Spatial-spectral Total Variation Research Articles

Spatial–Spectral Total Variation-Regularized Low-Rank Tensor Representation for Hyperspectral Anomaly Detection

Hyperspectral anomaly detection is a vital aspect of remote sensing as it focuses on identifying pixels with distinct spectral–spatial properties in comparison to their background representations. However, existing methods for anomaly detection in HSIs often overlook the spatial correlation between pixels by converting the three-dimensional tensor data into its folded form of independent signatures, which may lead to insufficient detection performance. To address this limitation, we develop an anomaly detection algorithm from a tensor representation perspective, which begins by separating the observed hyperspectral image into background and anomaly cubes. We leverage the tensor nuclear norm (TNN) to capture the inherent low-rank structure of background cube globally. This allows us to effectively model and represent the background information. To further improve the detection performance, we introduce spatial–spectral total variation (SSTV) for effectively promoting piecewise smoothness of the background tensor, aiding in the identification of anomalies. Additionally, we incorporate RX-derived attention weights-guided [Formula: see text] norm. This encourages group sparsity of anomalous pixels, improving the precision of anomaly detection. To solve our proposed method, we employ the alternating direction method of multipliers (ADMM), ensuring guaranteed convergence and efficient computation. Through experiments on different kinds of hyperspectral real datasets, we have demonstrated that our method surpasses several state-of-the-art detectors.

Tensor recovery using the tensor nuclear norm based on nonconvex and nonlinear transformations

Recently, the tensor completion problem has witnessed significant advancements with the introduction of the tensor nuclear norm based on self-supervised nonlinear transformations (S2NTNN). However, the tensor nuclear norm serves as a convex substitute for the tubal rank of tensors, often resulting in suboptimal solutions to the original problem. In this work, we propose a novel method, called Nonconvex Nonlinear transformation-based Tensor Nuclear Norm (N2TNN), which builds upon nonlinear transformations and introduces a generalized nonconvex function to better exploit the prior information of underlying tensors. Additionally, we utilize regularization techniques, namely spatial total variation (TV) and spatial spectral total variation (SSTV), to enhance both the details and edge information. Extensive numerical experiments unequivocally validate the superior performance of our proposed method when compared to other state-of-the-art techniques in terms of both numerical accuracy and visual quality.

Hyperspectral fruit image restoration using non-convex optimization

Hyperspectral (HS) imaging captured at low proximity ranges has become an effective method for assessing the quality and safety of food products in recent years. The majority of Hyperspectral cameras are push-broom type, which often contributes random noise and stripes to the spectral images. As a result, the quality of the collected HS images and the precision of the subsequent processing will both suffer. Despite the fact that there are several denoising techniques that have been studied and recorded in the literature, they frequently fail to fully restore the original HS images when there are strong stripe noises and other intricate mixed noises. In this study, we introduce a new method based on tensor logarithmic Schatten-p norm minimization with anisotropic spatial spectral total variation (ASSTV) regularization for simultaneous denoising and destriping of line scan-based hyperspectral images. Tensor logarithmic Schatten-p norm improves denoising performance by deleting the smaller singular values while protecting the larger singular values. Through the application of ASSTV, stripes and Gaussian noise are eliminated while maintaining the spatial–spectral smoothness of HS images and the directional characteristics of the stripes. The augmented Lagrange multiplier (ALM) approach is used to address the ensuing optimization problem. The quantitative and visual assessments reveal that the suggested technique has improved performance than its counterparts.

Hyperspectral image reconstruction based on low-rank coefficient tensor and global prior

ABSTRACT Compressive sensing (CS) has been employed to compress and store hyperspectral images (HSI) to transfer extensive data efficiently, and obtaining high-quality reconstructed images is crucial for subsequent applications. Model-based methods usually capture the priors of HSI, including sparsity, global and nonlocal low-rankness, nonlocal self-similarity and global correlation. However, it is challenging to reasonably integrate different regularizations in a united framework and exploit various priors mentioned to reconstruct high-quality images. In this paper, a tensor optimization model based on the low-rank coefficient tensor and global prior (LRCTGP) is proposed for HSI reconstruction. Complementary regularization terms are integrated into a united framework and effectively promote the reconstruction results. First, we apply the mode- tensor-matrix product to decompose the original HSI rather than employing the regularization to capture the spectral low-rankness and simultaneously constrain the original and feature HSI. Then, tensor ring decomposition is employed to constrain the coefficient tensor with fewer bands, which is more efficient in the low-rank approximation and has smaller calculation costs than applying it to the original HSI. Moreover, integrating BM3D as a regularizer is more efficient than other patch-based models. Finally, considering the smoothness and global correlation, spatial-spectral total variation (SSTV) is applied to compensate for the shortcomings after decomposing the original and feature HSI, which complements the overall structure and details of reconstructed images and improves the reconstruction quality. Alternating direction method of multipliers (ADMM) is used to optimize the proposed model. Experimental results of the LRCTGP model on different HSI datasets are better than existing state-of-the-art approaches, which proves the superiority.

Hybrid Low-Rank Tensor CP and Tucker Decomposition with Total Variation Regularization for HSI Noise Removal

The acquired hyperspectral images (HSIs) are affected by a mixture of several types of noise, which often suffer from information missing. Corrupted HSIs limit the precision of the subsequent processing. In this paper, the weighted Total Variation-regularized Hybrid Model of CP and Tucker (WTV-HMCT) is proposed to accurately identify the intrinsic structures of the clean HSIs. By jointly minimizing CP rank and Tucker rank in the low-rank tensor approximation, WTV-HMCT fully exploits the high-dimensional structure correlations of HSI. To ensure the piecewise smoothness of the recovered image, the hybrid low-rank tensor decomposition approach integrates the weighted spatial spectral total variation regularization for the separation of the noise-free HSI and mixed noise. By the Alternating Direction Method of Multipliers (ADMM), the optimization model is transformed into two subproblems. Finally, an efficient proximal alternating minimization algorithm is developed to optimize the proposed hybrid low-rank tensor decomposition efficiently. The experimental results show that the proposed model effectively handles Gauss noise, striping noise, and mixed noise and that it outperforms the most advanced methods in terms of evaluation metrics and visual evaluation.

Hyperspectral Denoising Using Asymmetric Noise Modeling Deep Image Prior

Deep image prior (DIP) is a powerful technique for image restoration that leverages an untrained network as a handcrafted prior. DIP can also be used for hyperspectral image (HSI) denoising tasks and has achieved impressive performance. Recent works further incorporate different regularization terms to enhance the performance of DIP and successfully show notable improvements. However, most DIP-based methods for HSI denoising rarely consider the distribution of complicated HSI mixed noise. In this paper, we propose the asymmetric Laplace noise modeling deep image prior (ALDIP) for HSI mixed noise removal. Based on the observation that real-world HSI noise exhibits heavy-tailed and asymmetric properties, we model the HSI noise of each band using an asymmetric Laplace distribution. Furthermore, in order to fully exploit the spatial–spectral correlation, we propose ALDIP-SSTV, which combines ALDIP with a spatial–spectral total variation (SSTV) term to preserve more spatial–spectral information. Experiments on both synthetic data and real-world data demonstrate that ALDIP and ALDIP-SSTV outperform state-of-the-art HSI denoising methods.

Anisotropic Spatial–Spectral Total Variation Regularized Double Low-Rank Approximation for HSI Denoising and Destriping

Hyperspectral images (HSIs) can finely discriminate distinct objects with a high spectral resolution, and they are widely employed in various applications. However, mixed noise severely degrades the quality of HSI and restricts the performance of subsequent tasks. As one of the critical pre-processing steps, HSI denoising has been developed rapidly, among which low-rank (LR) prior-based methods have achieved superior performance. Nevertheless, the existing approaches frequently fail to completely remove noise and reconstruct high-quality HSIs when tackling complicated mixed noise with multiple types of high-intensity stripe noise. To solve this problem, we propose an HSI denoising and destriping method based on anisotropic spatial and spectral total variation regularized double low-rank approximation (ATVDLR). The double low-rank approximation framework is devoted to separating the clean image from the mixed noise by exploiting both the global correlations of HSI tensor and the LR structure of stripe noise. Furthermore, the anisotropic spatial and spectral total variation regularization is introduced to preserve the spatial–spectral smoothness of HSI and the directional feature of stripes, thereby further suppressing high-level stripes and Gaussian noise. Finally, the alternating direction method of multipliers (ADMM) technique is designed to solve the proposed ATVDLR model. Extensive experimental results indicate that the proposed method outperforms other state-of-the-art techniques in multi-type high-intensity mixed noise reduction and image structural information protection, and has superior performance in complex mixed noise removal of real Gaofen-5 HSIs.

Hyperspectral Image Denoising Using Nonconvex Local Low-Rank and Sparse Separation With Spatial–Spectral Total Variation Regularization

In this paper, we propose a novel nonconvex approach to robust principal component analysis for HSI denoising, which focuses on simultaneously developing more accurate approximations to both rank and column-wise sparsity for the low-rank and sparse components, respectively. In particular, the new method adopts the log-determinant rank approximation and a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,log</sub> norm, to restrict the local low-rank or column-wisely sparse properties for the component matrices, respectively. For the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,log</sub> -regularized shrinkage problem, we develop an efficient, closed-form solution, which is named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,log</sub> -shrinkage operator. The new regularization and the corresponding operator can be generally used in other problems that require column-wise sparsity. Moreover, we impose the spatial-spectral total variation regularization in the log-based nonconvex RPCA model, which enhances the global piece-wise smoothness and spectral consistency from the spatial and spectral views in the recovered HSI. Extensive experiments on both simulated and real HSIs demonstrate the effectiveness of the proposed method in denoising HSIs.

Hyperspectral Snapshot Compressive Imaging With Dense Back-Projection Joint Attention Network

Hyperspectral Snapshot Compressive Imaging (SCI) system encodes three-dimensional hyperspectral images into a single two-dimensional snapshot measurement and then decodes the underlying 3D hyperspectral images by solving the compressive sensing reconstruction problem. Practical applications of SCI imaging systems require fast and high-quality reconstruction. To meet this requirement, we propose a novel encoder and decoder network with dense back-projection joint attention for hyperspectral SCI. The main contributions of our network lie in two aspects. First, we propose a dense back-projection module and deploy it in an encoder with five scales. It computes the back-projection between each scale and all its preceding scales, thereby fusing complementary information between different scales for efficient reconstruction. Second, we design a spatial-spectral attention module and deploy it in the decoder to boost reconstruction quality. By exploiting a cascade of spatial-spectral attention, it can efficiently capture spatial and spectral correlations in hyperspectral images with a low volume of parameters. In addition, a compound loss, including the reconstruction loss and the spatial-spectral total variation loss, is designed to guide network learning in an end-to-end manner. Intensive experiments on simulation and real data show that our method has obvious advantages over multiple state-of-the-art methods, achieving a significant improvement in reconstruction quality and a substantial reduction in running time.

Hyperspectral Image Destriping and Denoising Using Stripe and Spectral Low-Rank Matrix Recovery and Global Spatial-Spectral Total Variation

Hyperspectral image (HSI) is easily corrupted by different kinds of noise, such as stripes, dead pixels, impulse noise, Gaussian noise, etc. Due to less consideration of the structural specificity of stripes, many existing HSI denoising methods cannot effectively remove the heavy stripes in mixed noise. In this paper, we classify the noise on HSI into three types: sparse noise, stripe noise, and Gaussian noise. The clean image and different types of noise are treated as independent components. In this way, the image denoising task can be naturally regarded as an image decomposition problem. Thanks to the structural characteristic of stripes and the low-rank property of HSI, we propose to destripe and denoise the HSI by using stripe and spectral low-rank matrix recovery and combine it with the global spatial-spectral TV regularization (SSLR-SSTV). By considering different properties of different HSI ingredients, the proposed method separates the original image from the noise components perfectly. Both simulation and real image denoising experiments demonstrate that the proposed method can achieve a satisfactory denoising result compared with the state-of-the-art methods. Especially, it outperforms the other methods in the task of stripe noise removal visually and quantitatively.

Total Variation Regularized Low-Rank Model With Directional Information for Hyperspectral Image Restoration

Hyperspectral images (HSIs) are unavoidably polluted by various kinds of noise, which decrease the potential of subsequent processes in HSIs applications. Due to the diversity and complexity of HSIs mixed noise, including impulse noise, Gaussian noise, stripe noise and deadlines, traditional restoration technology cannot be used directly. In this paper, a novel HSIs restoration approach is proposed that integrates low-rank (LR) prior and spatial-spectral total variation with directional information. Specifically, by analyzing the characteristic of spatial-dependent edge and texture directional structure, a spatial-spectral directional total variation (SSDTV) regularization is defined. Then, considering the HSIs as a cube data, the proposed method utilizes SSDTV regularization to characterize spatial-spectral smoothness, as well as LR regularization to constrain spectral consistency. Finally, an extended alternating direction method of multipliers algorithm is designed to achieve simple and fast implementation, in which the complex optimization problem is separated into several easier subproblems. Both simulated and real-world HSIs experiments indicated that the proposed method is effective and numerically feasible for HSIs Restoration.

Hyperspectral Mixed Noise Removal via Spatial-Spectral Constrained Unsupervised Deep Image Prior

Recently, deep learning-based methods are proposed for hyperspectral images (HSIs) denoising. Among them, unsupervised methods such as deep image prior (DIP)-based methods have received much attention because these methods do not require any training data. However, DIP-based methods suffer from the semi-convergence behavior, i.e., the iteration of DIP-based methods needs to terminate by referring to the ground-truth image at the optimal iteration point. In this paper, we propose the spatial-spectral constrained deep image prior (S2DIP) for the HSI mixed noise removal. Specifically, we integrate the DIP, the spatial-spectral total variation regularization term, and the l1-norm sparse term to respectively capture the deep prior of the clean HSI, the spatial-spectral local smooth prior of the clean HSI, and the sparse prior of noise. The proposed S2DIP jointly leverages the expressive power brought from the deep convolutional neural network without any training data and exploits the HSI and noise structures via hand-crafted priors. Thus, our method avoids the semi-convergence behavior of DIP-based methods. Meanwhile, our method largely enhances the HSI denoising ability of DIP-based methods. To tackle the corresponding model, we utilize the alternating direction multiplier method algorithm. Extensive experiments demonstrate that our method outperforms model-based and deep learning-based state-of-the-art HSI denoising methods.

Hyperspectral Image Superresolution Using Global Gradient Sparse and Nonlocal Low-Rank Tensor Decomposition With Hyper-Laplacian Prior

This article presents a novel global gradient sparse and nonlocal low-rank tensor decomposition model with a hyper-Laplacian prior for hyperspectral image (HSI) superresolution to produce a high-resolution HSI (HR-HSI) by fusing a low-resolution HSI (LR-HSI) with an HR multispectral image (HR-MSI). Inspired by the investigated hyper-Laplacian distribution of the gradients of the difference images between the upsampled LR-HSI and latent HR-HSI, we formulate the relationship between these two datasets as a $\ell _{p}$ $(0 -norm term to enforce spectral preservation. Then, the relationship between the HR-MSI and latent HR-HSI is built using a tensor-based fidelity term to recover the spatial details. To effectively capture the high spatio-spectral-nonlocal similarities of the latent HR-HSI, we design a novel nonlocal low-rank Tucker decomposition to model the 3-D regular tensors constructed from the grouped nonlocal similar HR-HSI cubes. The global spatial-spectral total variation regularization is then adopted to ensure the global spatial piecewise smoothness and spectral consistency of the reconstructed HR-HSI from nonlocal low-rank cubes. Finally, an alternating direction method of multipliers-based algorithm is designed to efficiently solve the optimization problem. Experiments on both the synthetic and real datasets collected by different sensors show the effectiveness of the proposed method, from visual and quantitative assessments.

Hyperspectral image denoising via global spatial-spectral total variation regularized nonconvex local low-rank tensor approximation

Hyperspectral image (HSI) denoising aims to restore clean HSI from the noise-contaminated one which is usually caused during data acquisition and conversion. In this paper, we propose a novel spatial-spectral total variation (SSTV) regularized nonconvex local low-rank (LR) tensor approximation method to remove mixed noise in HSIs. From one aspect, the clean HSI data have its underlying local LR tensor property, even though the real HSI data is not globally low-rank due to the non-independent and non-identically distributed noise and out-liers. According to this fact, we propose a novel tensor Lγ-norm to formulate the local LR prior. From another aspect, HSIs are assumed to be piecewisely smooth in the global spatial and spectral domains. Instead of traditional bandwise total variation, we use the SSTV regularization to simultaneously consider global spatial and spectral smoothness. Results on simulated and real HSI datasets indicate that the use of local LR tensor penalty and global SSTV can boost the preserving of local details and overall structural information in HSIs.

Hyperspectral Image Restoration via Global L1-2 Spatial–Spectral Total Variation Regularized Local Low-Rank Tensor Recovery

Hyperspectral images (HSIs) are usually corrupted by various noises, e.g., Gaussian noise, impulse noise, stripes, dead lines, and many others. In this article, motivated by the good performance of the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1-2</sub> nonconvex metric in image sparse structure exploitation, we first develop a 3-D L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1-2</sub> spatial-spectral total variation ( L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1-2</sub> SSTV) regularization to globally represent the sparse prior in the gradient domain of HSIs. Then, we divide HSIs into local overlapping 3-D patches, and low-rank tensor recovery (LTR) is locally used to effectively separate the low-rank clean HSI patches from complex noise. The patchwise LTR can not only adapt to the local low-rank property of HSIs well but also significantly reduce the information loss caused by the global LTR. Finally, integrating the advantages of both the global L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1-2</sub> SSTV regularization and local LTR model, we propose a L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1-2</sub> SSTV regularized local LTR model for hyperspectral restoration. In the framework of the alternating direction method of multipliers, the difference of convex algorithm, the split Bregman iteration method, and tensor singular value decomposition method are adopted to solve the proposed model efficiently. Simulated and real HSI experiments show that the proposed model can reduce the dependence on noise independent and identical distribution hypotheses, and simultaneously remove various types of noise, even structure-related noise.

Hyperspectral Image Denoising Based on Nonlocal Low-Rank and TV Regularization

Hyperspectral image (HSI) acquisitions are degraded by various noises, among which additive Gaussian noise may be the worst-case, as suggested by information theory. In this paper, we present a novel tensor-based HSI denoising approach by fully identifying the intrinsic structures of the clean HSI and the noise. Specifically, the HSI is first divided into local overlapping full-band patches (FBPs), then the nonlocal similar patches in each group are unfolded and stacked into a new third order tensor. As this tensor shows a stronger low-rank property than the original degraded HSI, the tensor weighted nuclear norm minimization (TWNNM) on the constructed tensor can effectively separate the low-rank clean HSI patches. In addition, a regularization strategy with spatial–spectral total variation (SSTV) is utilized to ensure the global spatial–spectral smoothness in both spatial and spectral domains. Our method is designed to model the spatial–spectral non-local self-similarity and global spatial–spectral smoothness simultaneously. Experiments conducted on simulated and real datasets show the superiority of the proposed method.

Hyperspectral Image Mixed Noise Removal Based on Multidirectional Low-Rank Modeling and Spatial–Spectral Total Variation

Conventional low-rank (LR)-based hyperspectral image (HSI) denoising models generally convert high-dimensional data into 2-D matrices or just treat this type of data as 3-D tensors. However, these pure LR or tensor low-rank (TLR)-based methods lack flexibility for considering different correlation information from different HSI directions, which leads to the loss of comprehensive structure information and inherent spatial-spectral relationship. To overcome these shortcomings, we propose a novel multidirectional LR modeling and spatial-spectral total variation (MLR-SSTV) model for removing HSI mixed noise. By incorporating the weighted nuclear norm, we obtain the weighted sum of weighted nuclear norm minimization (WSWNNM) and the weighted sum of weighted tensor nuclear norm minimization (WSWTNNM) to estimate the more accurate LR tensor, especially, to remove the dead-line noise better. Gaussian noise is further denoised and the local spatial-spectral smoothness is preserved effectively by SSTV regularization. We develop an efficient algorithm for solving the derived optimization based on the alternating direction method of multipliers (ADMM). Extensive experiments on both synthetic data and real data demonstrate the superior performance of the proposed MLR-SSTV model for HSI mixed noise removal.

Hyperspectral image restoration by subspace representation with low‐rank constraint and spatial‐spectral total variation

Hyperspectral images (HSIs) restoration is an important preprocessing step. The spectral vectors in HSI can be separated into different classification based on the land-covers, which means the spectral space can be regarded as the union of several low-rank subspaces. Subspace low-rank representation (SLRR) is powerful in exploring the inner low-rank structure and has been applied for HSI restoration. However, the traditional SLRR only seek for the rank-minimum representation under a given dictionary, which may treat the structured sparse noise as inherent low-rank components. In addition, the SLRR framework cannot make full use of the spatial information. In this study, a framework named subspace representation with low-rank constraint and spatial-spectral total variation is proposed for HSI restoration. In which, an artificial rank constraint is introduced to control the rank of the representation result, which can improve the removal of the structured sparse noise and exploit the intrinsic structure of spectral space more effectively. Meanwhile, the spatial-spectral total variation regularisation is applied to enhance the spatial and spectral smoothness. Several experiments conducted in simulated and real HSI datasets demonstrate that the proposed method can achieve a state-of-the-art performance both in visual quality and quantitative assessments.

Hyperspectral Image Denoising With Total Variation Regularization and Nonlocal Low-Rank Tensor Decomposition

Hyperspectral images (HSIs) are normally corrupted by a mixture of various noise types, which degrades the quality of the acquired image and limits the subsequent application. In this article, we propose a novel denoising method for the HSI restoration task by combining nonlocal low-rank tensor decomposition and total variation regularization, which we refer to as TV-NLRTD. To simultaneously capture the nonlocal similarity and high spectral correlation, the HSI is first segmented into overlapping 3-D cubes that are grouped into several clusters by the $k$ -means++ algorithm and exploited by low-rank tensor approximation. Spatial–spectral total variation (SSTV) regularization is then investigated to restore the clean HSI from the denoised overlapping cubes. Meanwhile, the $\ell _{1} $ -norm facilitates the separation of the clean nonlocal low-rank tensor groups and the sparse noise. The proposed TV-NLRTD method is optimized by employing the efficient alternating direction method of multipliers (ADMM) algorithm. The experimental results obtained with both simulated and real hyperspectral data sets confirm the validity and superiority of the proposed method compared with the current state-of-the-art HSI denoising algorithms.

Blind cloud and cloud shadow removal of multitemporal images based on total variation regularized low-rank sparsity decomposition

Cloud and cloud shadow (cloud/shadow) removal from multitemporal satellite images is a challenging task and has elicited much attention for subsequent information extraction. Regarding cloud/shadow areas as missing information, low-rank matrix/tensor completion based methods are popular to recover information undergoing cloud/shadow degradation. However, existing methods required to determine the cloud/shadow locations in advance and failed to completely use the latent information in cloud/shadow areas. In this study, we propose a blind cloud/shadow removal method for time-series remote sensing images by unifying cloud/shadow detection and removal together. First, we decompose the degraded image into low-rank clean image (surface-reflected) component and sparse (cloud/shadow) component, which can simultaneously and completely use the underlying characteristics of these two components. Meanwhile, the spatial-spectral total variation regularization is introduced to promote the spatial-spectral continuity of the cloud/shadow component. Second, the cloud/shadow locations are detected from the sparse component using a threshold method. Finally, we adopt the cloud/shadow detection results to guide the information compensation from the original observed images to better preserve the information in cloud/shadow-free locations. The problem of the proposed model is efficiently addressed using the alternating direction method of multipliers. Both simulated and real datasets are performed to demonstrate the effectiveness of our method for cloud/shadow detection and removal when compared with other state-of-the-art methods.