HResNetAM: Hierarchical Residual Network With Attention Mechanism for Hyperspectral Image Classification

This article proposes a novel hierarchical residual network with attention mechanism (HResNetAM) for hyperspectral image (HSI) spectral-spatial classification to improve the performance of conventional deep learning networks. The straightforward convolutional neural network-based models have limitations in exploiting the multiscale spatial and spectral features, and this is the key factor in dealing with the high-dimensional nonlinear characteristics present in HSIs. The proposed hierarchical residual network can extract multiscale spatial and spectral features at a granular level, so the receptive fields range of this network will be increased, which can enhance the feature representation ability of the model. Besides, we utilize the attention mechanism to set adaptive weights for spatial and spectral features of different scales, and this can further improve the discriminative ability of extracted features. Furthermore, the double branch structure is also exploited to extract spectral and spatial features with corresponding convolution kernels in parallel, and the extracted spatial and spectral features of multiple scales are fused for hyperspectral image classification. Four benchmark hyperspectral datasets collected by different sensors and at different acquisition time are employed for classification experiments, and comparative results reveal that the proposed method has competitive advantages in terms of classification performance when compared with other state-of-the-art deep learning models.

Automated summary

This study introduces a novel hierarchical residual network combined with attention mechanism for hyperspectral image classification to extract multiscale spatial and spectral features, showing competitive advantages in classification performance compared to other state-of-the-art deep learning models.