Optical Transient Object Classification in Wide-field Small Aperture Telescopes with a Neural Network

Jia, Peng; Zhao, Yifei; Xue, Gang; Cai, Dongmei

doi:10.3847/1538-3881/ab1e52

Cited by 19 publications

(13 citation statements)

References 51 publications

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“…The result is shown in Table 5, and it is observed that the proposed method has a better detection performance than that of the MHT and EAOM methods. Jia et al [52] proposed a method that uses a neural network for optical transient object classification. This method can reach an accuracy of 97.20% in streak-like objects with minimal SN R = 3.10.…”

Section: Detection Performance Of Space Debris In Optical Image Sequencesmentioning

confidence: 99%

Space Debris Detection Using Feature Learning of Candidate Regions in Optical Image Sequences

Xiang

Ersoy

et al. 2020

IEEE Access

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Space debris detection is important in space situation awareness and space asset protection. In this paper, we propose a method to detect space debris using feature learning of candidate regions. The acquired optical image sequences are first processed to remove hot pixels and flicker noise, and the nonuniform background information is removed by the proposed one dimensional mean iteration method. Then, the feature learning of candidate regions (FLCR) method is proposed to extract the candidate regions and to detect space debris. The candidate regions of space debris are precisely extracted, and then classified by a trained deep learning network. The feature learning model is trained using a large number of simulated space debris with different signal to noise ratios (SNRs) and motion parameters, instead of using real space debris, which make it difficult to extract a sufficient number of real space debris with diverse parameters in optical image sequences. Finally, the candidate regions are precisely placed in the optical image sequences. The experiment is performed using the simulated data and acquired image sequences. The results show that the proposed method has good performance when estimating and removing background, and it can detect low SNR space debris with high detection probability.INDEX TERMS Space debris detection, background estimation, candidate region extraction, deep learning.

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Section: Detection Performance Of Space Debris In Optical Image Sequencesmentioning

confidence: 99%

Space Debris Detection Using Feature Learning of Candidate Regions in Optical Image Sequences

Xiang

Ersoy

et al. 2020

IEEE Access

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“…In recent years, different machine learning based astronomical image classification algorithms have been developed and they have achieved higher and higher classification accuracy and recall rate (Romano et al 2006;Tachibana and Miller 2018;Gonzalez et al 2018;Burke et al 2019;Duev et al 2019a,b;Turpin et al 2020). For WFSATs, we have also proposed a transient classification method based on ensemble learning and neural networks (Jia et al 2019). Our method can achieve acceptable classification performance for different kinds of astronomical targets.…”

mentioning

confidence: 95%

Detection and Classification of Astronomical Targets with Deep Neural Networks in Wide-field Small Aperture Telescopes

Jia

Liu

Sun

2020

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Wide field small aperture telescopes are widely used for optical transient observations. Detection and classification of astronomical targets in observed images are the most important and basic step. In this paper, we propose an astronomical targets detection and classification framework based on deep neural networks. Our framework adopts the concept of the Faster R-CNN and uses a modified Resnet-50 as backbone network and a Feature Pyramid Network to extract features from images of different astronomical targets. To increase the generalization ability of our framework, we use both simulated and real observation images to train the neural network. After training, the neural network could detect and classify astronomical targets automatically. We test the performance of our framework with simulated data and find that our framework has almost the same detection ability as that of the traditional method for bright and isolated sources and our framework has 2 times better detection ability for dim targets, albeit all celestial objects detected by the traditional method can be classified correctly. We also use our framework to process real observation data and find that our framework can improve 25% detection ability than that of the traditional method when the threshold of our framework is 0.6. Rapid discovery of transient targets is quite important and we further propose to install our framework in embedded devices such as the Nvidia Jetson Xavier to achieve real-time astronomical targets detection and classification abilities.

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“…These algorithms are also imported into data processing frameworks for WFSATs. Machine learning based image classification algorithms (Jia et al 2019;Turpin et al 2020) are integrated with classical astronomical target detection algorithms, such as: SExtractor (Bertin & Arnouts 1996) or simplexy in Astrometry (Lang et al 2010), to detect and classify astronomical targets of different kinds. For these integrated astronomical target detection frameworks, classical detection algorithms will firstly obtain positions of astronomical target candidates.…”

Section: Introductionmentioning

confidence: 99%

The Deep Neural Network based Photometry and Astrometry Framework for Wide Field Small Aperture Telescopes

Jia¹,

Sun²,

Yang³

et al. 2021

Preprint

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Wide field small aperture telescopes (WFSATs) are mainly used to obtain scientific information of point-like and streak-like celestial objects. However, qualities of images obtained by WFSATs are seriously affected by the background noise and variable point spread functions. Developing high speed and high efficiency data processing method is of great importance for further scientific research. In recent years, deep neural networks have been proposed for detection and classification of celestial objects and have shown better performance than classical methods. In this paper, we further extend abilities of the deep neural network based astronomical target detection framework to make it suitable for photometry and astrometry. We add new branches into the deep neural network to obtain types, magnitudes and positions of different celestial objects at the same time. Tested with simulated data, we find that our neural network has better performance in photometry than classical methods. Because photometry and astrometry are regression algorithms, which would obtain high accuracy measurements instead of rough classification results, the accuracy of photometry and astrometry results would be affected by different observation conditions. To solve this problem, we further propose to use reference stars to train our deep neural network with transfer learning strategy when observation conditions change. The photometry framework proposed in this paper could be used as an end-to-end quick data processing framework for WFSATs, which can further increase response speed and scientific outputs of WFSATs.

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Optical Transient Object Classification in Wide-field Small Aperture Telescopes with a Neural Network

Cited by 19 publications

References 51 publications

Space Debris Detection Using Feature Learning of Candidate Regions in Optical Image Sequences

Space Debris Detection Using Feature Learning of Candidate Regions in Optical Image Sequences

Detection and Classification of Astronomical Targets with Deep Neural Networks in Wide-field Small Aperture Telescopes

The Deep Neural Network based Photometry and Astrometry Framework for Wide Field Small Aperture Telescopes

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