The application of low-rank and sparse decomposition method in the field of climatology
Sign Up to like & getrecommendations! Published in 2017 at "Theoretical and Applied Climatology"
DOI: 10.1007/s00704-017-2074-0
Abstract: The present study reports a low-rank and sparse decomposition method that separates the mean and the variability of a climate data field. Until now, the application of this technique was limited only in areas such… read more here.
Keywords: field; rank sparse; method; climatology ... See more keywords
Background subtraction with multi-scale structured low-rank and sparse factorization
Sign Up to like & getrecommendations! Published in 2019 at "Neurocomputing"
DOI: 10.1016/j.neucom.2018.02.101
Abstract: Abstract Low-rank and sparse factorization, which models the background as a low-rank matrix and the foreground as the contiguously corrupted outliers, exhibits excellent performance in background subtraction, in which the structured constraints of the foreground… read more here.
Keywords: low rank; sparse factorization; rank sparse; rank ... See more keywords
Tensor low-rank sparse representation for tensor subspace learning
Sign Up to like & getrecommendations! Published in 2021 at "Neurocomputing"
DOI: 10.1016/j.neucom.2021.02.002
Abstract: Abstract Many subspace learning methods are implemented on a matrix of sample data. For multi-dimensional data, these methods have to convert data samples into vectors in advance, which often destroys the inherent spatial structure of… read more here.
Keywords: rank sparse; tensor; subspace learning; low rank ... See more keywords
Ground penetrating radar clutter removal via randomized low rank and sparse decomposition for missing data case
Sign Up to like & getrecommendations! Published in 2020 at "International Journal of Remote Sensing"
DOI: 10.1080/01431161.2020.1763508
Abstract: ABSTRACT Dealing with ground penetrating radar (GPR) data with missing entries can affect the performance of clutter removal methods heavily, making target imaging/detection via GPR practically impossible. This paper proposes a two-step approach based on… read more here.
Keywords: missing data; rank sparse; decomposition; low rank ... See more keywords
Nonlocal low-rank and sparse matrix decomposition for spectral CT reconstruction.
Sign Up to like & getrecommendations! Published in 2018 at "Inverse problems"
DOI: 10.1088/1361-6420/aa942c
Abstract: Spectral computed tomography (CT) has been a promising technique in research and clinic because of its ability to produce improved energy resolution images with narrow energy bins. However, the narrow energy bin image is often… read more here.
Keywords: rank sparse; low rank; nonlocal low; energy ... See more keywords
Local Low-Rank and Sparse Representation for Hyperspectral Image Denoising
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2019.2923255
Abstract: Hyperspectral image (HSI) denoising is a fundamental task in a plethora of HSI applications. Global low-rank property is widely adopted to exploit the spectral-spatial information of HSIs, providing satisfactory denoising results. In this paper, instead… read more here.
Keywords: hyperspectral image; low rank; local low; rank ... See more keywords
Self-Adaptive Low-Rank and Sparse Decomposition for Hyperspectral Anomaly Detection
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"
DOI: 10.1109/jstars.2022.3172120
Abstract: Hyperspectral anomaly detection is a widely used technique for exploring target of interest in hyperspectral images (HSIs). In recent years, the low-rank and sparse-decomposition-based anomaly detection model has attracted extensive attention. However, these models suffer… read more here.
Keywords: hyperspectral anomaly; rank sparse; low rank; detection ... See more keywords
Robust Low Rank and Sparse Representation for Multiple Kernel Dimensionality Reduction
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Circuits and Systems for Video Technology"
DOI: 10.1109/tcsvt.2021.3087643
Abstract: In the fields of pattern recognition and data mining, two problems need to be addressed. First, the curse of dimensionality degrades the performance of many practical data processing techniques. Second, due to the existence of… read more here.
Keywords: rank sparse; kernel; low rank; dimensionality ... See more keywords
Low-Rank and Sparse Decomposition With Mixture of Gaussian for Hyperspectral Anomaly Detection
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Cybernetics"
DOI: 10.1109/tcyb.2020.2968750
Abstract: Recently, the low-rank and sparse decomposition model (LSDM) has been used for anomaly detection in hyperspectral imagery. The traditional LSDM assumes that the sparse component where anomalies and noise reside can be modeled by a… read more here.
Keywords: detection; anomaly detection; rank sparse; low rank ... See more keywords
Haze Removal for a Single Remote Sensing Image Using Low-Rank and Sparse Prior
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2021.3135975
Abstract: Due to the influence of atmospheric scattering, the quality of remote sensing images is degraded, which severely limits the utility of remote sensing images. In this article, a novel dehazing algorithm for a single remote… read more here.
Keywords: rank sparse; low rank; remote sensing; image ... See more keywords
Bi-endmember Semi-NMF Based on Low-Rank and Sparse Matrix Decomposition
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2022.3159998
Abstract: This paper presents a bi-endmember semi-nonnegative matrix factorization (Semi-NMF) algorithm based on low-rank and sparse matrix decomposition (LRSMD), referred to as BLSNMF, to resolve the issues of endmember variability and nonlinear mixing. Given the fact… read more here.
Keywords: rank sparse; low rank; semi nmf; decomposition ... See more keywords