# Hyperspectral Image Spectral-Spatial Feature Extraction via Tensor Principal Component Analysis Source: [Hyperspectral Image Spectral-Spatial Feature Extraction via Tensor Principal Component Analysis]() Collection index: [llms.txt]() Data updated: 2025-10-26T03:46:34.345Z Authors: Yuemei Ren, Liang Liao, Stephen John Maybank, Yanning Zhang, Xin Liu Published: 2024-12-08T21:52:09.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()