Results 31 to 40 of about 151 (115)

Hyperspectral reflectance spectra of floating matters derived from Hyperspectral Imager for the Coastal Ocean (HICO) observations [PDF]

Earth System Science Data, 2022
Using data collected by the Hyperspectral Imager for the Coastal Ocean (HICO) on the International Space Station between 2010–2014, hyperspectral reflectance spectra of various floating matters in global oceans and lakes are derived for the spectral ...
C. Hu
doaj   +1 more source

Multiscale Convolutional Mask Network for Hyperspectral Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Deep learning has gained popularity in hyperspectral unmixing (HU) applications recently due to its powerful learning and data-fitting capabilities. As an unmixing baseline network, the autoencoder (AE) framework performs well in HU by automatically ...
Mingming Xu, Jin Xu, Shanwei Liu, Hui Sheng, Zhiru Yang   +4 more
doaj   +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

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

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

Endmember-Free Hyperspectral Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Unmixing networks for hyperspectral images (HSIs) often need to be redesigned for each sensor and initialized with endmember-estimation algorithms, which limits cross-scene generalization.
Baisen Liu, Longfei Hou, Weiming Zheng, Nazik Alturki, Jamel Baili, Weili Kong, Jiaming Pei   +6 more
doaj   +1 more source

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

Unmixing-Guided Convolutional Transformer for Spectral Reconstruction

Remote Sensing, 2023
Deep learning networks based on CNNs or transformers have made progress in spectral reconstruction (SR). However, many methods focus solely on feature extraction, overlooking the interpretability of network design.
Shiyao Duan, Jiaojiao Li, Rui Song, Yunsong Li, Qian Du   +4 more
doaj   +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