遥感图像分类是根据图像信息所反映的不同特征来区分不同类型目标的重要方法，传统卷积网络在解决这类问题时，由于深度的加深容易导致网络退化和计算量增大。鉴于此，提出一种改进的残差网络结构模型从而提高网络的分类性能。首先，利用三维神经网络和残差神经网络对Salinas scene数据集进行特征提取来减少数据量；其次，在残差神经网络加入通道注意力机制，进而提升网络对于高光谱图像特征权重的高效识别；最后，完成三维神经网络与残差神经网络的对比实验。结果显示优化后的残差网络更加高效。

Remote sensing image classification is an important method to distinguish different types of targets according to the different features reflected in the image information. When traditional convolutional networks solve this kind of problem, it could easily cause network degradation and increase the amount of calculation due to the deepening of depth. In view of this, the paper proposes an improved structural model of residual network to enhance the classification performance of the network. Firstly, a 3D neural network and a residual neural network are used to extract features from the Salinas scene dataset to reduce the data volume. Then a channel attention mechanism is added to the residual neural network to improve the efficient recognition of hyperspectral image feature weights. Finally, a comparison experiment between the 3D neural network and the residual neural network is conducted, and the results show that the optimized residual network is more efficient.