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大気歪み画像モデルを組み込んだ深層学習によるリモートセンシング画像の画質改善

利用深度学习结合大气畸变图像模型提高遥感图像的图像质量

基本信息

批准号：
19J13820
负责人：
LI YINHAO
金额：
$ 1.09万
依托单位：
Ritsumeikan University
依托单位国家：
日本
项目类别：
Grant-in-Aid for JSPS Fellows
财政年份：
2019
资助国家：
日本
起止时间：
2019-04-25 至 2021-03-31
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-19J13820/
关键词：
Image Processing Image processing Artificial intelligence

项目摘要

The purpose of this research is to enhance the quality of remote sensing images using deep 3D convolutional neural networks (CNNs).Improving the performance of CNNs-based methods with a few parameters and short processing time is very difficult, although it is a desirable task to improve the quality of remote sensing images. Thus, I proposed a new 2D CNN network using a parallel-connected backbone, the architecture of which consists residual connections and channel-attention mechanism. This work has been accepted by ACCV Workshop on Machine Learning and Computing for Visual Semantic Analysis, 2020.In addition, I proposed a new multi-spectral image fusion method using a combination of the proposed lightweight 3D VolumeNet model (which has been accepted by IEEE Transactions on Image Processing, 2021) and the texture transfer method using other modality high-resolution images. The experimental results show that the proposed method outperforms the existing methods in terms of objective accuracy assessment, efficiency and visual subjective evaluation. Consequently, I plan to submit this work to the IEEE Transactions on Geoscience and Remote Sensing.Overall, the progress of the research is basically in line with the original plan. I studied and referred to various state-of-the-art methods and then built my original models. It is worth noting that the proposed methods not only can exceed the existing methods in accuracy, but also has a faster processing speed and lower hardware requirements for saving the model, so they are suitable for practical applications.

本研究的目的是使用深度3D卷积神经网络（CNN）来提高遥感图像的质量。尽管提高遥感图像的质量是一项令人满意的任务，但在参数少、处理时间短的情况下提高基于CNN的方法的性能是非常困难的。因此，我提出了一个新的2D CNN网络，使用并行连接的骨干，其架构包括剩余连接和通道注意机制。该工作已被ACCV 2020年视觉语义分析机器学习和计算研讨会接受。此外，我提出了一种新的多光谱图像融合方法，该方法使用所提出的轻量级3D VolumeNet模型（已被IEEE Transactions on Image Processing，2021接受）和使用其他模态高分辨率图像的纹理转移方法相结合。实验结果表明，该方法在客观准确性评价、效率和视觉主观评价方面均优于现有方法。因此，我计划将这项工作提交给IEEE Transactions on Geoscience and Remote Sensing，总体而言，研究进展与原计划基本一致。我研究并参考了各种最先进的方法，然后建立了我的原始模型。值得注意的是，本文提出的方法不仅在精度上优于现有方法，而且具有更快的处理速度和更低的保存模型的硬件要求，适合于实际应用。