Convmixer with Selective Kernel Attention for Hyperspectral Image Classification

This paper presents an efficient approach for hyperspectral image classification based on ConvMixer networks. To boost the capabilities of this network, we add an attention layer based on the idea of selective kernels (SK). This layer combines the information obtained by applying kernels of different sizes to the feature maps. The aim is to capture better the spatial and channel-wise relationships for an enhanced representation of the data. The experimental results obtained on two hyperspectral datasets: WHU-Hi-HanChuan and WHU-Hi-HongHu datasets, confirm the promising capabilities of the proposed method compared to the state-of-the-art.