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Automatic Identification and Extraction of Clouds from Astronomical Images Based on Support Vector Machine

机译:基于支持向量机的天文图像自动识别和提取云

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摘要

For the time-domain astronomical research, the optical telescopes with a small and medium aperture can get a huge amount of data through automatic sky surveying. A certain proportion of automatically acquired data are interfered by clouds, which makes it very difficult to automatically extract the dim objects and make photometry. Therefore, it is necessary to identify and extract clouds from these images as the index figures for a reference in the subsequent information extraction. In this paper, an astronomical image selection system based on the support vector machine is proposed, which sets the gray value inconsistency and texture difference as the reference to select the images interfered by clouds. Based on the classification results, by through the histogram transformation and feature selection, the index figures of clouds can be further extracted. The experimental results show that our method can achieve the realtime selection of astronomical images with a classification accuracy better than 98%. By the histogram transformation and feature selection the index figure of clouds can be preliminarily extracted as the references for the photometry and dim object extraction.
机译:对于时域天文研究,具有中小型孔径的光学望远镜可以通过自动天空测量获得大量数据。一定比例的自动获取数据由云干扰,这使得自动提取暗淡物体并使光度测量非常困难。因此,有必要从这些图像中识别和提取云作为随后的信息提取中的参考的索引图。在本文中,提出了一种基于支持向量机的天文图像选择系统,其设定了灰度不一致和纹理差异作为选择由云干扰的图像的参考。基于分类结果,通过直方图转换和特征选择,可以进一步提取云的索引图。实验结果表明,我们的方法可以达到天文图像的实时选择,分类精度优于98%。通过直方图转换和特征选择,可以预先提取云的索引图作为光度测定和暗淡对象提取的引用。

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