Hyperspectral Image Classification Using Dual-Branch Residual Networks
Hyperspectral image classification is a basic operation for understanding and applying hyperspectral images,and its accuracy is a key index for measuring the performance of the algorithm used.A novel two-branch residual network(DSSRN)is proposed that can extract robust features of hyperspectral images and is applicable to hyperspectral image classification for improving classification accuracy.First,the Laplace transform,principal component analysis(PCA),and data-amplification methods are used to preprocess hyperspectral image data,enhance image features,remove redundant information,and increase the number of samples.Subsequently,an attention mechanism and a two-branch residual network are used,where spectral and spatial residual networks are adopted in each branch to extract spectral and spatial information as well as to generate one-dimensional feature vectors.Finally,image-classification results are obtained using the fully connected layer.Experiments are conducted on remote-sensing datasets at the Indian Pine,University of Pavia,and Kennedy Space Center.Compared with the two-branch ACSS-GCN,the classification accuracy of proposed model shows 1.94、0.27、20.85 percentage points improvements on the three abovementioned datasets,respectively.
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