Results 61 to 70 of about 1,407 (163)

Blind Hyperspectral Unmixing Using Autoencoders

, 2023
Efni þessarar ritgerðar er aðgreining fjölrásamynda (e. blind hyperspectral unmixing) með sjálfkóðurum (e. autoencoders) byggðum á djúpum lærdómi (e. deep learning). Tvær aðferðir byggðar á sjálfkóðurum eru kynntar og rannsakaðar. Báðar aðferðirnar leitast við að nýta sér rúmfræðilega fylgni rófa í fjölrásamyndum til að bæta árangur aðgreiningar.
openaire   +2 more sources

Lidar-Driven Spatial Regularization for Hyperspectral Unmixing [PDF]

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018
Only a few research works consider LiDAR data while conducting hyperspectral image unmixing. However, the digital surface model derived from LiDAR can provide meaningful information, in particular when spatially regularizing the inverse problem underlain by spectral unmixing.
Uezato, Tatsumi, Fauvel, Mathieu, Dobigeon, Nicolas   +2 more
openaire   +3 more sources

Combinatorial SiO2‐Encapsulated Quantum Dot Nanoparticles and their Use in Spectral Unmixing Analysis

Analysis &Sensing, Volume 6, Issue 1, January 2026.
Combinatorial QD‐SiO2 nanoparticles combined with linear unmixing of the photoluminescence spectrum increase the multiplexity of assays. Linear unmixing spectral analysis is a technique where signals from tens of fluorophores can be deconvoluted to increase multiplexing by 4–5‐fold.
Yuwei Wang, Jennifer I. L. Chen
wiley   +1 more source

Spatial Structural Priors for Sparse Unmixing of Remotely Sensed Hyperspectral Images

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
As spectral libraries continue to expand, sparse unmixing has become essential for effectively interpreting mixed pixels in remotely sensed hyperspectral data.
Shaoquan Zhang, Jiajun Zheng, Yuyang Liu, Fan Li, Chengzhi Deng, Antonio Plaza, Lianhui Liang, Zhi He, Shengqian Wang   +8 more
doaj   +1 more source

Crossing scales and eras: Correlative multimodal microscopy heritage studies

Journal of Microscopy, Volume 301, Issue 1, Page 40-68, January 2026.
Abstract The comprehensive characterisation of complex, irreplaceable cultural heritage artefacts presents significant challenges for traditional analytical methods, which can fall short in providing multi‐scale, non‐invasive analysis. Correlative Multimodal Microscopy (CoMic), an approach that integrates data from multiple techniques, offers a ...
Charles Wood, George Deakin, Atousa Moayedi, Jovana Radulovic   +3 more
wiley   +1 more source

Deep multimodal unmixing of hyperspectral images using Convolutional Block Attention Module (CBAM) and LiDAR features

Egyptian Journal of Remote Sensing and Space Sciences
Hyperspectral image unmixing has garnered considerable attention across various application domains, particularly remote sensing applications. However, relying solely on one modality to distinguish objects with similar spectral information presents ...
M Sreejam, L Agilandeeswari
doaj   +1 more source

Hyperspectral EELS image unmixing

, 2016
Electron Energy Loss Spectroscopy (EELS) performed in a Scanning Transmission Electron Microscope (STEM) provides hyperspectral images characterized by a large number of pixels and energy channels (typically 100 x 100 x 1000) [1].
Altmann, Yoann, Brun, Nathalie, Dobigeon, Nicolas, March, Katia, Moussaoui, Saïd, Schneegans, Olivier   +5 more
openaire   +2 more sources

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

TCCU-Net: Transformer and CNN Collaborative Unmixing Network for Hyperspectral Image

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
In recent years, deep-learning-based hyperspectral unmixing techniques have garnered increasing attention and made significant advancements. However, relying solely on the use of convolutional neural network (CNN) or transformer approaches is ...
Jianfeng Chen, Chen Yang, Lan Zhang, Linzi Yang, Lifeng Bian, Zijiang Luo, Jihong Wang   +6 more
doaj   +1 more source

A Spectral Unmixing Method by Maximum Margin Criterion and Derivative Weights to Address Spectral Variability in Hyperspectral Imagery

Remote Sensing, 2019
Limited to the low spatial resolution of the hyperspectral imaging sensor, mixed pixels are inevitable in hyperspectral images. Therefore, to obtain the endmembers and corresponding fractions in mixed pixels, hyperspectral unmixing becomes a hot spot in ...
Yang Shao, Jinhui Lan
doaj   +1 more source