Research article Special Issues

Multi-source remote sensing image classification based on two-channel densely connected convolutional networks

  • Received: 24 July 2020 Accepted: 09 October 2020 Published: 27 October 2020
  • Remote sensing image classification exploiting multiple sensors is a very challenging problem: The traditional methods based on the medium- or low-resolution remote sensing images always provide low accuracy and poor automation level because the potential of multi-source remote sensing data are not fully utilized and the low-level features are not effectively organized. The recent method based on deep learning can efficiently improve the classification accuracy, but as the depth of deep neural network increases, the network is prone to be overfitting. In order to address these problems, a novel Two-channel Densely Connected Convolutional Networks (TDCC) is proposed to automatically classify the ground surfaces based on deep learning and multi-source remote sensing data. The main contributions of this paper includes the following aspects: First, the multi-source remote sensing data consisting of hyperspectral image (HSI) and Light Detection and Ranging (LiDAR) are pre-processed and re-sampled, and then the hyperspectral data and LiDAR data are input into the feature extraction channel, respectively. Secondly, two-channel densely connected convolutional networks for feature extraction were proposed to automatically extract the spatial-spectral feature of HSI and LiDAR. Thirdly, a feature fusion network is designed to fuse the hyperspectral image features and LiDAR features. The fused features were classified and the output result is the category of the corresponding pixel. The experiments were conducted on popular dataset, the results demonstrate that the competitive performance of the TDCC with respect to classification performance compared with other state-of-the-art classification methods in terms of the OA, AA and Kappa, and it is more suitable for the classification of complex ground surfaces.

    Citation: Haifeng Song, Weiwei Yang, Songsong Dai, Haiyan Yuan. Multi-source remote sensing image classification based on two-channel densely connected convolutional networks[J]. Mathematical Biosciences and Engineering, 2020, 17(6): 7353-7377. doi: 10.3934/mbe.2020376

    Related Papers:

  • Remote sensing image classification exploiting multiple sensors is a very challenging problem: The traditional methods based on the medium- or low-resolution remote sensing images always provide low accuracy and poor automation level because the potential of multi-source remote sensing data are not fully utilized and the low-level features are not effectively organized. The recent method based on deep learning can efficiently improve the classification accuracy, but as the depth of deep neural network increases, the network is prone to be overfitting. In order to address these problems, a novel Two-channel Densely Connected Convolutional Networks (TDCC) is proposed to automatically classify the ground surfaces based on deep learning and multi-source remote sensing data. The main contributions of this paper includes the following aspects: First, the multi-source remote sensing data consisting of hyperspectral image (HSI) and Light Detection and Ranging (LiDAR) are pre-processed and re-sampled, and then the hyperspectral data and LiDAR data are input into the feature extraction channel, respectively. Secondly, two-channel densely connected convolutional networks for feature extraction were proposed to automatically extract the spatial-spectral feature of HSI and LiDAR. Thirdly, a feature fusion network is designed to fuse the hyperspectral image features and LiDAR features. The fused features were classified and the output result is the category of the corresponding pixel. The experiments were conducted on popular dataset, the results demonstrate that the competitive performance of the TDCC with respect to classification performance compared with other state-of-the-art classification methods in terms of the OA, AA and Kappa, and it is more suitable for the classification of complex ground surfaces.


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