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Multiscale dual-level network for hyperspectral image classification

Abstract. We propose a new dual-level convolutional neural network model based on Inception modules and residual connections. First, Inception has filters with different kernel sizes, and its output feature maps contain different scales of receptive fields. The feature map with the wide receptive field receives the global information, while the feature map with the small receptive field contains some local information. The multiscale feature provides more comprehensive information. Second, with the help of residual connections, the training process is simple and can avoid overfitting. Third, the proposed network adopts two levels, i.e., a low level and a high level, and uses the feature fusion operation to take full advantage of the complementary and correlated information of the two levels. Fourth, we combine the spatial features and the spectral features of hyperspectral image (HSI). The pixels to be classified with their neighborhood information serve as the input of the neural network to realize spectral–spatial classification for HSI. Experimental results show that our model performs better than other state-of-the-art methods.