Hyperspectral image restoration using low-rank matrix recovery
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 …
acquisition process, which can include Gaussian noise, impulse noise, dead lines, stripes …
Hyperspectral image denoising employing a spectral–spatial adaptive total variation model
The amount of noise included in a hyperspectral image limits its application and has a
negative impact on hyperspectral image classification, unmixing, target detection, and so on …
negative impact on hyperspectral image classification, unmixing, target detection, and so on …
Denoising of hyperspectral imagery using principal component analysis and wavelet shrinkage
In this paper, a new denoising method is proposed for hyperspectral data cubes that already
have a reasonably good signal-to-noise ratio (SNR)(such as 600: 1). Given this level of the …
have a reasonably good signal-to-noise ratio (SNR)(such as 600: 1). Given this level of the …
Denoising hyperspectral image with non-iid noise structure
Hyperspectral image (HSI) denoising has been attracting much research attention in remote
sensing area due to its importance in improving the HSI qualities. The existing HSI …
sensing area due to its importance in improving the HSI qualities. The existing HSI …
Hyperspectral image restoration using low-rank tensor recovery
This paper studies the hyperspectral image (HSI) denoising problem under the assumption
that the signal is low in rank. In this paper, a mixture of Gaussian noise and sparse noise is …
that the signal is low in rank. In this paper, a mixture of Gaussian noise and sparse noise is …
Minimum noise fraction versus principal component analysis as a preprocessing step for hyperspectral imagery denoising
Minimum noise fraction (MNF) is a well-known technique for hyperspectral imagery
denoising. It transforms a noisy data cube into output channel images with steadily …
denoising. It transforms a noisy data cube into output channel images with steadily …
Unsupervised band selection for hyperspectral imagery classification without manual band removal
The rich information available in hyperspectral imagery has provided significant
opportunities for material classification and identification. Due to the problem of the “curse of …
opportunities for material classification and identification. Due to the problem of the “curse of …
Hyperspectral image noise reduction based on rank-1 tensor decomposition
In this study, a novel noise reduction algorithm for hyperspectral imagery (HSI) is proposed
based on high-order rank-1 tensor decomposition. The hyperspectral data cube is …
based on high-order rank-1 tensor decomposition. The hyperspectral data cube is …
Noise removal from hyperspectral image with joint spectral–spatial distributed sparse representation
Hyperspectral image (HSI) denoising is a crucial preprocessing task that is used to improve
the quality of images for object detection, classification, and other subsequent applications. It …
the quality of images for object detection, classification, and other subsequent applications. It …
Multiple-spectral-band CRFs for denoising junk bands of hyperspectral imagery
P Zhong, R Wang - IEEE Transactions on Geoscience and …, 2012 - ieeexplore.ieee.org
Denoising of hyperspectral imagery in the domain of imaging spectroscopy by conditional
random fields (CRFs) is addressed in this work. For denoising of hyperspectral imagery, the …
random fields (CRFs) is addressed in this work. For denoising of hyperspectral imagery, the …