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Change detection for updates of vector database through region-based classification of VHR satellite data

机译:通过基于区域的VHR卫星数据的分类更改传染媒介数据库更新的检测

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Until now, interpretation of aerial photographs is a standard tool for monitoring land cover change where fine spatial resolutions are required and this task is expensive and time-consuming. Though, from a spaceborne perspective, the VHR satellite data are, since 1999, capable to meet the mapping and monitoring needs of municipal and regional planning agencies. Indeed, these data from the sensors Ikonos, QuickBird, OrbView-3, and in near future, the Pléiades-HR French sensors, have spatial resolution lower than 5 m in multispectral mode and lower than 1 m in panchromatic mode. These new sources of data combine the advantages of satellite data (synoptic view, digital format suitable for computer processing, quantitative land surface information at large spatial coverage and at frequent temporal intervals ...) with the very high spatial resolution. In spite of these advantages, the use of VHR satellite data involves some problems in traditional per-pixel classification often used in change detection techniques. There are still two occurring classification problems that can strongly deteriorate the result of a per-pixel classification of the VHR satellite data: spectral variability and poor spectral resolution. A solution to overcome these problems is the region-based classification that can be integrated in the common change detection techniques. The segmentation, before classification, produces regions which are more homogeneous in themselves than with nearby regions and represent discrete objects or areas in the image. Each image region then becomes a unit analysis and makes it possible to avoid much of the structural clutter. Image segmentation provides a logical transition from the units of pixels to larger units in maps more relevant to detect the changes in these. In this context, this research project suggests to use region based classification of VHR satellite data in the change detection processe for updates of vector database.
机译:到目前为止,航空照片的解释是监控陆地覆盖变化的标准工具,其中需要精细的空间分辨率,并且此任务昂贵且耗时。虽然,从星载的角度来看,VHR卫星数据以来,自1999年以来,能够满足市政和区域规划机构的测绘和监测需求。实际上,这些数据来自传感器Ikonos,Quickbird,Orbview-3,并且在不久的将来,Pléiades-HR法式传感器,在多光谱模式下具有低于5米的空间分辨率,并且在平面模式下低于1米。这些新的数据来源结合了卫星数据的优点(概要视图,适用于计算机处理的数字格式,在大空间覆盖的定量地面信息,频繁的时间间隔以频繁的时间间隔......)具有非常高的空间分辨率。尽管存在这些优点,但使用VHR卫星数据涉及通常用于改变检测技术的传统每像素分类中的一些问题。仍有两个发生的分类问题,可以强烈地恶化VHR卫星数据的每个像素分类的结果:光谱变异性和差的光谱分辨率。克服这些问题的解决方案是基于区域的分类,可以集成在共同的变化检测技术中。在分类之前,在分类之前,在本身产生比附近区域更均匀的区域,并且代表图像中的离散物体或区域。然后每个图像区域变为单位分析,并且可以避免大部分结构杂波。图像分割提供从像素单位到更大单位的逻辑转换,以更相关的地图以检测这些更改。在这种情况下,该研究项目表明,在更新的矢量数据库更新中使用基于VHR卫星数据的区域的VHR卫星数据分类。

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