Shape-Adaptive High-Order Tensor Decomposition for Hyperspectral Feature Extraction

Hyperspectral images (HSIs) offer rich spectral–spatial information but pose challenges due to a high dimensionality and noise. This letter proposes a shape-adaptive high-order tensor decomposition (SAHTD) framework for hyperspectral feature extraction. The SAHTD employs a shape-adaptive sampling strategy based on a simplified local polynomial approximation and intersection confidence interval (LPA-ICI) to enhance edge segmentation. It integrates tensor Tucker decomposition and a nuclear norm-constrained regression model to extract discriminative features while preserving low-rank structures. Experiments on the Pavia University and Houston 2013 datasets demonstrate that the SAHTD achieves superior classification performance compared to relevant methods, validating its effectiveness for HSI analysis.