Zobrazeno 1 - 10
of 56 140
pro vyhledávání: '"Unmixing"'
Autor:
Zhang, Mingle1,2 (AUTHOR) zhangmingle21@mails.ucas.ac.cn, Yang, Mingyu1 (AUTHOR) xiehongyu21@mails.ucas.ac.cn, Xie, Hongyu1,2 (AUTHOR) yuepinliang22@mails.ucas.ac.cn, Yue, Pinliang1,2 (AUTHOR) zhangwei20f@mails.ucas.ac.cn, Zhang, Wei1,2 (AUTHOR) jiaoqingbin@ciomp.ac.cn, Jiao, Qingbin1 (AUTHOR) xuliang@ciomp.ac.cn, Xu, Liang1 (AUTHOR) tanxin@ciomp.ac.cn, Tan, Xin1 (AUTHOR)
Publikováno v:
Remote Sensing. Sep2024, Vol. 16 Issue 17, p3149. 26p.
Autor:
Andrés-Anaya, Paula1 (AUTHOR) gustavo1976@usal.es, Hernández-Herráez, Gustavo1 (AUTHOR), Del Pozo, Susana1 (AUTHOR), Lagüela, Susana1 (AUTHOR)
Publikováno v:
Remote Sensing. Aug2024, Vol. 16 Issue 16, p3067. 15p.
Deep learning based unmixing methods have received great attention in recent years and achieve remarkable performance. These methods employ a data-driven approach to extract structure features from hyperspectral image, however, they tend to be less p
Externí odkaz:
http://arxiv.org/abs/2409.04719
Autor:
Preston, Jade, Basener, William
Hyperspectral unmixing is the process of determining the presence of individual materials and their respective abundances from an observed pixel spectrum. Unmixing is a fundamental process in hyperspectral image analysis, and is growing in importance
Externí odkaz:
http://arxiv.org/abs/2408.07580
Autor:
Kouakou, Hugues, Goulart, José Henrique de Morais, Vitale, Raffaele, Oberlin, Thomas, Rousseau, David, Ruckebusch, Cyril, Dobigeon, Nicolas
This work introduces an on-the-fly (i.e., online) linear unmixing method which is able to sequentially analyze spectral data acquired on a spectrum-by-spectrum basis. After deriving a sequential counterpart of the conventional linear mixing model, th
Externí odkaz:
http://arxiv.org/abs/2407.15636
Multitemporal hyperspectral image unmixing (MTHU) holds significant importance in monitoring and analyzing the dynamic changes of surface. However, compared to single-temporal unmixing, the multitemporal approach demands comprehensive consideration o
Externí odkaz:
http://arxiv.org/abs/2407.10427
Autor:
Yu, Yang
Deep learning-based (DL-based) hyperspectral image (HIS) super-resolution (SR) methods have achieved remarkable performance and attracted attention in industry and academia. Nonetheless, most current methods explored and learned the mapping relations
Externí odkaz:
http://arxiv.org/abs/2407.06525
Autor:
Lin, Chia-Hsiang, Lin, Jhao-Ting
Multispectral unmixing (MU) is critical due to the inevitable mixed pixel phenomenon caused by the limited spatial resolution of typical multispectral images in remote sensing. However, MU mathematically corresponds to the underdetermined blind sourc
Externí odkaz:
http://arxiv.org/abs/2407.15358
This work proposes a variational inference (VI) framework for hyperspectral unmixing in the presence of endmember variability (HU-EV). An EV-accounted noisy linear mixture model (LMM) is considered, and the presence of outliers is also incorporated i
Externí odkaz:
http://arxiv.org/abs/2407.14899