SSPNet: Spatial-Spectral Perception Network for Mineral Hyperspectral Image Classification

Bo Zhang, Yaxiong Chen, Ruilin Yao, Shili Xiong, Shengwu Xiong, Xiaoqiang Lu

Abstract: Unlike general scenes, mineral hyperspectral images often exhibit similar spatial and spectral characteristics across different mines, making traditional classification methods less effective due to compromised robustness. To address this, we propose a Spatial-Spectral Perception Network for mineral hyperspectral image classification. This approach divides spatial-spectral feature extraction into two stages. In the spatial feature perception stage, we introduce a Spatial Frequency Perceptron that maps three-dimensional spatial features into low-frequency and high-frequency domains. We then apply Triple-Cross-Attention to each frequency domain to better differentiate spatial features of similar mines. In the spectral perception stage, we design a Spectral Linear Perceptron using Absolute Linear Attention, which captures fine-grained spectral differences by establishing internal relationships between spectral features through Absolute Positional Weighting. This enables effective separation of similar spectra for final classification. Extensive experiments on three publicly available mineral hyperspectral image datasets and one agricultural hyperspectral dataset show that our method outperforms popular alternatives in both effectiveness and robustness. The open-source code can be accessed at https://github.com/WUTCM-Lab/SSPNet.

External IDs:doi:10.1109/tgrs.2025.3618097