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论文类型 ： 期刊论文

通讯作者 ： Xiaofeng Wang

全部作者 ： Ruyi Feng, Zhongyu Guo and Xiaofeng Wang*

收录刊物 ： EI  

所属单位 ： 计算机学院

刊物所在地 ： USA

学科门类 ： 工学

一级学科 ： 计算机

文献类型 ： J

关键字 ： Feedback embedding, hyperspectral image (HSI), pixel awareness, recurrent network, super-resolution

摘要 ： Hyperspectral images (HSIs) contain abundant spectral information, while the spatial resolution is usually limited. To
obtain high-spatial-resolution HSIs, various HSI super-resolution
(SR) methods are proposed. Currently, deep-learning-based SR
reconstruction methods are studied in depth, which take different
measures to make full use of the spatial and spectral information
of HSIs, and optimize the network with lots of trainings. Although
they have achieved satisfying spatial resolution, the spectral consistency before and after reconstruction is difficult to guarantee. In
this article, we

是否译文 ： 否