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

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

机构地区：[1]ATR National Key Laboratory of Defense Technology,Shenzhen University,Shenzhen 518060,China [2]Tencent,Shenzhen 518057,China

出　　处：《Chinese Journal of Electronics》2023年第3期415-428,共14页电子学报（英文版）

基　　金：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).

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.

关 键 词：Hyperspectral image SUPER-RESOLUTION Spectral difference Spatial-spectral feature extraction. 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]