Results 51 to 60 of about 828,208 (170)

Model-inspired deep neural networks for hyperspectral unmixing

, 2022
Model-based and learning-based methods are two typical classes for hyperspectral unmixing. Model-based methods are interpretable but rely on the definition of a physical model and iterative optimization. Learning-based methods have high learning ability,
Xiong, F, Zhou, J, Ye, M, Qian, Y
core   +1 more source

Spectral-spatial adversarial network for nonlinear hyperspectral unmixing of imbalanced datasets

International Journal of Applied Earth Observations and Geoinformation, 2023
With its successful application in various fields, hyperspectral unmixing (HU) technology has received extensive attention in remote sensing processing. Recently, various autoencoders based on the linear mixing model (LMM) have been proposed to provide a
Xu Yang, Jianguo Chen, Zihao Chen
doaj   +1 more source

Hyperspectral unmixing using weighted sparse regression with total variation regularization

, 2022
Spectral unmixing aims at identifying the pure spectral signatures in hyperspectral images and simultaneously estimating their proportions in each pixel of the scene.
Ma, Zheng, Hu, Jianming, Ren, Longfei, Bruzzone, Lorenzo, Bovolo, Francesca   +4 more
core   +1 more source

Hyperspectral Image Resolution Enhancement Based on Spectral Unmixing and Information Fusion [PDF]

, 2011
Hyperspectral imaging sensors exibit high spectral resolution, but normally low spatial resolution. This leads to spectral signatures of pixels originating from different object types. Such pixels are called mixed pixels.
Avbelj, Janja, Reinartz, Peter, Müller, Rupert, Cerra, Daniele, Bieniarz, Jakub   +4 more
core  

An Outlier-Insensitive Unmixing Algorithm With Spatially Varying Hyperspectral Signatures

IEEE Access, 2019
Effective hyperspectral unmixing (HU) is essential to the estimation of the underlying materials' signatures (endmember signatures) and their spatial distributions (abundance maps) from a given image (data) of a hyperspectral scene.
Yao-Rong Syu, Chia-Hsiang Lin, Chong-Yung Chi   +2 more
doaj   +1 more source

Fast and Structured Block-Term Tensor Decomposition for Hyperspectral Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023
The block-term tensor decomposition model with multilinear rank-$(L_{r},L_{r},1)$ terms (or the “${\mathsf{LL1}}$ tensor decomposition” in short) offers a valuable alternative formulation for hyperspectral unmixing (HU), which ensures the ...
Meng Ding, Xiao Fu, Xi-Le Zhao
doaj   +1 more source

Hyperspectral Imaging: The Intelligent Eye to Uncover the Password of Plant Science

Modern Agriculture, Volume 3, Issue 2, December 2025.
Hyperspectral imaging (HSI) has emerged as a powerful non‐destructive technique for characterisation of the plant phenotype and physiological traits. The ongoing development of cost‐effective hardware, coupled with standardised acquisition protocols and open‐access spectral libraries, is accelerating its integration with multi‐omics approaches to ...
Jingyan Song, Haifeng Liang, Bingjie Lu, Jing Guo, Yuan Gao, Xiao Hu, Manlin Yang, Xiaofan Li, Zhenyu Wang, Yongqi Chen, Yinyin Zhang, Shen Su, Zhangyun Gao, Shijie Li, Ping Chen, Jing Wang, Wanneng Yang, Hui Feng   +17 more
wiley   +1 more source

SSF-Net: A Spatial–Spectral Features Integrated Autoencoder Network for Hyperspectral Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
In recent years, deep learning has received tremendous attention in the field of hyperspectral unmixing (HU) due to its powerful learning capabilities.
Bin Wang, Huizheng Yao, Dongmei Song, Jie Zhang, Han Gao   +4 more
doaj   +1 more source

Compact Spectral Imaging: A Review of Miniaturized and Integrated Systems

Laser &Photonics Reviews, Volume 19, Issue 21, November 6, 2025.
This review explores the rapid shift toward compact spectral imaging systems by examining four key design paradigms: Do‐It‐Yourself (DIY) platforms, freeform optics, filter‐on‐chip integration, and multifunctional metasurfaces. The discussion highlights critical applications in medicine, agriculture, and environmental monitoring, providing comparative ...
Sani Mukhtar, Amir Arbabi, Jaime Viegas
wiley   +1 more source

CCTV‐Hyperspectral Imaging for Suspended Sediment Transport (HISST): Proof‐of‐Concept for a Continuous Day‐and‐Night Monitoring Approach

Water Resources Research, Volume 61, Issue 10, October 2025.
Abstract Effective sediment monitoring is crucial for managing dynamic river environments where suspended sediment transport varies over time. However, manual sampling and turbidity sensor‐based methods provide limited spatial coverage and can be labor‐intensive.
Siyoon Kwon, Hyoseob Noh, Il Won Seo, Yun Ho Lee   +3 more
wiley   +1 more source