Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
Hyperspectral unmixing based on nonnegative matrix factorization: A comprehensive review
Hyperspectral unmixing has been an important technique that estimates a set of
endmembers and their corresponding abundances from a hyperspectral image (HSI) …
endmembers and their corresponding abundances from a hyperspectral image (HSI) …
Tensor decompositions for hyperspectral data processing in remote sensing: A comprehensive review
Owing to the rapid development of sensor technology, hyperspectral (HS) remote sensing
(RS) imaging has provided a significant amount of spatial and spectral information for the …
(RS) imaging has provided a significant amount of spatial and spectral information for the …
Hyperspectral image denoising using local low-rank matrix recovery and global spatial–spectral total variation
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 …
stripes, deadlines, impulse noise, Gaussian noise, and so on, which significantly limits their …
Total variation regularized reweighted sparse nonnegative matrix factorization for hyperspectral unmixing
Blind hyperspectral unmixing (HU), which includes the estimation of endmembers and their
corresponding fractional abundances, is an important task for hyperspectral analysis …
corresponding fractional abundances, is an important task for hyperspectral analysis …
Remote sensing image stripe noise removal: From image decomposition perspective
Stripe noise removal (destri**) is a fundamental problem in remote sensing image
processing that holds significant practical importance for subsequent applications. These …
processing that holds significant practical importance for subsequent applications. These …
Spectral–spatial sparse subspace clustering for hyperspectral remote sensing images
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 …
complexity. In this paper, we propose a novel spectral-spatial sparse subspace clustering S …
Computational intelligence in optical remote sensing image processing
With the ongoing development of Earth observation techniques, huge amounts of remote
sensing images with a high spectral-spatial-temporal resolution are now available, and have …
sensing images with a high spectral-spatial-temporal resolution are now available, and have …
Spatial–spectral total variation regularized low-rank tensor decomposition for hyperspectral image denoising
Several bandwise total variation (TV) regularized low-rank (LR)-based models have been
proposed to remove mixed noise in hyperspectral images (HSIs). These methods convert …
proposed to remove mixed noise in hyperspectral images (HSIs). These methods convert …
Nonconvex-sparsity and nonlocal-smoothness-based blind hyperspectral unmixing
Blind hyperspectral unmixing (HU), as a crucial technique for hyperspectral data
exploitation, aims to decompose mixed pixels into a collection of constituent materials …
exploitation, aims to decompose mixed pixels into a collection of constituent materials …
Orthogonal subspace unmixing to address spectral variability for hyperspectral image
Hyperspectral unmixing aims at estimating pure spectral signatures and their proportions in
each pixel. In practice, the atmospheric effects, intrinsic variation of the spectral signatures of …
each pixel. In practice, the atmospheric effects, intrinsic variation of the spectral signatures of …