Results 121 to 130 of about 930,547 (198)

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

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

Opt Lett, 2021
Zhou Y, Zhong F, Hu 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

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

Double reweighted sparse regression for hyperspectral unmixing

, 2016
Spectral unmixing is an important technology in hyperspectral image applications. Recently, sparse regression is widely used in hyperspectral unmixing. This paper proposes a double reweighted sparse regression method for hyperspectral unmixing.
Heng-Chao Li, Liao, Wenzhi, Aleksandra Pizurica, Wenzhi Liao, Pizurica, Aleksandra, Li, Hengchao, Wang, Rui, Rui Wang   +7 more
core   +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

Bilinear models for nonlinear unmixing of hyperspectral images

, 2011
This paper compares several nonlinear models recently introduced for hyperspectral image unmixing. All these models consist of bilinear models that have shown interesting properties for hyperspectral images subjected to multipath effects.
Altmann, Yoann, Jean-Yves Tourneret, Altmann, Yoann; id_orcid, Yoann Altmann, Nicolas Dobigeon, Dobigeon, Nicolas, Tourneret, Jean-Yves   +6 more
core   +1 more source

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