Irregular Tensor Low-Rank Representation for Hyperspectral Image Representation

• URL: http://arxiv.org/abs/2410.18388v1
• Date: Thu, 24 Oct 2024 02:56:22 GMT
• Title: Irregular Tensor Low-Rank Representation for Hyperspectral Image Representation
• Authors: Bo Han, Yuheng Jia, Hui Liu, Junhui Hou,
• Abstract summary: Low-rank tensor representation is an important approach to alleviate spectral variations. Previous low-rank representation methods can only be applied to the regular data cubes. We propose a novel irregular lowrank representation method that can efficiently model the irregular 3D cubes.
• Score: 71.69331824668954
• License:
• Abstract: Spectral variation is a common problem for hyperspectral image (HSI) representation. Low-rank tensor representation is an important approach to alleviate spectral variations. However, the spatial distribution of the HSI is always irregular, while the previous tensor low-rank representation methods can only be applied to the regular data cubes, which limits the performance. To remedy this issue, in this paper we propose a novel irregular tensor low-rank representation model. We first segment the HSI data into several irregular homogeneous regions. Then, we propose a novel irregular tensor low-rank representation method that can efficiently model the irregular 3D cubes. We further use a non-convex nuclear norm to pursue the low-rankness and introduce a negative global low-rank term that improves global consistency. This proposed model is finally formulated as a convex-concave optimization problem and solved by alternative augmented Lagrangian method. Through experiments on four public datasets, the proposed method outperforms the existing low-rank based HSI methods significantly. Code is available at: https://github.com/hb-studying/ITLRR.

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