Hyperspectral image classification based on lightweight network with attention mechanism

Abstract:

Hyperspectral image (HSI) classification is a challenging task in the field of remote sensing, because the HSI has high spectral dimensionality and low spatial resolution, which makes it difficult to fully extract the spatial-spectral features of hyperspectral images in the classification task. Aiming at solving the problems of the existing convolutional neural network (CNN)-based HSI classification models, such as large parameter size, high computational cost and low classification accuracy, a lightweight network hyperspectral image classification model based on attention mechanism (AMLW-CNN) is proposed in this paper. In order to enhance feature extraction ability of the network, the spatial-spectral feature extraction module is designed based on two multiscale extraction modules. In addition, we use the residual structures to connect the convolutional layers of spatial feature extraction module and incorporate the attention mechanism to enhance the extraction of useful features. Furthermore, to reduce the number of model parameters, an asymmetric convolution and a depthwise separable convolution are introduced to replace the 3D and 2D convolution kernels, respectively. The experimental results show that classification accuracy of AMLW-CNN is better than that of the comparison algorithms, with lower computational complexity and higher robustness. The overall classification accuracies on the datasets of Indian Pines, Salinas and Pavia U has attain 98.5%,99.8% and 99.9%, respectively.