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基于循环翻译的协作培训,用于高光谱-RGB多模式变化检测.

Wenqian Dong, Junying Ren, Song Xiao

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society

|September 16, 2025

概括

此摘要是机器生成的。

这项研究引入了一种用于高光谱图像-RGB多式变化检测 (HSI-RGB CD) 的新方法. 该方法使用循环翻译和联合培训来有效地识别土地覆盖面的变化,使用来自不同传感器的互补数据.

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科学领域:

遥感遥感遥感遥感
计算机视觉计算机视觉
地理空间分析是什么

背景情况:

超光谱图像变化检测 (HSI-CD) 依赖于同质的多时段HSI,这些HSI很难获得.
通过整合RGB数据来解决HSI可用性问题,但由于不同的成像机制而面临挑战.

研究的目的:

开发一种强大的HSI-RGB多式变化检测方法,克服模式差异.
从HSI和RGB数据中实现互补变化信息的协作学习.

主要方法:

基于循环翻译的协作培训 (联合培训) 框架被提议用于HSI-RGB多式联络CD.
一个基于CycleGAN的交叉模式引导的图像翻译模块通过双向翻译来缓解模式差异.
一个空间光谱交互式联合培训CD模块促进了代的跨模式信息交换,以提取差异特征.

主要成果:

与现有的CD技术相比,拟议的方法在真实和合成数据集上都显示出更高的性能.
该方法有效地通过利用跨模式交互来提取互补的变化信息.
成功缓解模式差异使更准确的变化检测成为可能.

结论:

开发的循环翻译和联合培训方法在HSI-RGB多式联通变化检测方面取得了重大进展.
该方法提供了一个强大的解决方案,用于识别土地覆盖面的变化,使用不同的数据源.
该研究提供了一个新的公共HSI-RGB多式联网数据集和开源代码.