6 research outputs found
Hyperspectral Image Restoration via Total Variation Regularized Low-rank Tensor Decomposition
Hyperspectral images (HSIs) are often corrupted by a mixture of several types
of noise during the acquisition process, e.g., Gaussian noise, impulse noise,
dead lines, stripes, and many others. Such complex noise could degrade the
quality of the acquired HSIs, limiting the precision of the subsequent
processing. In this paper, we present a novel tensor-based HSI restoration
approach by fully identifying the intrinsic structures of the clean HSI part
and the mixed noise part respectively. Specifically, for the clean HSI part, we
use tensor Tucker decomposition to describe the global correlation among all
bands, and an anisotropic spatial-spectral total variation (SSTV)
regularization to characterize the piecewise smooth structure in both spatial
and spectral domains. For the mixed noise part, we adopt the norm
regularization to detect the sparse noise, including stripes, impulse noise,
and dead pixels. Despite that TV regulariztion has the ability of removing
Gaussian noise, the Frobenius norm term is further used to model heavy Gaussian
noise for some real-world scenarios. Then, we develop an efficient algorithm
for solving the resulting optimization problem by using the augmented Lagrange
multiplier (ALM) method. Finally, extensive experiments on simulated and
real-world noise HSIs are carried out to demonstrate the superiority of the
proposed method over the existing state-of-the-art ones.Comment: 15 pages, 20 figure
Hyperspectral Image Denoising With Group Sparse and Low-Rank Tensor Decomposition
Hyperspectral image (HSI) is usually corrupted by various types of noise, including Gaussian
noise, impulse noise, stripes, deadlines, and so on. Recently, sparse and low-rank matrix decomposition
(SLRMD) has demonstrated to be an effective tool in HSI denoising. However, the matrix-based SLRMD
technique cannot fully take the advantage of spatial and spectral information in a 3-D HSI data. In this paper,
a novel group sparse and low-rank tensor decomposition (GSLRTD) method is proposed to remove different
kinds of noise in HSI, while still well preserving spectral and spatial characteristics. Since a clean 3-D HSI
data can be regarded as a 3-D tensor, the proposed GSLRTD method formulates a HSI recovery problem
into a sparse and low-rank tensor decomposition framework. Specifically, the HSI is first divided into a set
of overlapping 3-D tensor cubes, which are then clustered into groups by K-means algorithm. Then, each
group contains similar tensor cubes, which can be constructed as a new tensor by unfolding these similar
tensors into a set of matrices and stacking them. Finally, the SLRTD model is introduced to generate noisefree
estimation for each group tensor. By aggregating all reconstructed group tensors, we can reconstruct a
denoised HSI. Experiments on both simulated and real HSI data sets demonstrate the effectiveness of the
proposed method.This paper was supported in part by the National Natural Science Foundation of China under Grant 61301255, Grant 61771192, and
Grant 61471167, in part by the National Natural Science Fund of China for Distinguished Young Scholars under Grant 61325007, in part
by the National Natural Science Fund of China for International Cooperation and Exchanges under Grant 61520106001, and in part by the
Science and Technology Plan Project Fund of Hunan Province under Grant 2015WK3001 and Grant 2017RS3024.Peer Reviewe
Interpretable Hyperspectral AI: When Non-Convex Modeling meets Hyperspectral Remote Sensing
Hyperspectral imaging, also known as image spectrometry, is a landmark
technique in geoscience and remote sensing (RS). In the past decade, enormous
efforts have been made to process and analyze these hyperspectral (HS) products
mainly by means of seasoned experts. However, with the ever-growing volume of
data, the bulk of costs in manpower and material resources poses new challenges
on reducing the burden of manual labor and improving efficiency. For this
reason, it is, therefore, urgent to develop more intelligent and automatic
approaches for various HS RS applications. Machine learning (ML) tools with
convex optimization have successfully undertaken the tasks of numerous
artificial intelligence (AI)-related applications. However, their ability in
handling complex practical problems remains limited, particularly for HS data,
due to the effects of various spectral variabilities in the process of HS
imaging and the complexity and redundancy of higher dimensional HS signals.
Compared to the convex models, non-convex modeling, which is capable of
characterizing more complex real scenes and providing the model
interpretability technically and theoretically, has been proven to be a
feasible solution to reduce the gap between challenging HS vision tasks and
currently advanced intelligent data processing models