Coupled Convolutional Neural Network-Based Detail Injection Method for Hyperspectral and Multispectral Image Fusion

Applied Sciences

卷:11

Fengde Jia¹  Dezheng Yang¹  Xiaochen Lu¹  Yifeng Zhao² 

[1] School of Information Science and Technology, Donghua University, Shanghai 201620, China;

[2] Shanghai Radio Equipment Research Institute, Shanghai 201109, China;

关键词: convolutional neural network;    hyper-sharpening;    hyperspectral;    image fusion;    multispectral;   

DOI  :  10.3390/app11010288

来源: DOAJ

【 摘 要 】

In this paper, a detail-injection method based on a coupled convolutional neural network (CNN) is proposed for hyperspectral (HS) and multispectral (MS) image fusion with the goal of enhancing the spatial resolution of HS images. Owing to the excellent performance in spectral fidelity of the detail-injection model and the image spatial–spectral feature exploration ability of CNN, the proposed method utilizes a couple of CNN networks as the feature extraction method and learns details from the HS and MS images individually. By appending an additional convolutional layer, both the extracted features of two images are concatenated to predict the missing details of the anticipated HS image. Experiments on simulated and real HS and MS data show that compared with some state-of-the-art HS and MS image fusion methods, our proposed method achieves better fusion results, provides excellent spectrum preservation ability, and is easy to implement.

【 授权许可】

Unknown