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8 October 2015 Track extraction of moving targets in astronomical images based on the algorithm of NCST-PCNN
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Proceedings Volume 9675, AOPC 2015: Image Processing and Analysis; 96752I (2015) https://doi.org/10.1117/12.2202380
Event: Applied Optics and Photonics China (AOPC2015), 2015, Beijing, China
Abstract
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.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Lin Du, Huayan Sun, Tinghua Zhang, and Taohu Xu "Track extraction of moving targets in astronomical images based on the algorithm of NCST-PCNN", Proc. SPIE 9675, AOPC 2015: Image Processing and Analysis, 96752I (8 October 2015); https://doi.org/10.1117/12.2202380
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