Irregular tensor low-rank representation for hyperspectral image representation
Author(s)
Jia, Yuheng
Author(s)
Han, B.
Hou, J.
Date Issued
2024
Abstract
Spectral variations pose a common challenge in analyzing hyperspectral images (HSI). To address this, lowrank tensor representation has emerged as a robust strategy, leveraging inherent correlations within HSI data. However, the spatial distribution of ground objects in HSIs is inherently irregular, existing naturally in tensor format, with numerous class-specific regions manifesting as irregular tensors. Current low-rank representation techniques are designed for regular tensor structures and overlook this fundamental irregularity in real-world HSIs, leading to performance limitations. To tackle this issue, we propose a novel model for irregular tensor lowrank representation tailored to efficiently model irregular 3D cubes. By incorporating a non-convex nuclear norm to promote low-rankness and integrating a global negative low-rank term to enhance the discriminative ability, our proposed model is formulated as a constrained optimization problem and solved using an alternating augmented Lagrangian method. Experimental validation conducted on four public datasets demonstrates the superior performance of our method compared to existing state-of-the-art approaches.
SFU Affiliated Publication
Yes
Availability at SFU Library
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