Hyperspectral Image Compression

Hyperspectral image compression aims to efficiently reduce the massive data volume inherent in hyperspectral images while preserving crucial spectral and spatial information. Current research heavily utilizes deep learning approaches, particularly autoencoders (including transformer-based architectures) and implicit neural representations, focusing on improving compression ratios and reconstruction fidelity. These advancements are crucial for enabling wider adoption of hyperspectral imaging in applications like remote sensing and medical imaging, where efficient data handling is paramount. The development of large-scale benchmark datasets is also driving progress by facilitating the comparison and improvement of different compression techniques.