Remote Sensing Image Fusion with Convolutional Neural Network

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成果类型：

期刊论文

作者：

Jinying Zhong;Bin Yang;Guoyu Huang;Fei Zhong;Zhongze Chen

通讯作者：

Yang, Bin(yangbin01420@163.com)

作者机构：

[Bin Yang; Zhongze Chen; Jinying Zhong; Guoyu Huang; Fei Zhong] College of Electric Engineering, University of South China, Hengyang, 421001, China

通讯机构：

[Bin Yang] C

College of Electric Engineering, University of South China, Hengyang, China

语种：

英文

关键词：

Remote sensing image fusion;Super-resolution;Convolutional neural;network;Gram-Schmidt transform

期刊：

Sensing and Imaging

ISSN：

1557-2064

年：

2016

卷：

期：

页码：

140-155

DOI：

10.1007/s11220-016-0135-6

基金类别：

National Natural Science Foundation of ChinaNational Natural ScienceFoundation of China [61102108]; Scientific Research Fund of HunanProvincial Education DepartmentHunan Provincial Education Department[YB2013B039]; Young talents program of the University of South China;construct program of key disciplines in USC [NHXK04]

机构署名：

本校为第一且通讯机构

院系归属：

电气工程学院

摘要：

Remote sensing image fusion (RSIF) is referenced as restoring the high-resolution multispectral image from its corresponding low-resolution multispectral (LMS) image aided by the panchromatic (PAN) image. Most RSIF methods assume that the missing spatial details of the LMS image can be obtained from the high resolution PAN image. However, the distortions would be produced due to the much difference between the structural component of LMS image and that of PAN image. Actually, the LMS image can fully utilize its spatial details to improve the resolution. In this paper, a novel two-stage RSIF al...

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Remote Sensing Image Fusion with Convolutional Neural Network

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