In this paper, we present a spatial spectral hyperspectral image (HSI) mixed-noise removal method named total variation (TV)-regularized low-rank matrix factorization (LRTV). In general, HSIs are not ...only assumed to lie in a low-rank subspace from the spectral perspective but also assumed to be piecewise smooth in the spatial dimension. The proposed method integrates the nuclear norm, TV regularization, and L 1 -norm together in a unified framework. The nuclear norm is used to exploit the spectral low-rank property, and the TV regularization is adopted to explore the spatial piecewise smooth structure of the HSI. At the same time, the sparse noise, which includes stripes, impulse noise, and dead pixels, is detected by the L 1 -norm regularization. To tradeoff the nuclear norm and TV regularization and to further remove the Gaussian noise of the HSI, we also restrict the rank of the clean image to be no larger than the number of endmembers. A number of experiments were conducted in both simulated and real data conditions to illustrate the performance of the proposed LRTV method for HSI restoration.
Blind hyperspectral unmixing (HU), which includes the estimation of endmembers and their corresponding fractional abundances, is an important task for hyperspectral analysis. Recently, nonnegative ...matrix factorization (NMF) and its extensions have been widely used in HU. Unfortunately, most of the NMF-based methods can easily lead to an unsuitable solution, due to the nonconvexity of the NMF model and the influence of noise. To overcome this limitation, we make the best use of the structure of the abundance maps, and propose a new blind HU method named total variation regularized reweighted sparse NMF (TV-RSNMF). First, the abundance matrix is assumed to be sparse, and a weighted sparse regularizer is incorporated into the NMF model. The weights of the weighted sparse regularizer are adaptively updated related to the abundance matrix. Second, the abundance map corresponding to a single fixed endmember should be piecewise smooth. Therefore, the TV regularizer is adopted to capture the piecewise smooth structure of each abundance map. In our multiplicative iterative solution to the proposed TV-RSNMF model, the TV regularizer can be regarded as an abundance map denoising procedure, which improves the robustness of TV-RSNMF to noise. A number of experiments were conducted in both simulated and real-data conditions to illustrate the advantage of the proposed TV-RSNMF method for blind HU.
Clustering for hyperspectral images (HSIs) is a very challenging task due to its inherent complexity. In this paper, we propose a novel spectral-spatial sparse subspace clustering S 4 C algorithm for ...hyperspectral remote sensing images. First, by treating each kind of land-cover class as a subspace, we introduce the sparse subspace clustering (SSC) algorithm to HSIs. Then, considering the spectral and spatial properties of HSIs, the high spectral correlation and rich spatial information of the HSIs are taken into consideration in the SSC model to obtain a more accurate coefficient matrix, which is used to build the adjacent matrix. Finally, spectral clustering is applied to the adjacent matrix to obtain the final clustering result. Several experiments were conducted to illustrate the performance of the proposed S 4 C algorithm.
Hyperspectral images (HSIs) are often degraded by a mixture of various kinds of noise in the acquisition process, which can include Gaussian noise, impulse noise, dead lines, stripes, and so on. This ...paper introduces a new HSI restoration method based on low-rank matrix recovery (LRMR), which can simultaneously remove the Gaussian noise, impulse noise, dead lines, and stripes. By lexicographically ordering a patch of the HSI into a 2-D matrix, the low-rank property of the hyperspectral imagery is explored, which suggests that a clean HSI patch can be regarded as a low-rank matrix. We then formulate the HSI restoration problem into an LRMR framework. To further remove the mixed noise, the "Go Decomposition" algorithm is applied to solve the LRMR problem. Several experiments were conducted in both simulated and real data conditions to verify the performance of the proposed LRMR-based HSI restoration method.
Hyperspectral images (HSIs) are usually contaminated by various kinds of noise, such as stripes, deadlines, impulse noise, Gaussian noise, and so on, which significantly limits their subsequent ...application. In this paper, we model the stripes, deadlines, and impulse noise as sparse noise, and propose a unified mixed Gaussian noise and sparse noise removal framework named spatial-spectral total variation regularized local low-rank matrix recovery (LLRSSTV). The HSI is first divided into local overlapping patches, and rank-constrained low-rank matrix recovery is adopted to effectively separate the low-rank clean HSI patches from the sparse noise. Differing from the previous low-rank-based HSI denoising approaches, which process all the patches individually, a global spatial-spectral total variation regularized image reconstruction strategy is utilized to ensure the global spatial-spectral smoothness of the reconstructed image from the low-rank patches. In return, the globally reconstructed HSI further promotes the separation of the local low-rank components from the sparse noise. An augmented Lagrange multiplier method is adopted to solve the proposed LLRSSTV model, which simultaneously explores both the local low-rank property and the global spatial-spectral smoothness of the HSI. Both simulated and real HSI experiments were conducted to illustrate the advantage of the proposed method in HSI denoising, from visual/quantitative evaluations and time cost.
Sparse representation has been widely used in image classification. Sparsity-based algorithms are, however, known to be time consuming. Meanwhile, recent work has shown that it is the collaborative ...representation (CR) rather than the sparsity constraint that determines the performance of the algorithm. We therefore propose a nonlocal joint CR classification method with a locally adaptive dictionary (NJCRC-LAD) for hyperspectral image (HSI) classification. This paper focuses on the working mechanism of CR and builds the joint collaboration model (JCM). The joint-signal matrix is constructed with the nonlocal pixels of the test pixel. A subdictionary is utilized, which is adaptive to the nonlocal signal matrix instead of the entire dictionary. The proposed NJCRC-LAD method is tested on three HSIs, and the experimental results suggest that the proposed algorithm outperforms the corresponding sparsity-based algorithms and the classical support vector machine hyperspectral classifier.
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•A high-performance g-C3N4/ZnO humidity sensor for respiratory monitoring is designed.•The oxygen vacancies and hydroxyl groups have stronger adsorption capacity for water ...molecules.•The g-C3N4/ZnO sensor has a high response ((1.05 ± 0.07)×104) in the 11 %–95 % RH range.•The humidity sensing mechanism is analyzed based on density functional theory.•The sensor shows strong application potential in the diagnosis of respiratory diseases.
In this work, a high-performance graphite carbon nitride/zinc oxide (g-C3N4/ZnO) humidity sensor for respiratory monitoring was designed for the first time. The humidity sensing mechanism for g-C3N4/ZnO was analysized by the first principles of density functional theory (DFT). It is found that compared to ZnO, oxygen vacancy defects and hydroxyl groups on g-C3N4/ZnO surface have higher adsorption energy for water molecules to make more water molecules to be adsorbed on surface of g-C3N4/ZnO. Experiments showed that the combination of g-C3N4 and ZnO increases the number of oxygen vacancy defects and hydroxyl groups on the surface of g-C3N4/ZnO, which makes a large number of water molecules adsorb on surface and accelerates the decomposition of water molecules into conductive ions to improve the performance of g-C3N4/ZnO humidity sensor. Such a g-C3N4/ZnO humidity sensor shows high response ((1.05±0.07)×104), small hysteresis (2.4%), good linearity, fast response/recovery speed (22/5 s) and higher stability in the range of 11 % RH to 95 % RH when the mass ratio of g-C3N4 and ZnO is 5%. In addition, the g-C3N4/ZnO sensor is effectively used to detect different respiratory states of the human, which shows strong application potential in the diagnosis of respiratory diseases.
Cloud and cloud shadow detection is a necessary preprocessing step for optical remote sensing applications because of the huge negative influence cloud and cloud shadow can have on data analysis. ...However, most of the existing cloud/shadow detection methods are designed based on specific band configurations of certain sensors, and their working mechanisms are relatively complex and computationally complicated, which limits their application. In view of this, in this paper, a unified cloud/shadow detection algorithm based on spectral indices (CSD-SI) is proposed for most of the widely used multi/hyperspectral optical remote sensing sensors with both visible and infrared spectral channels. On the one hand, the cloud index (CI) and cloud shadow index (CSI) are proposed to indicate the potential clouds and cloud shadows based on their physical reflective characteristics. In addition, considering the spatial coexistence of cloud and cloud shadow, a spatial matching strategy is utilized to remove the pseudo cloud shadows. The effectiveness of the proposed CSD-SI algorithm is demonstrated on eight different types of widely used multi/hyperspectral optical sensors, with different spectral and spatial resolution levels. Overall, in the experiments undertaken in this study, CSD-SI achieved a mean overall accuracy of 98.52% for cloud, with a mean producer’s accuracy of 93.13% and a mean user’s accuracy of 98.13%. For cloud shadow, CSD-SI achieved a means producer’s accuracy of 84.33% and a mean user’s accuracy of 89.12%. The experimental results show that the proposed CSD-SI method based on spectral indices can obtain a comparable cloud/shadow detection performance to that of the other state-of-the-art methods.
In this paper, we propose a superpixel-level sparse representation classification framework with multitask learning for hyperspectral imagery. The proposed algorithm exploits the class-level sparsity ...prior for multiple-feature fusion, and the correlation and distinctiveness of pixels in a spatial local region. Compared with some of the state-of-the-art hyperspectral classifiers, the superiority of the multiple-feature combination, the spatial prior utilization, and the computational complexity are maintained at the same time in the proposed method. The proposed classification algorithm was tested on three hyperspectral images. The experimental results suggest that the proposed algorithm performs better than the other sparse (collaborative) representation-based algorithms and some popular hyperspectral multiple-feature classifiers.
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.
