通过自适应张量分解提升高光谱图像分类精度与效率

Hyperspectral image classification plays a pivotal role in precision agriculture, providing accurate insights into crop health monitoring, disease detection, and soil analysis. However, traditional methods struggle with high-dimensional data, spectral-spatial redundancy, and the scarcity of labeled samples, often leading to suboptimal performance. To address these challenges, we propose the Self-Adaptive Tensor- Regularized Network (SDTN), which combines tensor decomposition with regularization mechanisms to dynamically adjust tensor ranks, ensuring optimal feature representation tailored to the complexity of the data. Building upon SDTN, we propose the Tensor-Regularized Network (TRN), which integrates the features extracted by SDTN into a lightweight network capable of capturing spectral-spatial features at multiple scales. This approach not only maintains high classification accuracy but also significantly reduces computational complexity, making the framework highly suitable for real-time deployment in resource-constrained environments. Experiments on PaviaU datasets demonstrate significant improvements in accuracy and reduced model parameters compared to state-of-the-art methods.