Adaptive selection of spectral–spatial features for hyperspectral image classification using a modified-CBAM-based network

Convolutional neural networks (CNNs) have demonstrated strong capabilities in hyperspectral image (HSI) classification. However, it is still a challenge to adaptively adjust the size of the receptive fields (RFs) of CNNs base on the information of different scales in HSI to achieve adaptive selection of spectral–spatial features. In the paper, we modify the convolutional block attention module (CBAM) and propose a modified-CBAM-based network (MCNet) to adaptively select spectral–spatial features for HSI classification. In particular, the modified CBAM not only enables the model to adjust its RF size according to the information of different scales in HSI, but also enables the model to achieve a joint focus on important spectral and spatial features. This is very important to adaptively select more descriptive and discriminative spectral–spatial features. The proposed MCNet is compared with currently popular methods on Indian Pines, Kennedy Space Center, University of Pavia, and Botswana HSI datasets. The results show that MCNet has better classification results than other methods on overall accuracy, average accuracy, and Kappa.