Abstract
Hyperspectral images (HSIs) are often degraded by different kinds of noises. Low rank (LR)-based methods have achieved great performance in HSI denoising problem. However, the LR-based methods only consider the rank of the whole spectral space, conducting no constraints on the intrinsic structure within the LR space. In fact, the spectral vectors can be classified into different categories based on the land-covers. As a result, the spectral space can be modelled as a union of multiple LR subspaces. Regarding this structure, we introduce the framework of subspace low rank (SLR) representation into HSI denoising problem and propose a novel SLR-based denoising method for HSIs. Experiments conducted on both simulated and real data show that our method achieves great improvement over the state-of-art methods qualitatively and quantitatively.
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Acknowledgments
This work was supported in part by the National Nature Science Foundation (No. 61171117) and the Capital Health Research and Development of Special (No. 2014-2-4025) of China.
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Wang, M., Yu, J., Niu, L., Sun, W. (2017). Hyperspectral Image Denoising Based on Subspace Low Rank Representation. In: Yuan, H., Geng, J., Bian, F. (eds) Geo-Spatial Knowledge and Intelligence. GRMSE 2016. Communications in Computer and Information Science, vol 699. Springer, Singapore. https://doi.org/10.1007/978-981-10-3969-0_7
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DOI: https://doi.org/10.1007/978-981-10-3969-0_7
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