【摘要】 针对传统高光谱图像分类方法难以有效提取空间-光谱联合信息以及卷积神经网络难以有效关注到重要特征的现象，论文提出了基于卷积神经网络的3D-CNN结合CBAM注意力机制的高光谱图像分类方法。高光谱数据由于其高维性，给特征提取方面带来了困难，因此，论文采用三维卷积神经网络（3D-CNN）提取特征，结合视觉注意力机制使卷积神经网络更好地关注重要特征。通过在两个公共数据集上验证，证明论文方法能够取得较好的分类精度。更多还原

【Abstract】 Aiming at the phenomenon that traditional hyperspectral image classification methods are difficult to effectively extract space-spectral joint information and convolutional neural networks are difficult to effectively pay attention to important features,this paper proposes a hyperspectral image based on convolutional neural network 3D-CNN combined with CBAM attention mechanism classification. Hyperspectral data brings difficulties to feature extraction due to its high dimensionality. Therefore,this paper uses three-dimensional convolutional neural network(3D-CNN)to extract features,combined with visual attention mechanism to make convolutional neural network more important. feature. Through verification on two public data sets,it is proved that the method in this paper can achieve better classification accuracy.更多还原