Hyperspectral Datasets

Hyperspectral datasets, comprising images with hundreds of narrow spectral bands, are crucial for advanced remote sensing and material analysis, aiming to extract detailed spectral and spatial information for various applications. Current research focuses on developing robust model architectures, including convolutional neural networks (CNNs), transformers, and autoencoders, often coupled with self-supervised learning and data augmentation techniques to address challenges like limited training data and high dimensionality. These advancements are improving the accuracy and efficiency of tasks such as image classification, super-resolution, anomaly detection, and material segmentation, impacting fields ranging from precision agriculture to environmental monitoring and mineral exploration.