Self Supervised Hyperspectral

Self-supervised learning is revolutionizing hyperspectral image processing, particularly addressing challenges like data inpainting (filling missing or corrupted data) and change detection. Current research focuses on developing novel algorithms, often incorporating low-rank and sparsity constraints or leveraging deep neural networks with spatial-spectral attention mechanisms, to effectively reconstruct and analyze hyperspectral data without relying on large labeled datasets. These advancements are significantly improving the quality and usability of hyperspectral images across diverse applications, including remote sensing, medical imaging, and astronomy, by enabling more robust and efficient analysis of incomplete or noisy data.