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首页> 外文期刊>Geoscience and Remote Sensing Letters, IEEE >Automatic GCP Extraction in Mountainous Areas Using DEM and PolSAR Data
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Automatic GCP Extraction in Mountainous Areas Using DEM and PolSAR Data

机译:使用DEM和PolSAR数据自动提取山区GCP

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摘要

Mountainous areas in synthetic aperture radar (SAR) images suffer severe geometric distortions caused by different look directions. Consequently, ground control point (GCP) extraction hardly obtains accurate results for aircraft positioning. Based on the digital elevation model (DEM) and polarimetric SAR (PolSAR) data, we propose a method for extracting GCPs in mountainous areas by introducing the polarization orientation angle shift (POAS) to minimize geometric distortions. In this method, DEM data are used as the reference map by providing POASs at arbitrary look directions to make up for the look-direction sensitivity of POASs transformed from PolSAR data. The geometric distortions between POASs transformed from DEM and PolSAR data are effectively reduced by calculating the POASs from DEM data at the same look direction and look angles of the PolSAR data. In contrast to the SAR data, which have a large dynamic range, the values of POAS are limited to a small interval. Therefore, the illumination distortions induced by visualization can be reduced. Finally, the GCP extraction between the POAS images is conducted by bilateral filter scale-invariant feature transform. Experiments using various data at different look directions demonstrate that the proposed method obtains better-quality GCPs but less invalid keypoints than the method using only intensity images for mountainous areas.
机译:合成孔径雷达(SAR)图像中的山区会因不同的视线方向而遭受严重的几何失真。因此,地面控制点(GCP)提取几乎无法获得飞机定位的准确结果。基于数字高程模型(DEM)和极化SAR(PolSAR)数据,我们提出了一种通过引入极化取向角位移(POAS)来最小化几何畸变的山区GCP提取方法。在这种方法中,通过在任意视线方向提供POAS来弥补从PolSAR数据转换而来的POAS的视线方向敏感性,将DEM数据用作参考图。通过从DEM数据计算与POSAR数据相同的观察方向和视角的POAS,可以有效地减少从DEM和PolSAR数据转换而来的POAS之间的几何失真。与动态范围较大的SAR数据相比,POAS的值被限制为较小的间隔。因此,可以减少可视化引起的照明失真。最后,通过双边滤波器尺度不变特征变换进行POAS图像之间的GCP提取。通过在不同观察方向上使用各种数据的实验表明,与仅对山区使用强度图像的方法相比,该方法可获得质量更好的GCP,但无效关键点更少。

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