Abstract
Hyperspectral data have been widely used in various fields due to its rich spectral and spatial information in recent years. Yet, hyperspectral images are always tainted by a variety of mixed noises. These noises seriously limit the accuracy of subsequent applications. To remove noise, this paper, based on low-rank tensor decomposition, combined with non-local self-similar prior information, proposes a tensor-based non-local low-rank denoising model, where non-local self-similarity uses mainly spatial correlation while low-rank tensor decomposition method uses mainly spectral correlation between bands. Traditional tensor-based methods are commonly NP-hard to compute and are sensitive to sparse noise. However, the method proposed in this paper can separate efficiently the low-rank clean image from Gaussian noise and sparse noise (pulses, deadlines, stripes, speckle, etc.) by using a new tensor singular value decomposition (T-SVD) and tensor nuclear norm (TNN). The NP-hard task was also achieved well by the alternating direction multiplier method. Due to the full use of spectral and spatial information of the data, Gaussian noise and sparse noise can be effectively removed. The effectiveness of our algorithm was verified through experiments using simulated and real data.
Highlights
The hyperspectral imaging spectrometer has important contributions to earth observation and remote sensing
These algorithms include band-wise K-singular value decomposition (SVD) [10], band-wise BM3D [11], BM4D [21] and low-rank matrix recovery (LRMR) [7], which are two-dimensional extended to three-dimensional methods, and PARAFAC [24], low-rank tensor approximation (LRTA) [28] and tensor dictionary learning (TDL) [29], which are tensor-based methods
To evaluate and analyze these optimal parameters, we used the Washington DC Mall dataset and the Pavia City Center dataset as an example in Case 2, and used mean PSNR (MPSNR) as the evaluation measure of the analysis
Summary
The hyperspectral imaging spectrometer has important contributions to earth observation and remote sensing. Since the tensor-based denoising methods fully retain useful spatial structure information, they perform better than the 2D extended methods [38]–[40] These existing algorithms have achieved good results in hyperspectral noise reduction. Non-local self-similarity (NSS) [41], [42] uses mainly spatial correlation while low-rank matrix decomposition method [41] uses mainly spectral correlation between bands To utilize both spectral correlation and spatial information in hyperspectral data, a tensor-based non-local low-rank denoising model is proposed here. This method can eliminate both Gaussian noise and sparse noise (including impulse noise, deadlines, stripes, and speckle).
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