Irregular Tensor Low-Rank Representation for Hyperspectral Image Representation

• URL: http://arxiv.org/abs/2410.18388v2
• Date: Sat, 15 Feb 2025 13:44:29 GMT
• Title: Irregular Tensor Low-Rank Representation for Hyperspectral Image Representation
• Authors: Bo Han, Yuheng Jia, Hui Liu, Junhui Hou,
• Abstract summary: Spectral variations pose a common challenge in analyzing hyperspectral images (HSI) Low-rank tensor representation has emerged as a robust strategy, leveraging inherent correlations within HSI data. We propose a novel model for irregular tensor lowrank representation tailored to efficiently model irregular 3D cubes.
• Score: 71.69331824668954
• License:
• Abstract: Spectral variations pose a common challenge in analyzing hyperspectral images (HSI). To address this, low-rank 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 low-rank 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 convex-concave optimization problem and solved using an alternative 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. The code is publicly available at https://github.com/hb-studying/ITLRR.

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