Hyperspectral Image Super-Resolution Based on Spatial-Spectral Feature Extraction Network

作     者：LI Yanshan CHEN Shifu LUO Wenhan ZHOU Li XIE Weixin LI Yanshan;CHEN Shifu;LUO Wenhan;ZHOU Li;XIE Weixin

作者机构：ATR National Key Laboratory of Defense Technology Shenzhen University Tencent 

出 版 物：《Chinese Journal of Electronics》 (电子学报(英文))

年 卷 期：2023年第32卷第3期

页      面：415-428页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 081002[工学-信号与信息处理] 

基　　金：partially supported by the National Natural Science Foundation of China (61771319, 61871154) the Natural Science Foundation of Guangdong Province (2017A030313343, 2019A1515011307) the Shenzhen Science and Technology Project (JCYJ20180507182259896) 

主　　题：Hyperspectral image Super-resolution Spectral difference Spatial-spectral feature extraction 

摘      要：Constrained by the physics of hyperspectral sensors, the spatial resolution of hyperspectral images(HSI) is low. Hyperspectral image super-resolution(HSI SR) is a task to obtain high-resolution hyperspectral images from low-resolution hyperspectral images. Existing algorithms have the problem of losing important spectral information while improving spatial resolution.To handle this problem, a spatial-spectral feature extraction network(SSFEN) for HSI SR is proposed in this paper. It enhances the spatial resolution of the HSI while preserving the spectral information. The SSFEN is composed of three parts: spatial-spectral mapping network,spatial reconstruction network, and spatial-spectral fusing network. And a joint loss function with spatial and spectral constraints is designed to guide the training of the SSFEN. Experiment results show that the proposed method improves the spatial resolution of the HSI and effectively preserves the spectral information simultaneously.