Hyperspectral Image Classification

Hyperspectral image classification aims to automatically assign labels to each pixel in a hyperspectral image, identifying different materials or land cover types based on their unique spectral signatures. Current research heavily focuses on improving the accuracy and efficiency of classification using deep learning architectures, including convolutional neural networks (CNNs), transformers, and novel state-space models like Mamba, often incorporating spatial and spectral information in innovative ways. These advancements are crucial for various applications, such as precision agriculture, environmental monitoring, and medical imaging, enabling more accurate and timely analysis of complex datasets. Furthermore, significant effort is dedicated to addressing challenges like computational cost, limited training data, and uncertainty quantification in classification results.