A Dual-Stage Wavelet and Linear Attention Enhancement Network for Agricultural Hyperspectral Image Classification

Yaxiong Chen, Xinyu Zhang, Bo Zhang, Shili Xiong, Shengwu Xiong, Xiaoqiang Lu

Abstract: Hyperspectral image (HSI) classification faces unique challenges in agricultural scenario due to spectral-spatial feature similarity caused by complex planting structures and high spectral similarity. Existing spatial-spectral joint feature extraction methods fail to fully exploit the advantages of spatial and spectral information, thus have certain limitations and cannot effectively distinguish similar crops in agricultural scenarios. To address these limitations, we proposed a dual-stage wavelet and linear attention enhancement network (DSW-LAN) for agricultural HSI classification, addressing the challenges of complex spatial-spectral information and high redundancy. We integrates a direction factorized deformable 3D Convolution (DFDWConv3D) module to capture multi-scale spatial-spectral features through adaptive kernel adjustments, while wavelet transform decomposes spatial features into low-frequency (structural) and high-frequency (textural) components for targeted enhancement. Additionally, a spectral probe-guided linear attention mechanism efficiently models long-range spectral dependencies with reduced computational complexity by prioritizing discriminative bands. Experimental results demonstrate superior performance on three challenging agricultural HSI datasets, achieving enhanced classification accuracy with reduced computational complexity.

External IDs:doi:10.1109/tgrs.2025.3628164