problems lead to the classification accuracy unsatisfactory. One problem is that the recent research on HSI classification is based on spectral features, the relationship between different pixels has been ignored; the other is that the HSI data does not contain or only contain a small amount of labeled data, so it is impossible to build a well classification model. To solve these problems, a dual-channel CNN model has been proposed to boost its discriminative capability for HSI classification. The proposed dual-channel CNN model has several distinct advantages. Firstly, the model consists of spectral feature extraction channel and spatial feature extraction channel; the 1-D CNN and 3-D CNN are used to extract the spectral and spatial features, respectively. Secondly, the dual-channel CNN have been used for fusing the spatial-spectral features, the fusion feature is input into the classifier, which effectively improves the classification accuracy. Finally, due to considering the spatial and spectral features, the model can effectively solve the problem of lack of training samples. The experiments on benchmark data sets have demonstrated that the proposed dual-channel CNN model considerably outperforms other state-of-the-art method.
- hyperspecral image,
- spatial-spectral information,
- dual-channel,
- convolutional neural network,
- classification
Citation: Haifeng Song, Weiwei Yang, Songsong Dai, Lei Du, Yongchen Sun. Using dual-channel CNN to classify hyperspectral image based on spatial-spectral information[J]. Mathematical Biosciences and Engineering, 2020, 17(4): 3450-3477. doi: 10.3934/mbe.2020195

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Abstract

In the field of remote sensing image processing, the classification of hyperspectral image (HSI) is a hot topic. There are two main problems lead to the classification accuracy unsatisfactory. One problem is that the recent research on HSI classification is based on spectral features, the relationship between different pixels has been ignored; the other is that the HSI data does not contain or only contain a small amount of labeled data, so it is impossible to build a well classification model. To solve these problems, a dual-channel CNN model has been proposed to boost its discriminative capability for HSI classification. The proposed dual-channel CNN model has several distinct advantages. Firstly, the model consists of spectral feature extraction channel and spatial feature extraction channel; the 1-D CNN and 3-D CNN are used to extract the spectral and spatial features, respectively. Secondly, the dual-channel CNN have been used for fusing the spatial-spectral features, the fusion feature is input into the classifier, which effectively improves the classification accuracy. Finally, due to considering the spatial and spectral features, the model can effectively solve the problem of lack of training samples. The experiments on benchmark data sets have demonstrated that the proposed dual-channel CNN model considerably outperforms other state-of-the-art method.

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