Hyperspectral Image Compression Method Based on Spatio-Spectral Joint Feature Extraction and Attention Mechanism

Traditional hyperspectral image compression methods often struggle to achieve high compression ratios while maintaining satisfactory reconstructed image quality under low-bitrate conditions. With the progressive development of deep learning, it has demonstrated significant advantages in lossy image compression research. Compared to visible light images, hyperspectral images possess rich spectral information. When directly applying visible light image compression models to hyperspectral image compression, the spectral information of hyperspectral images is overlooked, making it difficult to achieve optimal compression performance. In this paper, we combine the characteristics of hyperspectral images by extracting spatial and spectral features and performing fusion-based encoding and decoding to achieve end-to-end lossy compression of hyperspectral images. The structures of the encoding end and the decoding end are in symmetry. Additionally, attention mechanism is incorporated to enhance reconstruction quality. The proposed model is compared with the latest hyperspectral image compression standard algorithms to validate its effectiveness. Experimental results show that, under the same image quality, the proposed method reduces the bpp (bits per pixel) by 4.67% compared to CCSDS123.0-B-2 on the Harvard hyperspectral dataset while also decreasing the spectral angle loss by 13.68%, achieving better performance.