Abstract
This paper presents a novel denoising method based on subspace superpixel based low rank representation for hyperspectral imagery. First, the original hyperspectral data is assumed to be low-rank in both spectral and spatial domains. The spectral low rankness of HSI data is represented by decomposing it into two sub-matrices of lower rank while the spatial low rankness is explored within superpixel based regions in the subspace. The superpixels are generated by utilizing state-of-the-art superpixel segmentation algorithms in the first principle component of the original HSI. The final model could be efficiently solved by augmented Lagrangian method (ALM). Experimental results on simulated hyperspectral dataset validate that the proposed method produces superior performance than other state-of-the-art denoising methods in terms of quantitative assessment and visual quality.
This work was supported by the Natural Science Foundation of Jiangsu Province and China (BK20150923, 61601236, 61602423), the PAPD fund and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (NRF-2015H1D3A1036067, NRF-2016R1D1A1B03934305).
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Sun, L., Wang, Y., Wang, J., Zheng, Y. (2018). A Novel Subspace Super-Pixel Based Low Rank Representation Method for Hyperspectral Denoising. In: Park, J., Loia, V., Yi, G., Sung, Y. (eds) Advances in Computer Science and Ubiquitous Computing. CUTE CSA 2017 2017. Lecture Notes in Electrical Engineering, vol 474. Springer, Singapore. https://doi.org/10.1007/978-981-10-7605-3_76
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DOI: https://doi.org/10.1007/978-981-10-7605-3_76
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