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 …
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 …
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 …
H Zhang, L Liu, W He, L Zhang - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
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 …
YQ Zhao, J Yang - IEEE Transactions on Geoscience and …, 2014 - ieeexplore.ieee.org
Hyperspectral image (HSI) denoising is an essential preprocess step to improve the performance of subsequent applications. For HSI, there is much global and local …
Y Chen, X Cao, Q Zhao, D Meng… - IEEE transactions on …, 2017 - ieeexplore.ieee.org
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 …
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 …
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 …
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 …