Results 101 to 110 of about 6,533 (191)

Anomaly-Guided Double Autoencoders for Hyperspectral Unmixing

Remote Sensing
Deep learning has emerged as a prevalent approach for hyperspectral unmixing. However, most existing unmixing methods employ a single network, resulting in moderate estimation errors and less meaningful endmembers and abundances.
Hongyi Liu, Chenyang Zhang, Jianing Huang, Zhihui Wei   +3 more
doaj   +1 more source

Comparison of Imaging Models for Spectral Unmixing in Oil Painting. [PDF]

Sensors (Basel), 2021
Grillini F, Thomas JB, George S.
europepmc   +1 more source

Model-based analysis of the relationship between endmember variability and temporal unmixing performance

GIScience & Remote Sensing
Temporal unmixing, an extension of traditional spectral unmixing in a multi-temporal context, leverages endmembers defined by their temporal signatures to decompose mixed pixel responses into fractional cover.
Da Zhang, Chen Shi
doaj   +1 more source

Superpixel spectral unmixing framework for the volumetric assessment of tissue chromophores: A photoacoustic data-driven approach. [PDF]

Photoacoustics, 2022
Grasso V, Willumeit-Rӧmer R, Jose J.
europepmc   +1 more source

AD-HKFCM: A Robust Nonlinear Spectral Variability-Aware Unmixing via Intra/Inter-Class Affinity Cohesion

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Spectral variability and nonlinear mixing interactions critically degrade spectral unmixing accuracy, especially in heterogeneous environments. To address these challenges, this study proposes a robust nonlinear spectral variability-aware unmixing model,
Jie Yu, Xin Chen, Yi Lin, Yu Rong, Junbo Lv, Yuxuan Yang, Daiqi Zhong, Yiyuan Tian, Yi Jing, Xiaonan Yang   +9 more
doaj   +1 more source

Temporal and spectral unmixing of photoacoustic signals by deep learning. [PDF]

Opt Lett, 2021
Zhou Y, Zhong F, Hu S.
europepmc   +1 more source

SSST-GAN: A Sampling-Based Spatial-Spectral Transformer and Generative Adversarial Network for Hyperspectral Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Transformer-based architectures have shown strong potential in hyperspectral unmixing due to their powerful modeling capabilities. However, most existing transformer-based methods still struggle to effectively capture and fuse spatial–spectral ...
Yu Zhang, Jiageng Huang, Yefei Huang, Wei Gao, Jie Chen   +4 more
doaj   +1 more source

Hyperspectral imaging and spectral unmixing for improving whole-body fluorescence cryo-imaging. [PDF]

Biomed Opt Express, 2021
Wirth D, Byrd B, Meng B, Strawbridge RR, Samkoe KS, Davis SC.   +5 more
europepmc   +1 more source

Nonlinear Spectral Unmixing Using Bézier Surfaces

IEEE Transactions on Geoscience and Remote Sensing
Abstract: Accurate estimation of the fractional abundances of intimately mixed materials from spectral reflectances is generally hard due to a highly nonlinear relationship between the measured spectrum and the composition of the material. Changes in the acquisition and the illumination conditions cause variability in the spectral reflectance, further ...
Bikram Koirala, Behnood Rasti, Zakaria Bnoulkacem, Paul Scheunders   +3 more
openaire   +2 more sources

CResDAE: A Deep Autoencoder with Attention Mechanism for Hyperspectral Unmixing

Remote Sensing
Hyperspectral unmixing aims to extract pure spectral signatures (endmembers) and estimate their corresponding abundance fractions from mixed pixels, enabling quantitative analysis of surface material composition.
Chong Zhao, Jinlin Wang, Qingqing Qiao, Kefa Zhou, Jiantao Bi, Qing Zhang, Wei Wang, Dong Li, Tao Liao, Chao Li, Heshun Qiu, Guangjun Qu   +11 more
doaj   +1 more source