Rethinking Hyperspectral Image Three-Dimensional Feature Extraction with Singular Spectrum Analysis

Hyperspectral images (HSI) are three-dimensional (3D) data cubes with a unique joint dependency structure characterized by spectral continuity and spatial similarity. While Singular Spectrum Analysis (SSA) has proven to be an effective method for feature extraction, existing SSA-like methods do not adequately capture 3D features of HSI. To overcome this limitation, we rethink the 3D representation of HSI through dimensionality extension and propose a novel 3DSSA framework. In 3DSSA, we introduce patch embedding and adaptive embedding techniques to construct the trajectory tensor corresponding to the HSI, allowing us to explore its low-rank intrinsic features through tensor decomposition. Experimental results demonstrate the superior performance of the proposed method in capturing spatial detail information and improving feature classification accuracy. The code of this work is available at https://github.com/RsAI-lab.