| 应用湖泊中心点作为控制点实现大区域遥感影像的配准 |
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| 引用本文： | 沈占锋,SHENGYongwei,骆剑承,夏列钢.应用湖泊中心点作为控制点实现大区域遥感影像的配准[J].遥感学报,2013,17(5):1118-1130. |
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| 作者姓名： | 沈占锋 SHENGYongwei 骆剑承 夏列钢 |
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| 作者单位： | 中国科学院 遥感与数字地球研究所, 北京 100101;Department of Geography, University of California, Los Angeles (UCLA), Los Angeles, CA 90095;中国科学院 遥感与数字地球研究所, 北京 100101;中国科学院 遥感与数字地球研究所, 北京 100101 |
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| 基金项目： | 国家高技术研究发展计划(863计划)(编号:2013AA12A401); National Aeronautics and Space Administration through the Terrestrial Hydrology Program(编号:NNX08AE51G); USGS Landsat Science Team Program (编号:G12PC00071) |
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| 摘 要： | 在进行北美阿拉斯加地区多期影像湖泊变化分析过程中,由于该区域长期被冰雪及湖泊覆盖,几乎没有较明显的地面标志点可作为影像配准控制点,给影像的配准工作带来困难。在分析长时相区域湖泊形状变化的基础上,认为湖泊中最稳定的点为湖泊的中心点,该点位置随湖泊面积的变化不大,可以作为影像配准的控制点。与多边形质心相比,多边形的最大内圆圆心始终位于多边形的内部,且以该点为圆心的内圆半径最大(对应的内圆即为最大内圆),其计算方法可以应用矢量多边形的Voronoi图来求得。本文在分析简单多边形Voronoi图性质及其计算方法的基础上,提出了一种面向复杂多边形的最大内圆圆心点查找方法,给出了其算法实现流程与算法的复杂度分析。通过北美阿拉斯加地区湖泊最大内圆圆心查找的测试实例,表明本文提出的方法能够较好地计算出各种复杂矢量多边形的最大内圆圆心点,并达到较高的计算效率,且以多边形最大内圆圆心点作为配准点实现的影像间配准效果也较好。
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| 关 键 词： | Voronoi图 最大内圆圆心 复杂多边形 阿拉斯加 |
| 收稿时间： | 2012/8/15 0:00:00 |
| 修稿时间： | 2013/1/29 0:00:00 |
Registration of remote sensing images of large regions using lake center points as ground control points |
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| SHEN Zhanfeng,SHENG Yongwei,LUO Jiancheng and XIA Liegang.Registration of remote sensing images of large regions using lake center points as ground control points[J].Journal of Remote Sensing,2013,17(5):1118-1130. |
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| Authors: | SHEN Zhanfeng SHENG Yongwei LUO Jiancheng and XIA Liegang |
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| Institution: | Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China;Department of Geography, University of California, Los Angeles (UCLA), Los Angeles, CA 90095, USA;Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China;Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China |
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| Abstract: | Image registration is used to detect changes in lakes by using large volumes of remotely sensed images. Alaska is covered by ice, snow, and lakes all year, which make it difficult to find valid Ground Control Points (GCPs) using remotely sensed images. This adds to the difficulty of image registration used in lake change detection and analysis. Based on lake change regulation, we propose that the deepest point of a lake is the most stable point during changes in the shape of that lake, and that this point can be used as a GCP in image registration. The center point of a polygon has been applied to many fields. Compared with the centroid of a polygon, the center point of the Largest Inner Circle (LIC) of a polygon is always in the interior of the polygon, and the distance from this point to all the edges of the polygon remains the furthest, which meets the requirements of numerous applications. The center point of a polygon can be computed using the Voronoi diagram of the polygon. After analyzing the Voronoi diagram computing procedure for a simple polygon, this paper presents the Voronoi method of generating a complex polygon, and then analyzes the algorithm complexity. We use the proposed algorithm to seek the LIC of many lakes in Alaska. Results show that the proposed algorithm can compute the center points of all the LICs of the lakes with high efficiency and achieve perfect registration effect based on these points. |
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| Keywords: | Voronoi diagram center point of the largest inner circle complex polygon Alaska |
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