Considering camera distortion panoramic images forming method for unmanned aerial vehicle multispectral data

A. Lamaka
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引用次数: 1

Abstract

The work is devoted to the study and modification of existing methods for merging a number of images obtained using a multispectral camera installed on an unmanned aerial vehicle into a single panoramic image for the purpose of its further thematic processing. A generalised method based on the existing detectors and descriptors of special areas of images was proposed for the automated solution of this issue, as well as the developed method for filtering matches of special areas. An analysis was carried out to select the best detectors and descriptors of special areas for the tasks of merging images of forest areas. It has been determined that the combination of ORB and FREAK methods show better results in detecting and describing specific points to perform the above tasks, than BRISK, SURF and ORB methods. Particular attention is paid to the importance of determining and correcting camera distortion used in data acquisition, the method used and the results of distortion correction are described. The effect of camera distortion to the displacement between singular points standard deviation in the case of data alignment is estimated. It is shown that the proposed automatic obtaining panoramic multispectral images method makes it possible to connect images with an average accuracy of up to 5 pixels when solving connection of multispectral images set issues.
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考虑相机畸变的无人机多光谱全景图像形成方法
这项工作致力于研究和修改现有的方法,将安装在无人驾驶飞行器上的多光谱相机获得的许多图像合并为单个全景图像,以便进一步进行专题处理。提出了一种基于现有图像特殊区域检测器和描述符的广义方法来自动解决这一问题,并开发了特殊区域匹配滤波方法。针对森林区域图像合并任务,对特殊区域的检测器和描述符进行了优选分析。结果表明,结合ORB和FREAK方法在检测和描述特定点以执行上述任务方面的效果优于BRISK、SURF和ORB方法。特别注意了在数据采集中确定和校正相机畸变的重要性,描述了畸变校正的方法和结果。估计了在数据对齐情况下,相机畸变对奇异点间位移的影响。结果表明,本文提出的全景多光谱图像自动获取方法在解决多光谱图像集连接问题时,可以实现平均精度高达5像素的图像连接。
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