The deep convolution neural network algorithm has achieved excellent performance in hyperspectral image classification.However,these deep learning algorithms generally use first-order pooling operation,which ignores the correlation between different spectral bands.Thus,obtaining high-order statistical discriminant features is difficult.In addition,using these algorithms to choose the optimal window size and capture different receptive field information is complicated.This paper proposes a hyperspectral classification method combining covariance pooling and cross-scale feature extraction to solve the aforementioned problems.This method aims to automatically extract the complementary and discriminative information of different scales and exploit the first-and second-order pooling features to improve the classification performance.A covariance pooling and cross-scale feature extraction method is proposed for hyperspectral image classification.In this method,a cross-scale adaptive feature extraction module is designed.This module can automatically combine multiscale feature information and obtain complementary information of different visual fields,avoiding the scale selection problem.Furthermore,the first-and second-order statistics combined with spatial-spectral information are obtained using the joint pooling operation of average and fast covariance pooling.Finally,the first-and second-order pooled features are fused for classification.A total of 5％,5％,and 1％labeled samples were randomly selected from three public hyperspectral datasets,namely,Indian pines,Houston University,and Pavia University,respectively.The overall classification accuracy of the proposed algorithm reached 97.63％,98.48％,and 98.21％,and the classification performance was better than the state-of-the-art deep learning methods.Cross-scale feature extraction considers the complementary spatial-spectral information between different scales to obtain additional adaptive feature information.Combining fast covariance and average pooling,the discriminant features are obtained by pooling feature fusion to obtain superior classification results.
维普
万方数据
