Dual-channel mixed spectral-spatial features combined with second-order pooling for hyperspectral image classification

In order to effectively improve the classification accuracy of hyperspectral images, we propose a classification method based on the combination of dual-channel mixed spectral-spatial features and second-order pooling. Firstly, we use PCA to reduce the dimensionality of the hyperspectral image; secondly, the fused spectral-spatial features are extracted by stacking three dual-channel mixed spectral-spatial residual modules; finally, the second-order pooling is used to fully mine the fused spectral-spatial features, and through the fully connected layer to realize classification. We conduct comparative experiments on Indian Pines and Pavia University datasets, and the overall accuracy of the proposed method respectively reaches 99.17% and 99.74%, which are higher than other classification methods. This verifies the effectiveness of the proposed method classification function.