The development of learning-based hyperspectral image (HSI) compression models has recently attracted significant interest. Existing models predominantly utilize convolutional filters, which capture only local dependencies. Furthermore, they often incur high training costs and exhibit substantial computational complexity. To address these limitations, in this paper we propose Hyperspectral Compression Transformer (HyCoT) that is a transformer-based autoencoder for pixelwise HSI compression. Additionally, we introduce an efficient training strategy to accelerate the training process. Experimental results on the HySpecNet-11k dataset demonstrate that HyCoT surpasses the state-of-the-art across various compression ratios by over 1 dB with significantly reduced computational requirements. Our code and pre-trained weights are publicly available at https://git.tu-berlin.de/rsim/hycot .

本研究解决了现有高光谱图像压缩模型在局部依赖捕捉和高训练成本方面的不足。提出的高光谱压缩变换器（HyCoT）采用变换器架构，并引入了一种高效的训练策略，从而显著提升了压缩效果和训练效率。实验结果表明，HyCoT在多个压缩比率下均超过了最新的技术，且计算需求大幅降低，具有重要的实际应用潜力。

HyCoT：具有高效训练策略的高光谱压缩变换器