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首页> 外文期刊>Journal of visual communication & image representation >Quality assessment on remote sensing image based on neural networks
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Quality assessment on remote sensing image based on neural networks

机译：基于神经网络的遥感影像质量评估

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摘要

Image quality assessment is of great significance for the designment and application of remote sensing systems. CNN based method is proposed for image quality assessment on remote sensing image in this paper. Specifically, we first introduce the convolutional neural network and deep learning method. Then a deep CNN architecture is constructed to automatically extract image features to evaluate image quality. Afterward, the information entropy threshold is used to remove the image blocks with less information content. Finally, a deep network model with two convolutional layers is used to achieve feature extraction and image quality scoring. The experimental results show that the quality score of this method has good subjective and objective consistency for multi-distortion remote sensing images and common multi-distortion images. Evaluation of distorted images does not depend on a specific database and has database independence. In addition, our proposed method is simple to achievement. (C) 2019 Published by Elsevier Inc.
机译：图像质量评估对遥感系统的设计和应用具有重要意义。提出了一种基于CNN的遥感图像质量评估方法。具体来说，我们首先介绍卷积神经网络和深度学习方法。然后，构建了一个深层的CNN架构来自动提取图像特征以评估图像质量。之后，利用信息熵阈值去除信息量较少的图像块。最后，使用具有两个卷积层的深度网络模型来实现特征提取和图像质量评分。实验结果表明，该方法对多畸变遥感图像和普通多畸变图像具有良好的主观和客观一致性。失真图像的评估不依赖于特定的数据库，并且具有数据库独立性。此外，我们提出的方法易于实现。 （C）2019由Elsevier Inc.发布

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