通过无监督的生成对抗网络进行远程传感图像消毒
Liquan Zhao1, Yanjiang Yin1, Tie Zhong1
1Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin 132012, China.
Sensors (Basel, Switzerland)
|September 9, 2023
概括
本研究介绍了一种无监督的生成对抗网络,用于远程传感图像处理. 这种新的方法有效地消除了雾,显著改善了图像质量和数据解释.
科学领域:
- 计算机视觉 计算机视觉
- 遥感 遥感 遥感 遥感
- 人工智能的人工智能
背景情况:
- 雾显著降低了遥感图像质量,阻碍了信息提取.
- 现有的除气方法与遥感图像的复杂性作斗争.
研究的目的:
- 开发一个无监督的生成对抗网络 (GAN) 以实现有效的远程传感图像处理.
- 提高受雾影响的遥感数据的视觉质量和可解释性.
主要方法:
- 提出了一个无监督的GAN,有两个生成器和两个用于图像破坏的歧视器.
- 在编码器-解码器生成器架构中集成的多级特征提取和注意模块.
- 引入了一个改进的损失函数与颜色常数损失,并设计了一个多级别的分辨器.
主要成果:
- 与最先进的方法相比,实现了最高峰信号噪声比 (PSNR) 和结构相似度指数 (SSIM) 度量.
- 在有效地去除远程传感图像中的雾方面表现出卓越的性能.
- 拟议的注意力模块有效地指导特征提取,以强调雾和纹理.
结论:
- 无监督的GAN有效地减轻了遥感图像中的雾.
- 新的架构和损失函数导致图像质量的显著改善.
- 该方法为增强远程传感数据分析提供了一个有希望的解决方案.
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