Line-based registration for UAV remote sensing imagery of wide-spanning river basin

Wenqian Zang, Jiayuan Lin, Baosen Zhang, H. Tao, Zhongmei Wang
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引用次数: 5

Abstract

With the development of Unmanned Aerial Vehicles (UAVs) remote sensing technique, some researchers began to apply the UAV imagery to investigate Yellow River's dike hazards. There has been an urgent need for establishing automatic and accurate registration techniques of UAV remote sensing imagery. High-precision image registration will enhance the reliability of the investigation. As the Yellow River is very wide, it is very difficult to find enough Ground Control Points (GCP) and the limited GCPs are usually distributed unevenly. Therefore, the traditional point-based image registration approach cannot satisfy the requirements of imagery registration. However, the river basin contains linear features richly. This paper proposes a line-based registration approach for UAV remote sensing imagery of the wide-spanning river basin. This method regards the segment straight line as the registration primitives. After detect the primitives of input images, we estimate the parameters of transformation model between reference-image and registering-image with Modified Iterated Hough Transform (MIHT). Experiments using the UAV imagery of Ning-Meng section of Yellow River conclude that this is a robust method for registration for UAV remote sensing imagery of wide-spanning river basin.
基于线的大跨度流域无人机遥感影像配准
随着无人机遥感技术的发展,一些研究人员开始将无人机图像应用于黄河堤防灾害调查。建立无人机遥感影像的自动精确配准技术已成为迫切需要。高精度图像配准将提高调查的可靠性。由于黄河河面很宽,很难找到足够的地面控制点,而且有限的地面控制点往往分布不均匀。因此,传统的基于点的图像配准方法不能满足图像配准的要求。然而,该流域具有丰富的线性特征。提出了一种基于线的大跨度流域无人机遥感影像配准方法。该方法以线段直线为配准原语。在检测输入图像的原语后,利用改进迭代霍夫变换(MIHT)估计参考图像与配准图像之间的转换模型参数。利用黄河宁蒙段无人机影像进行的实验表明,该方法是一种鲁棒的大跨度流域无人机遥感影像配准方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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