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For hyperspectral remote sensing image (HSI) classification, the learning process of deep neural networks has been progressively advanced in depth, but the fine features are often largely lost or even disappear in the process of depth transfer. With the increase in feature aggregation and connectivity, the complexity of the network and the training parameters increases greatly, requiring more training time. This paper proposed a multi-scale dense network (MSDN) for HSI classification that made full use of different scale information in the network structure and combined scale information throughout the network. It implemented feature extraction of HSIs in two dimensions, including the features at fine and coarse levels. In the horizontal direction, it considered the deep extraction of HSI features, and the 3-D dense connection structure was used for aggregating features at different levels. In the vertical direction, scale information was considered, and three-scale feature maps at low, middle, and high levels were generated based on the first layer of the network. The MSDN used stride convolution for downsampling and combined feature information at different scale levels. The MSDN extracted features along the diagonal line. The network implemented the reconstruction of deep feature extraction and multi-scale fusion for HSI classification. The MSDN model performed well on representative HSI datasets, namely, the Indian Pines, Pavia University, Salinas, Botswana, and Kennedy Space Center datasets. It improved the training speed and accuracy for HSI classification and especially improved the convergence speed, which effectively saved computing resources and had high stability. [ABSTRACT FROM AUTHOR]

In order to make full use of the spatial–spectral information of hyperspectral images (HSIs), a multiscale three-dimensional (3-D) convolutional neural network (CNN) model is proposed to extract the features of HSIs and classify them. First, the principal component analysis method is used to reduce the dimension of the original HSI, reduce the spectrum size on the basis of retaining the majority of spectrum information, and improve the training speed of the model. Second, at present, most of the HSI classification methods are based on spectral features and ignore the pixels' neighborhood information on each band, thus a 3-D-CNN model is proposed for convolution feature extraction using a spatial–spectral joint method, which makes full use of the spatial–spectral information of HSI and greatly improves the classification effect of the model. Finally, traditional CNN models mostly use single-size convolution kernels. Several convolution kernels of different sizes are added to the convolution layer to extract the spatial features of different sizes, which effectively improves the classification ability of the model. The experimental results show that the multiscale 3-D-CNN model designed, when used on three datasets: Indian Pines, Pavia University, and Salinas, reached 97.54%, 99.78%, and 99.24% overall classification accuracy, respectively. Compared with the traditional single-scale two-dimensional-CNN model, the overall classification accuracy is improved by about 1% to 2%, which can better complete the classification task of HSIs. [ABSTRACT FROM AUTHOR]