A Feature Embedding Network with Multiscale Attention for Hyperspectral Image Classification

In recent years, convolutional neural networks (CNNs) have been widely used in the field of hyperspectral image (HSI) classification and achieved good classification results due to their excellent spectral-spatial feature extraction ability. However, most methods use the deep semantic features at the end of the network for classification, ignoring the spatial details contained in the shallow features. To solve the above problems, this article proposes a hyperspectral image classification method based on a Feature Embedding Network with Multiscale Attention (MAFEN). Firstly, a Multiscale Attention Module (MAM) is designed, which is able to not only learn multiscale information about features at different depths, but also extract effective information from them. Secondly, the deep semantic features can be embedded into the low-level features through the top-down channel, so that the features at all levels have rich semantic information. Finally, an Adaptive Spatial Feature Fusion (ASFF) strategy is introduced to adaptively fuse features from different levels. The experimental results show that the classification accuracies of MAFEN on four HSI datasets are better than those of the compared methods.

Automated summary

This paper proposes a hyperspectral image classification method based on a Feature Embedding Network with Multiscale Attention. By designing a Multiscale Attention Module and introducing an Adaptive Spatial Feature Fusion strategy, the method is able to extract and fuse features at different depths, resulting in better classification accuracies compared to other methods.