An Attention Based Lightweight Network For Hyperspectral Images Classification

This paper presents an attention based lightweight network (ALN) for hyperspectral image (HSI) classification, aiming to jointly optimize classification performance and parameter efficiency. It employs a light spectral feature extraction module to learn the spectral information of the input data, and then uses the proposed spatial self-attention module to aggregate the spatial information related to the center pixel. Therefore, it can extract more informative spectral-spatial features related to the classified pixels. Extensive experiments show that the proposed ALN significantly outperforms the state-of-the-art methods. The codes of this work will be available at https://github.com/xmzhang2018.