Spatial Spectral

Spatial spectral analysis focuses on integrating spatial and spectral information from various data sources, primarily hyperspectral images, to improve the accuracy and efficiency of tasks like classification, denoising, and anomaly detection. Current research emphasizes the development of hybrid models combining convolutional neural networks (CNNs) and transformers, often incorporating attention mechanisms and multi-scale processing to capture complex spatial-spectral relationships. These advancements are significantly impacting fields like remote sensing, astronomy, and medical imaging by enabling more robust and informative analysis of multi-dimensional data, leading to improved accuracy and reduced computational costs in various applications.