Spectral–Spatial Generative Adversarial Network for Super-Resolution Land Cover Mapping With Multispectral Remotely Sensed Imagery

Super-resolution mapping (SRM) can effectively predict the spatial distribution of land cover classes within mixed pixels at a higher spatial resolution than the original remotely sensed imagery. The uncertainty of land cover fraction errors within mixed pixels is one of the most important factors a...

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Detalhes bibliográficos

Principais autores:	Cheng Shang, Shan Jiang, Feng Ling, Xiaodong Li, Yadong Zhou, Yun Du
Formato:	Artigo
Idioma:	Inglês
Publicado em:	IEEE 2023-01-01
coleção:	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Assuntos:	Deep learning (DL) generative adversarial network (GAN) land cover fractions spectral–spatial errors super-resolution mapping (SRM)
Acesso em linha:	https://ieeexplore.ieee.org/document/9982425/
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