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A sequential framework for image change detection.
IEEE Transactions on Image Processing ( IF 10.6 ) Pub Date : 2014-05-13 , DOI: 10.1109/tip.2014.2309432
Andrew J. Lingg , Edmund Zelnio , Fred Garber , Brian D. Rigling

We present a sequential framework for change detection. This framework allows us to use multiple images from reference and mission passes of a scene of interest in order to improve detection performance. It includes a change statistic that is easily updated when additional data becomes available. Detection performance using this statistic is predictable when the reference and image data are drawn from known distributions. We verify our performance prediction by simulation. Additionally, we show that detection performance improves with additional measurements on a set of synthetic aperture radar images and a set of visible images with unknown probability distributions.

中文翻译:

图像变化检测的顺序框架。

我们为变更检测提供了一个顺序框架。该框架允许我们使用感兴趣场景的参考和任务遍历中的多个图像,以提高检测性能。它包含一个更改统计信息,当有更多数据可用时,可以轻松地对其进行更新。当从已知分布中提取参考数据和图像数据时,使用此统计信息的检测性能是可预测的。我们通过仿真验证了我们的效果预测。此外,我们显示,通过对一组合成孔径雷达图像和一组具有未知概率分布的可见图像进行额外的测量,检测性能会提高。
更新日期:2019-11-01
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