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.
View on arXiv@article{han2025_2410.18388,
title={ Irregular Tensor Low-Rank Representation for Hyperspectral Image Representation },
author={ Bo Han and Yuheng Jia and Hui Liu and Junhui Hou },
journal={arXiv preprint arXiv:2410.18388},
year={ 2025 }
}