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基于深度学习和人眼视觉系统的遥感图像质量评价

Quality Assessment of Remote Sensing Images Based on Deep Learning and Human Visual System

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摘要

提出了一种基于深度学习和人眼视觉特性的遥感图像质量评价方法。利用卷积神经网络和反向传播神经网络分类器,同时对遥感图像进行特征学习及模糊和噪声强度的等级分类。利用掩盖效应和感知加权因子修正评价模型,得到了更符合人眼视觉的遥感图像质量评价结果。研究结果表明,所提方法有效解决了同时存在模糊和噪声的遥感图像质量评价的困难,能有效准确地评价遥感图像的质量,且与主观评价结果有较好的一致性,更符合人眼视觉感受。

Abstract

A quality assessment method of remote sensing images is proposed based on deep learning and the human visual characteristics. The convolutional neural network and the back propagation neural network classifiers are used for the simultaneous feature learning and grade classification of blur and noise intensity for the remote sensing images. The masking effect and the corrected assessment model of the perceptual weighting factors are used to obtain the quality assessment results of remote sensing images, which are more in line with the human visual characteristics. The research results show that the proposed method can effectively solve the difficulty in the quality assessment of remote sensing images with both blur and noise. Moreover, the quality of remote sensing images can be effectively and accurately evaluated, and the results are well in good agreement with both the subjective evaluation results and the human visual experiences.

Newport宣传-MKS新实验室计划
补充资料

中图分类号:TP183

DOI:10.3788/lop56.061101

所属栏目:成像系统

收稿日期:2018-09-18

修改稿日期:2018-09-26

网络出版日期:2018-09-30

作者单位    点击查看

刘迪:航天工程大学电子与光学工程系, 北京 101416
李迎春:航天工程大学电子与光学工程系, 北京 101416

联系人作者:李迎春(13910953181@139.com)

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引用该论文

Liu Di,Li Yingchun. Quality Assessment of Remote Sensing Images Based on Deep Learning and Human Visual System[J]. Laser & Optoelectronics Progress, 2019, 56(6): 061101

刘迪,李迎春. 基于深度学习和人眼视觉系统的遥感图像质量评价[J]. 激光与光电子学进展, 2019, 56(6): 061101

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