Spectral Spatial Transformer

Spectral-spatial transformers are a class of deep learning models designed to effectively analyze data with both spatial and spectral dimensions, such as hyperspectral images and electroencephalography (EEG) signals. Current research focuses on integrating transformers with convolutional neural networks (CNNs) and employing various attention mechanisms to capture long-range dependencies and multiscale features within these datasets, often using architectures like 3D Swin Transformers and recurrent transformer U-Nets. These advancements improve classification, denoising, super-resolution, and object detection tasks in diverse applications, leading to more accurate and efficient processing of complex, high-dimensional data.