Fine-Scale Classification of Wetland in the Yellow River Estuary based on UAV Hyperspectral Data

Accurate information on ground features is essential for the ecological protection and efficient management of this wetland. The resolution of satellite remote sensing images often falls short of the requirements for fine-scale classification. The Yellow River Estuary wetland is primarily vegetated, characterized by similar spectral curves. The use of images with limited spectral information additionally restricts the precision of classification. In response to the above issues, this study proposes a multi-branch fusion Transformer (MB Transformer) method based on high spatial resolution unmanned aerial vehicle (UAV) hyperspectral data. By constructing vegetation index features, dimensionality-reduced spectral features, and original spectral features, a multi-branch fusion Transformer model is used to classify in the Yellow River Estuary wetland at a fine scale. The results indicate that this method has obvious advantages in fine-scale classification of wetland in the Yellow River Estuary.