Convolutional Neural Networks (CNNs) are widely used in various fields, and have shown good performance in hyperspectral image (HSI) classification. Recently, utilizing deep networks to learn spatial-spectral features has become of great interest. However, excessively increasing the depth of network may result in overfitting. Moreover, in HSI classification, the existing network models ignore the strong complementary yet correlated spatial-spectral information among different hierarchical layers. In order to address these two problems, a novel CNN-based method for HSI classification is proposed. Firstly, it considers fusing the outputs of recurrent two layers in each large convolutional block and thereby using the fusion result as the input of next layer, which facilitates the extraction of discriminative features. Next, the spectral-spatial features are extracted by cascading spectral features to four-scale spatial features from shallow to deep layers. Finally, a 1$\times$1 convolution layer is used to interact and integrate information across channels. Without increasing the number of training samples and the size of pixel patches at the training stage, the proposed approach achieves the state-of-the-art results in the experiment on three well-known hyperspectral images.

卷积神经网络（CNN）广泛用于各个领域，并且在高光谱图像（HSI）分类中显示出良好的性能。近年来，利用深度网络学习空间光谱特征已引起人们极大的兴趣。但是，过度增加网络深度可能会导致过度拟合。此外，在HSI分类中，现有的网络模型忽略了不同层次层之间强互补但相关的空间光谱信息。为了解决这两个问题，提出了一种新的基于CNN的HSI分类方法。首先，它考虑将每个大卷积块中递归两层的输出融合在一起，从而将融合结果用作下一层的输入，这有助于提取判别特征。下一个，通过从浅层到深层将光谱特征级联为四级空间特征，从而提取光谱空间特征。最后是1$\times$1个卷积层用于在通道之间进行交互和集成信息。在不增加训练样本数量和训练阶段像素块大小的情况下，所提出的方法在三个著名的高光谱图像实验中获得了最新的结果。