论文标题
基于图像堆栈的多通SAR更改检测的自回旋模型
Autoregressive Model for Multi-Pass SAR Change Detection Based on Image Stacks
论文作者
论文摘要
变化检测是重要的合成孔径雷达（SAR）应用，通常用于在不同时刻的时间内检测地面场景测量的变化。传统上，变化检测算法（CDA）主要是针对在不同瞬间检索到的两个合成孔径雷达（SAR）图像的设计。但是，可以使用更多图像来改善算法性能，女巫作为SAR变更检测的研究主题出现。图像堆栈信息可以随着时间的推移视为数据系列，并且可以通过自回归（AR）模型进行建模。因此，我们根据图像堆栈考虑了AR模型，就SAR更改检测提出了一些初步发现。为图像堆栈中的每个像素位置应用AR模型，我们获得了地面场景的估计图像，该图像可用作CDA的参考图像。实验结果表明，AR模型的地面场景估计是准确的，可用于更改检测应用。
Change detection is an important synthetic aperture radar (SAR) application, usually used to detect changes on the ground scene measurements in different moments in time. Traditionally, change detection algorithm (CDA) is mainly designed for two synthetic aperture radar (SAR) images retrieved at different instants. However, more images can be used to improve the algorithms performance, witch emerges as a research topic on SAR change detection. Image stack information can be treated as a data series over time and can be modeled by autoregressive (AR) models. Thus, we present some initial findings on SAR change detection based on image stack considering AR models. Applying AR model for each pixel position in the image stack, we obtained an estimated image of the ground scene which can be used as a reference image for CDA. The experimental results reveal that ground scene estimates by the AR models is accurate and can be used for change detection applications.
