Spectral Spatial Dimension

Spectral-spatial dimension research focuses on effectively leveraging both spectral and spatial information within data, primarily in hyperspectral imaging and high-contrast spectroscopy, to improve analysis and classification accuracy. Current research emphasizes the use of deep learning models, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), often incorporating techniques like tensor decomposition and neural architecture search to optimize feature extraction and reduce computational complexity. This work is significant for enhancing the capabilities of various applications, such as exoplanet detection, remote sensing, and solving high-dimensional partial differential equations, by enabling more efficient and accurate analysis of complex, multi-dimensional datasets.