Advances in hyperspectral image and signal processing: A comprehensive overview of the state of the art
Recent advances in airborne and spaceborne hyperspectral imaging technology have
provided end users with rich spectral, spatial, and temporal information. They have made a …
provided end users with rich spectral, spatial, and temporal information. They have made a …
Noise reduction in hyperspectral imagery: Overview and application
Hyperspectral remote sensing is based on measuring the scattered and reflected
electromagnetic signals from the Earth's surface emitted by the Sun. The received radiance …
electromagnetic signals from the Earth's surface emitted by the Sun. The received radiance …
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 …
Image restoration for remote sensing: Overview and toolbox
Remote sensing provides valuable information about objects and areas from a distance in
either active (eg, radar and lidar) or passive (eg, multispectral and hyperspectral) modes …
either active (eg, radar and lidar) or passive (eg, multispectral and hyperspectral) modes …
Hyperspectral image denoising with total variation regularization and nonlocal low-rank tensor decomposition
Hyperspectral images (HSIs) are normally corrupted by a mixture of various noise types,
which degrades the quality of the acquired image and limits the subsequent application. In …
which degrades the quality of the acquired image and limits the subsequent application. In …
Hyperspectral image denoising via sparse representation and low-rank constraint
Hyperspectral image (HSI) denoising is an essential preprocess step to improve the
performance of subsequent applications. For HSI, there is much global and local …
performance of subsequent applications. For HSI, there is much global and local …
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 denoising using spectral-spatial transform-based sparse and low-rank representations
This article proposes a denoising method based on sparse spectral–spatial and low-rank
representations (SSSLRR) using the 3-D orthogonal transform (3-DOT). SSSLRR can be …
representations (SSSLRR) using the 3-D orthogonal transform (3-DOT). SSSLRR can be …
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 …
Coupled sparse denoising and unmixing with low-rank constraint for hyperspectral image
Hyperspectral image (HSI) denoising is significant for correct interpretation. In this paper, a
sparse representation framework that unifies denoising and spectral unmixing in a closed …
sparse representation framework that unifies denoising and spectral unmixing in a closed …