Recent Developments in Parallel and Distributed Computing for Remotely Sensed Big Data Processing

Wu, Zebin; Sun, Jin; Zhang, Yi; Wei, Zhihui; Chanussot, Jocelyn

doi:10.1109/jproc.2021.3087029

Cited by 58 publications

(13 citation statements)

References 152 publications

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“…Meanwhile, in 20 continuous bands, 10% of pixels are contaminated by salt and pepper noise. • case 2: zero-mean Gaussian noise is added as the same condition in case 1, and deadlines with the randomly selected number in the range [3,10] and widths in [1,3] are added to the continuous 20 bands.…”

Section: Simulation Configurationsmentioning

confidence: 99%

“…Hyperspectral image (HSI) has played an important role in many modern scenarios like urban planning, agricultural exploration, criminal investigation, military surveillance, etc. [1]. However, the existing mixed noise generated during the process of digital imaging, e.g., Gaussian noise, salt and pepper, stripes and deadlines, seriously diminishes the accuracy of the above applications [2].…”

Section: Introductionmentioning

confidence: 99%

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Spectral-Smoothness and Non-Local Self-Similarity Regularized Subspace Low-Rank Learning Method for Hyperspectral Mixed Denoising

Liu

Sun

2021

Remote Sensing

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During the imaging process, hyperspectral image (HSI) is inevitably affected by various noises, such as Gaussian noise, impulse noise, stripes or deadlines. As one of the pre-processing steps, the removal of mixed noise for HSI has a vital impact on subsequent applications, and it is also one of the most challenging tasks. In this paper, a novel spectral-smoothness and non-local self-similarity regularized subspace low-rank learning (termed SNSSLrL) method was proposed for the mixed noise removal of HSI. First, under the subspace decomposition framework, the original HSI is decomposed into the linear representation of two low-dimensional matrices, namely the subspace basis matrix and the coefficient matrix. To further exploit the essential characteristics of HSI, on the one hand, the basis matrix is modeled as spectral smoothing, which constrains each column vector of the basis matrix to be a locally continuous spectrum, so that the subspace formed by its column vectors has continuous properties. On the other hand, the coefficient matrix is divided into several non-local block matrices according to the pixel coordinates of the original HSI data, and block-matching and 4D filtering (BM4D) is employed to reconstruct these self-similar non-local block matrices. Finally, the formulated model with all convex items is solved efficiently by the alternating direction method of multipliers (ADMM). Extensive experiments on two simulated datasets and one real dataset verify that the proposed SNSSLrL method has greater advantages than the latest state-of-the-art methods.

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Section: Simulation Configurationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spectral-Smoothness and Non-Local Self-Similarity Regularized Subspace Low-Rank Learning Method for Hyperspectral Mixed Denoising

Liu

Sun

2021

Remote Sensing

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“…In recent years, deep learning techniques [37,38] have been widely used, including hyperspectral image classification, due to their strong ability to mine high-level spatially invariant and discriminant features [39,40]. In some typical models [41][42][43], feature transformation or feature selection for hyperspectral pre-processing is used, followed by a deep network, e.g.…”

Section: Introductionmentioning

confidence: 99%

A Novel Band Selection and Spatial Noise Reduction Method for Hyperspectral Image Classification

Zhang

Sun

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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“…Classification accuracy can be improved by using more advanced classifiers [10] - [14]. For example, random forests [15], multi-layer perceptron [16], collaborative representation classifier [17] and the more popular deep learning models [18], [19] in recent years have more applications in the fields of hyperspectral remote sensing, energy and natural disasters. However, because a single classifier often cannot obtain an optimal classification result, some researchers have proposed to improve the classification accuracy of hyperspectral images based on ensemble learning methods [20] - [22].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Ensemble Learning With Multi-View Kernel Collaborative Subspace Clustering for Hyperspectral Image Classification

Lü

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Recently, a series of collaborative representation (CR) methods have attracted much attention for hyperspectral images classification. In this paper, two CR-based dynamic ensemble selection (DES) methods using multi-view kernel collaborative subspace clustering (MVKCSC) and random subspace multi-view kernel collaborative subspace clustering (RSMVKCSC) are proposed. In order to combine spectral and spatial information to construct a region of competence (RoC), the multi-view learning strategy is used in the general DES method. Compared with traditional clustering methods, the MVC can more effectively utilize multi-feature information. Moreover, a new method of constructing the Laplacian matrix using kernel collaborative representation coefficients is proposed for clustering based on subspace clustering and CR theory. This method is called MVKCSC, which can obtain the clustering results by using kernel CR self-representation coefficients. In addition, to increase diversity of samples, the random subspace method (RSM) and MVKCSC are combined for RMVKCSC. Moreover, the algorithm can obtain better clustering results by constraining samples and features simultaneously. The effectiveness of the proposed methods is validated using three hyperspectral data sets with few samples. The experimental results show that both DES-MVKCSC and DES-RSMVKCSC outperform their single classifier counterparts. In particular, the proposed methods provide superior performance compared with the state-of-the-art DES methods.

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Recent Developments in Parallel and Distributed Computing for Remotely Sensed Big Data Processing

Cited by 58 publications

References 152 publications

Spectral-Smoothness and Non-Local Self-Similarity Regularized Subspace Low-Rank Learning Method for Hyperspectral Mixed Denoising

Spectral-Smoothness and Non-Local Self-Similarity Regularized Subspace Low-Rank Learning Method for Hyperspectral Mixed Denoising

A Novel Band Selection and Spatial Noise Reduction Method for Hyperspectral Image Classification

Dynamic Ensemble Learning With Multi-View Kernel Collaborative Subspace Clustering for Hyperspectral Image Classification

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