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Track Extraction of Moving Targets in Astronomical Images based on the Algorithm of NCST-PCNN

机译:基于NCST-PCNN算法的天文图像在天文图像中追踪移动目标的提取

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Space targets in astronomical images such as spacecraft and space debris are always in the low level of brightness and hold a small amount of pixels, which are difficult to distinguish from fixed stars. Because of the difficulties of space target information extraction, dynamic object monitoring plays an important role in the military, aerospace and other fields, track extraction of moving targets in short-exposure astronomical images holds great significance. Firstly, capture the interesting stars by region growing method in the sequence of short-exposure images and extract the barycenter of interesting star by gray weighted method. Secondly, use adaptive threshold method to remove the error matching points and register the sequence of astronomical images. Thirdly, fuse the registered images by NCST-PCNN image fusion algorithm to hold the energy of stars in the images. Fourthly, get the difference of fused star image and final star image by subtraction of brightness value in the two images, the interesting possible moving targets will be captured by energy accumulation method. Finally, the track of moving target in astronomical images will be extracted by judging the accuracy of moving targets by track association and excluding the false moving targets. The algorithm proposed in the paper can effectively extract the moving target which is added artificially from three images or four images respectively, which verifies the effectiveness of the algorithm.
机译:天文学图像中的空间目标,例如航天器和空间碎片始终处于低水平的亮度并且保持少量像素,这难以区分固定的恒星。由于空间目标信息提取的困难,动态对象监测在军事,航空航天等领域起着重要作用,追踪短曝光天文图像中的移动目标的提取具有重要意义。首先,在短曝光图像序列中按区域生长方法捕获有趣的恒星,并通过灰色加权方法提取有趣的恒星的重心。其次,使用自适应阈值方法去除错误匹配点并注册天文图像的序列。第三,通过NCST-PCNN图像融合算法融合登记的图像,以保持图像中的星星的能量。第四,通过减法在两个图像中减去亮度值来获得融合星图像和最终星图像的差异,将通过能量累积方法捕获有趣的可能的移动目标。最后,将通过轨道关联判断移动目标的准确性并排除虚假移动目标来提取天文图像中的移动目标的轨道。本文中提出的算法可以有效地提取分别从三个图像或四个图像中提取的移动目标,这验证了算法的有效性。

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