@inproceedings{061bad5c97b147aeb20ce636794d4e0c,
title = "Hyperspectral image super-resolution via convolutional neural network",
abstract = "Due to the tradeoff between spatial and spectral resolution in remote sensing imaging, hyperspectral images are often acquired with a relative low spatial resolution, which limits their applications in many areas. Inspired by recent achievements in convolutional neural network (CNN) based super resolution (SR), a novel CNN based framework is constructed for SR of hyperspectral images by considering both spatial context and spectral correlation. As a result, the spectral distortion incurred by directly applying traditional SR algorithms to hyperspectral images is alleviated. Experimental results on several benchmark hyperspectral datasets have demonstrated that higher quality of reconstruction and spectral fidelity can be achieved, compared to band-wise manner based algorithms.",
keywords = "Convolutional neural network, Deep learning, Hyperspectral, Super-resolution",
author = "Shaohui Mei and Xin Yuan and Jingyu Ji and Shuai Wan and Junhui Hou and Qian Du",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 24th IEEE International Conference on Image Processing, ICIP 2017 ; Conference date: 17-09-2017 Through 20-09-2017",
year = "2017",
month = jul,
day = "2",
doi = "10.1109/ICIP.2017.8297093",
language = "英语",
series = "Proceedings - International Conference on Image Processing, ICIP",
publisher = "IEEE Computer Society",
pages = "4297--4301",
booktitle = "2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings",
}