超光谱图像否定:从模型驱动,数据驱动,到模型数据驱动
概括
本综述分析了超光谱图像 (HSI) 中的噪声,并评估了消除噪声的方法. 它为开发有效的HSI denoising算法和未来研究方向提供了关键的见解.
科学领域:
- 遥感 遥感 遥感 遥感
- 图像处理 图像处理
- 信号处理 信号处理
背景情况:
- 超光谱图像 (HSI) 噪声对数据的解释和应用产生重大影响.
- 有效的解密对于释放HSI数据的全部潜力至关重要.
研究的目的:
- 提供对高光谱图像 (HSI) 否定方法的全面技术审查.
- 分析各种杂的HSI中的噪音,并确定算法开发的关键考虑因素.
- 评估和比较现有的HSI无色化技术.
主要方法:
- 在不同杂的HSI数据集中进行噪音分析.
- 为优化制定一个一般的HSI恢复模型.
- 对基于模型,数据 (包括二维CNN,3DCNN,混合网络和无监督网络) 和基于模型数据的揭露策略的全面审查.
- 使用模拟和真实HSI数据对除方法的比较评估.
主要成果:
- 总结和对比每一个denoising策略的优点和缺点.
- 基于无色图像的分类准确性的HSI无色化方法的评估.
- 评估各种高强度标识破解技术的执行效率.
- 确定编程HSI解密算法的关键点.
结论:
- 在HSI中的噪音污染需要先进的消噪技术来进行可靠的分析.
- 该综述提供了一个结构化的概述,并对当前的高强度标识识别方法进行了比较分析.
- 概述了未来的研究方向,以推进高强度分析的领域.
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