Hyperspectral Image Sharpening Based on Deep Convolutional Neural Network and Spatial-Spectral Spread Transform Models  

作　　者：陆小辰 刘晓慧 杨德政 赵萍 阳云龙 LU Xiaochen;LIU Xiaohui;YANG Dezheng;ZHAO Ping;YANG Yunlong(College of Information Science and Technology,Donghua University,Shanghai 201620,China)

机构地区：[1]College of Information Science and Technology,Donghua University,Shanghai 201620,China

出　　处：《Journal of Donghua University(English Edition)》2023年第1期88-95,共8页东华大学学报（英文版）

基　　金：National Natural Science Foundation of China(No.61902060);Natural Science Foundation of Shanghai,China(No.19ZR1453800);Fundamental Research Funds for the Central Universities,China(No.2232021D-33)。

摘　　要：In order to improve the spatial resolution of hyperspectral(HS)image and minimize the spectral distortion,an HS and multispectral(MS)image fusion approach based on convolutional neural network(CNN)is proposed.The proposed approach incorporates the linear spectral mixture model and spatial-spectral spread transform model into the learning phase of network,aiming to fully exploit the spatial-spectral information of HS and MS images,and improve the spectral fidelity of fusion images.Experiments on two real remote sensing data under different resolutions demonstrate that compared with some state-of-the-art HS and MS image fusion methods,the proposed approach achieves superior spectral fidelities and lower fusion errors.

关 键 词：convolutional neural network(CNN) hyperspectral image image fusion multispectral image unmixing method 

分 类 号：TP751.1[自动化与计算机技术—检测技术与自动化装置]