Guang-Yao Hu

ABSTRACT Hyperspectral image (HSI) classification is critical for accurate land cover mapping by utilizing rich spectral and spatial information.However, existing methods often struggle with inadequate fusion of spatial and spectral features, excessive computational burden, and limited generalization capability. To overcome these challenges, this study proposes a novel dual-branch convolutional neural network framework, termed DB-FusionNet.