Hyperspectral Image Classification Employing Spatial-Spectral Feature Supported by 3D Convolution and Transformer 1.2 NMF
Due to its strong ability to extract local features,CNN is still the mainstream depth model in hyperspectral image processing and analysis.However,CNN has limited receptive field,cannot establish long-distance dependence,and is limited in learning global semantic information.Transformer's self-attention mechanism can perform attention calcula-tions at each position in the input sequence to effectively capture global context information.How to realize the technical coupling of CNN and Transformer and make full use of spatial and spectral information for hyperspectral remote sensing image classification is an important problem to be studied.In view of this,a new hyperspectral remote sensing image clas-sification method based on 3D convolution and Transformer is proposed in this paper,which attempts to improve the inter-pretation ability by combining spatial spectral features.Firstly,the principal component analysis method is used to reduce the dimensionality of hyperspectral remote sensing images in the vertical direction.Then the spatial features of the remote sensing images after dimensionality reduction are extracted along the horizontal direction by non-negative matrix decom-position algorithm,and then the remote sensing images processed by the two tools are combined to fully retain the infor-mation.Then the three-dimensional convolution check is used to extract the spatial and spectral features of the remote sensing images.Finally,the attention mechanism of Transformer is used to establish long-distance dependencies on remote sensing image sequences that extract spatial and spectral information,and a multi-layer perceptron is used to complete classification tasks.Experiment shows that the proposed method exhibits better classification performance than the com-parison method on the WHU-Hi Longkou,Hanchuan,Honghu,and Matiwan Village datasets in Xiong'an New Area,in-dicating that the proposed method has a certain degree of generalization and robustness.
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