Results 1 to 10 of about 1,179 (158)

Robust Hyperspectral Unmixing with Practical Learning-Based Hyperspectral Image Denoising

Remote Sensing, 2023
The noise corruption problem commonly exists in hyperspectral images (HSIs) and severely affects the accuracy of hyperspectral unmixing algorithms.
Risheng Huang, Xiaorun Li, Yiming Fang, Zeyu Cao, Chaoqun Xia   +4 more
doaj   +4 more sources

Hyperspectral Image Denoising via Adversarial Learning

Remote Sensing, 2022
Due to sensor instability and atmospheric interference, hyperspectral images (HSIs) often suffer from different kinds of noise which degrade the performance of downstream tasks.
Junjie Zhang, Zhouyin Cai, Fansheng Chen, Dan Zeng   +3 more
doaj   +4 more sources

Simultaneous Nonconvex Denoising and Unmixing for Hyperspectral Imaging [PDF]

IEEE Access, 2019
Sparse hyperspectral unmixing aims at finding the sparse fractional abundance vector of a spectral signature present in a mixed pixel. However, there are several types of noise present in the hyperspectral images.
Taner Ince, Tugcan Dundar
doaj   +2 more sources

How Hyperspectral Image Unmixing and Denoising Can Boost Each Other

Remote Sensing, 2020
Hyperspectral linear unmixing and denoising are highly related hyperspectral image (HSI) analysis tasks. In particular, with the assumption of Gaussian noise, the linear model assumed for the HSI in the case of low-rank denoising is often the same as the
Behnood Rasti, Bikram Koirala, Paul Scheunders, Pedram Ghamisi   +3 more
doaj   +3 more sources

Hyperspectral Image Denoising With Dual Deep CNN [PDF]

IEEE Access, 2019
A new hyperspectral image denoising algorithm, called the dual deep convolutional neural network (DD-CNN), is proposed in this paper. In contrast to internal denoising methods that utilize only the features from the target noisy image, the DD-CNN ...
Wei Shan, Peng Liu, Lin Mu, Caihong Cao, Guojin He   +4 more
doaj   +2 more sources

Revolutionizing hyper spectral image denoising: a squeezenet paradigm [PDF]

Scientific Reports
Hyperspectral images (HSIs) frequently experience various types of noise due to atmospheric interference and sensor instability, which impairs the efficiency of subsequent operations.
Nandhagopal Nachimuthu, Ramya Murugesan, M. Dharmalingam, G. Prakash   +3 more
doaj   +2 more sources

A Single Model CNN for Hyperspectral Image Denoising

IEEE Transactions on Geoscience and Remote Sensing, 2020
Denoising is a common preprocessing step prior to the analysis and interpretation of hyperspectral images (HSIs). However, the vast majority of methods typically adopted for HSI denoising exploit architectures originally developed for grayscale or RGB images, exhibiting limitations when processing high-dimensional HSI data cubes.
Juan M Haut, Mercedes E Paoletti, Javier Plaza   +2 more
exaly   +4 more sources

Attention-Based Octave Network for Hyperspectral Image Denoising [PDF]

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022
Inevitable corruption and degeneration make the performance of subsequent high-level semantic tasks in hyperspectral images (HSIs) unsatisfactory. Despite that many denoising methods have been proposed, significant room for improvement still remains.
Ziwen Kan, Suhang Li, Mingzheng Hou, Leyuan Fang, Yi Zhang   +4 more
doaj   +2 more sources

Hybrid Convolutional and Attention Network for Hyperspectral Image Denoising

IEEE Geoscience and Remote Sensing Letters
IEEE GRSL ...
Feng Gao, Xiaowei Zhou, Junyu Dong
exaly   +3 more sources

Hyperspectral Image Denoising Based on Multi-Stream Denoising Network [PDF]

2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021
Hyperspectral images (HSIs) have been widely applied in many fields, such as military, agriculture, and environment monitoring. Nevertheless, HSIs commonly suffer from various types of noise during acquisition. Therefore, denoising is critical for HSI analysis and applications.
Yan Gao, Feng Gao 0005, Junyu Dong
openaire   +2 more sources