Abstract

Hyperspectral (HS) pansharpening, which fuses the HS image with a high spatial resolution panchromatic (PAN) image, provides a good solution to overcome the limitation of HS imaging devices. However, most existing convolutional neural network (CNN)-based methods are hard to understand and lack interpretability due to the black-box design. In this Letter, we propose a multi-level spatial details cross-extraction and injection network (MSCIN) for HS pansharpening, which introduces the mature multi-resolution analysis (MRA) technology to the neural network. Following the general idea of MRA, the proposed MSCIN divides the pansharpening process into details extraction and details injection, in which the missing details and the injection gains are estimated by two specifically designed interpretable sub-networks. Experimental results on two widely used datasets demonstrate the superiority of the proposed method.

© 2022 Optica Publishing Group

Hyperspectral image super-resolution based on the transfer of both spectra and multi-level features

Xuheng Cao, Yusheng Lian, Zilong Liu, Han Zhou, Xiangmei Hu, Beiqing Huang, and Wan Zhang
Opt. Lett. 47(14) 3431-3434 (2022)

Local model-based hyperspectral pansharpening algorithm via optimization constraint equation and sliding window

Wenqian Dong, Song Xiao, and Jiahui Qu
J. Opt. Soc. Am. A 36(11) 1917-1925 (2019)

Hyperspectral image super-resolution via a multi-stage scheme without employing spatial degradation

Xuheng Cao, Yusheng Lian, Zilong Liu, Han Zhou, Bin Wang, Wan Zhang, and Beiqing Huang
Opt. Lett. 47(19) 5184-5187 (2022)

TIPFNet: a transformer-based infrared polarization image fusion network

Kunyuan Li, Meibin Qi, Shuo Zhuang, Yanfang Yang, and Jun Gao
Opt. Lett. 47(16) 4255-4258 (2022)

Sparse phase retrieval using a physics-informed neural network for Fourier ptychographic microscopy

Zhonghua Zhang, Tian Wang, Shaowei Feng, Yongxin Yang, Chunhong Lai, Xinwei Li, Lizhi Shao, and Xiaoming Jiang
Opt. Lett. 47(19) 4909-4912 (2022)