Learning Based Hyperspectral

Learning-based hyperspectral image (HSI) processing aims to leverage the power of deep learning to overcome limitations of traditional HSI analysis, primarily focusing on tasks like compression, reconstruction, fusion, object detection, and classification. Current research emphasizes the development of novel neural network architectures, including transformers and autoencoders, to effectively capture both spatial and spectral information within HSI data, often incorporating attention mechanisms and adversarial training techniques to improve performance and efficiency. These advancements are significantly impacting various fields, enabling improved remote sensing analysis, medical imaging diagnostics (e.g., brain tumor detection), and other applications requiring high-dimensional spectral data processing.