The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
Hyperspectral Image Denoising via Noise-Adjusted Iterative Low-Rank Matrix Approximation
Abstract
Due to the low-dimensional property of clean hyperspectral images (HSIs), many low-rank-based methods have been proposed to denoise HSIs. However, in an HSI, the noise intensity in different bands is often different, and most of the existing methods do not take this fact into consideration. In this paper, a noise-adjusted iterative low-rank matrix approximation (NAILRMA) method is proposed for HSI denoising. Based on the low-rank property of HSIs, the patchwise low-rank matrix approximation (LRMA) is established. To further separate the noise from the signal subspaces, an iterative regularization framework is proposed. Considering that the noise intensity in different bands is different, an adaptive iteration factor selection based on the noise variance of each HSI band is adopted. This noise-adjusted iteration strategy can effectively preserve the high-SNR bands and denoise the low-SNR bands. The randomized singular value decomposition (RSVD) method is then utilized to solve the NAILRMA optimization problem. A number of experiments were conducted in both simulated and real data conditions to illustrate the performance of the proposed NAILRMA method for HSI denoising.
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Citations
Cites
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Cited by
Ghamisi, P., Yokoya, N., Li, J., Liao, W., Liu, S., Plaza, J., Rasti, B., & Plaza, A. (2017) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2017.2762087 · Cited by 909
Total-Variation-Regularized Low-Rank Matrix Factorization for Hyperspectral Image Restoration
He, W., Zhang, H., Zhang, L., & Shen, H. (2016) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2015.2452812 · Cited by 667
Self-citationFast Hyperspectral Image Denoising and Inpainting Based on Low-Rank and Sparse Representations
Zhuang, L., & Bioucas-Dias, J. M. (2018) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2018.2796570 · Cited by 363
Noise Reduction in Hyperspectral Imagery: Overview and Application
Rasti, B., Scheunders, P., Ghamisi, P., Licciardi, G., & Chanussot, J. (2018) · Remote Sensing · doi:10.3390/rs10030482 · Cited by 346
Metadata
- Authors
- He, W., Zhang, H., Zhang, L., & Shen, H.
- Venue
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- Published
- 2015-06
- Volume
- 8
- Issue
- 6
- Pages
- 3050-3061
- ISSN
- 1939-1404 (ISSN-L)
- 2151-1535
- OpenAlex
- W1991003630
- Cited by
- 269
- References
- 43
- FWCI
- 18.77
- Type
- journal article
- Language
- en
- Open access
- closed
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- He, W., Zhang, H., Zhang, L., & Shen, H. (2015). Hyperspectral Image Denoising via Noise-Adjusted Iterative Low-Rank Matrix Approximation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. https://doi.org/10.1109/jstars.2015.2398433
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