用张量方法提取高光谱图像的谱空间特征，提升分类性能。
Hyperspectral Image Spectral-Spatial Feature Extraction via Tensor Principal Component Analysis
- 将循环卷积融入张量结构，联合建模谱与空间信息。
- 在多个基准数据集上，新方法分类准确率优于传统PCA和现有先进方法。
- 适合需要高效融合多维光谱信息的研究者使用。
本文针对高光谱图像分类中的谱空间特征提取难题，提出一种基于张量的新框架。该方法将循环卷积引入张量结构，有效捕捉并整合光谱与空间信息。在此基础上，将传统主成分分析（PCA）拓展为张量主成分分析（TPCA），利用高光谱数据的固有多维结构实现更优的特征表示。在多个基准高光谱数据集上的实验表明，采用TPCA特征的分类模型性能持续优于传统PCA及其他先进方法，验证了该张量框架在推进高光谱图像分析方面的潜力。
原文摘要 · Abstract (English)
This paper addresses the challenge of spectral-spatial feature extraction for hyperspectral image classification by introducing a novel tensor-based framework. The proposed approach incorporates circular convolution into a tensor structure to effectively capture and integrate both spectral and spatial information. Building upon this framework, the traditional Principal Component Analysis (PCA) technique is extended to its tensor-based counterpart, referred to as Tensor Principal Component Analysis (TPCA). The proposed TPCA method leverages the inherent multi-dimensional structure of hyperspectral data, thereby enabling more effective feature representation. Experimental results on benchmark hyperspectral datasets demonstrate that classification models using TPCA features consistently outperform those using traditional PCA and other state-of-the-art techniques. These findings highlight the potential of the tensor-based framework in advancing hyperspectral image analysis.
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