Hyperspectral Image Classification Based on Three-dimensional Convolutional Neural Network
Aiming at the phenomenon that traditional hyperspectral image classification methods are difficult to effectively ex-tract space-spectral joint information and convolutional neural networks are difficult to effectively pay attention to important fea-tures,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 mecha-nism 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.
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