Spectral Spatial Mamba

Spectral-spatial Mamba is a novel deep learning architecture designed for efficient and accurate processing of hyperspectral images (HSIs), primarily focusing on classification and denoising tasks. Current research emphasizes variations of the Mamba model, incorporating techniques like wavelet transformations, interval grouping, and 3D processing to enhance feature extraction and reduce computational complexity compared to transformers. These advancements improve the handling of HSI's high dimensionality and spatial-spectral dependencies, leading to improved accuracy and efficiency in remote sensing applications such as land cover mapping and environmental monitoring. The linear computational complexity of Mamba offers a significant advantage over quadratic-complexity alternatives for processing large-scale remote sensing data.