Results 71 to 80 of about 539 (168)

Lightweight Accelerated Unfolding Network With Collaborative Attention for Snapshot Spectral Compressive Imaging

IET Image Processing, Volume 19, Issue 1, January/December 2025.
This study presents a novel approach called the collaborative attention accelerated unfolding network (CA2UN), designed to enhance the recovery of 3D hyperspectral images from single 2D measurements by improving feature extraction in deep unfolding networks (DUNs).
Mengjie Qin, Yuchao Feng
wiley   +1 more source

Iterative Low-rank Network for Hyperspectral Image Denoising

Hyperspectral image (HSI) denoising is a crucial preprocessing step for subsequent tasks. The clean HSI usually reside in a low-dimensional subspace, which can be captured by low-rank and sparse representation, known as the physical prior of HSI.
Xiong, F, Zhou, J, Ye, J, Qian, Y
core   +1 more source

Hyperspectral Image Denoising and Anomaly Detection Based on Low-Rank and Sparse Representations

, 2020
Hyperspectral imaging measures the amount of electromagnetic energy across the instantaneous field of view at a very high resolution in hundreds or thousands of spectral channels.
Zhang, Bing, Gao, Lianru, Bioucas-Dias, Jose M., Zhuang, Lina, Fu, Xiyou   +4 more
core   +1 more source

Variational Weighted ℓp−ℓq$\ell _p-\ell _q$ Regularization for Hyperspectral Image Restoration Under Mixed Noise

IET Image Processing, Volume 19, Issue 1, January/December 2025.
In this paper, we propose to use weighted l2‐norm for approximating the solution of general ℓp−ℓq$\ell _p-\ell _q$‐norm regularization problem for recovering hyperspectral images (HSI) corrupted by a mixture of Gaussian‐impulse noise. We design an optimization framework to accommodate the combined effect of different noise sources.
Hazique Aetesam, V. B. Surya Prasath
wiley   +1 more source

Hyperspectral Image Denoising Based on Principal-Third-Order-Moment Analysis

Remote Sensing
Denoising serves as a critical preprocessing step for the subsequent analysis of the hyperspectral image (HSI). Due to their high computational efficiency, low-rank-based denoising methods that project the noisy HSI into a low-dimensional subspace ...
Shouzhi Li, Xiurui Geng, Liangliang Zhu, Luyan Ji, Yongchao Zhao   +4 more
doaj   +1 more source

3D‐TabNetHS: A hyperspectral image classification method based on improved interpretable 3D attentive TabNet

IET Radar, Sonar &Navigation, Volume 18, Issue 12, Page 2749-2767, December 2024.
The paper proposes a 3D‐TabNet HSI classification method (3D‐TabNetHS) based on an improved attentive interpretable tabular learning (TabNet) as well as an unsupervised U3D‐TabNetHS classifier. On three typical HSI datasets, the accuracy metric OA (Overall Accuracy) of 3D‐TabNetHS reached as high as 98.71%, 94.73%, and 97.23% respectively ...
Ning Li, Daozhi Wei, Shucai Huang, Yong Zhang   +3 more
wiley   +1 more source

Depth Mapping in Turbid and Deep Waters Using AVIRIS‐NG Imagery: A Study in Wax Lake Delta, Louisiana, USA

Water Resources Research, Volume 60, Issue 11, November 2024.
Abstract Remote sensing has been widely applied to investigate fluvial processes, but depth retrievals face significant constraints in deep and turbid conditions. This study evaluates the potential for depth retrievals under such challenging conditions using NASA's Airborne Visible/Infrared Imaging Spectrometer‐Next Generation (AVIRIS‐NG) imagery.
Siyoon Kwon, Paola Passalacqua, Antoine Soloy, Daniel Jensen, Marc Simard   +4 more
wiley   +1 more source

A robust low‐rank tensor completion model with sparse noise for higher‐order data recovery

IET Image Processing, Volume 18, Issue 12, Page 3430-3446, 16 October 2024.
A robust low‐rank tensor completion model for repairing damaged higher‐order tensor data corrupted by noise and missing values is proposed. Abstract The tensor singular value decomposition‐based model has garnered increasing attention in addressing tensor recovery challenges.
Min Wang, Zhuying Chen, Shuyi Zhang
wiley   +1 more source

A Joint Architecture of Mixed-Attention Transformer and Octave Module for Hyperspectral Image Denoising

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Convolutional neural networks (CNNs) recently have achieved impressive performance for hyperspectral image denoising. However, current CNNs have limitations in exploring spectral correlations across various bands and the interactions among features ...
Mahmood Ashraf, Lihui Chen, Xichuan Zhou, Muhammad Allah Rakha   +3 more
doaj   +1 more source

Deep Content-Dependent 3-D Convolutional Sparse Coding for Hyperspectral Image Denoising

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Despite the significant successes in hyperspectral image (HSI) denoising, pure data-driven HSI denoising networks still suffer from limited understanding of inference. Deep unfolding (DU) is a feasible way to improve the interpretability of deep network.
Haitao Yin, Hao Chen
doaj   +1 more source