{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:34:09Z","timestamp":1760150049236,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2023,10,16]],"date-time":"2023-10-16T00:00:00Z","timestamp":1697414400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In the task of classifying high-altitude flying objects, due to the limitations of the target flight altitude, there are issues such as insufficient contour information, low contrast, and fewer pixels in the target objects obtained through infrared detection technology, making it challenging to accurately classify them. In order to improve the classification performance and achieve the effective classification of the targets, this study proposes a high-altitude flying object classification algorithm based on radiation characteristic data. The target images are obtained through an infrared camera, and the radiation characteristics of the targets are measured using radiation characteristic measurement techniques. The classification is performed using an attention-based convolutional neural network (CNN) and gated recurrent unit (GRU) (referred to as ACGRU). In ACGRU, CNN-GRU and GRU-CNN networks are used to extract vectorized radiation characteristic data. The raw data are processed using Highway Network, and SoftMax is used for high-altitude flying object classification. The classification accuracy of ACGRU reaches 94.8%, and the F1 score reaches 93.9%. To verify the generalization performance of the model, comparative experiments and significance analysis were conducted with other algorithms on radiation characteristic datasets and 17 multidimensional time series datasets from UEA. The results show that the proposed ACGRU algorithm performs excellently in the task of high-altitude flying object classification based on radiation characteristics.<\/jats:p>","DOI":"10.3390\/rs15204985","type":"journal-article","created":{"date-parts":[[2023,10,16]],"date-time":"2023-10-16T10:06:36Z","timestamp":1697450796000},"page":"4985","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Classification of High-Altitude Flying Objects Based on Radiation Characteristics with Attention-Convolutional Neural Network and Gated Recurrent Unit Network"],"prefix":"10.3390","volume":"15","author":[{"given":"Deen","family":"Dai","sequence":"first","affiliation":[{"name":"State Key Laboratory of Laser Interaction with Matter, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lihua","family":"Cao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Laser Interaction with Matter, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yangfan","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Laser Interaction with Matter, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yao","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Laser Interaction with Matter, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhaolong","family":"Wu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Laser Interaction with Matter, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"45","DOI":"10.4316\/AECE.2022.02006","article-title":"Classification of Low-Resolution Flying Objects in Videos Using the Machine Learning Approach","volume":"22","author":"Stancic","year":"2022","journal-title":"Adv. Electr. Comput. Eng."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"107729","DOI":"10.1016\/j.patcog.2020.107729","article-title":"Infrared small target detection via adaptive M-estimator ring top-hat transformation","volume":"112","author":"Deng","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.neucom.2020.08.065","article-title":"Infrared small target detection via self-regularized weighted sparse model","volume":"420","author":"Zhang","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"103659","DOI":"10.1016\/j.infrared.2021.103659","article-title":"ISTDet: An efficient end-to-end neural network for infrared small target detection","volume":"114","author":"Ju","year":"2021","journal-title":"Infrared Phys. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1745","DOI":"10.1109\/TIP.2022.3199107","article-title":"Dense Nested Attention Network for Infrared Small Target Detection","volume":"32","author":"Li","year":"2023","journal-title":"IEEE Trans. Image Process."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Deng, X., Wang, Y., Han, G., and Xue, T. (2022). Research on a measurement method for middle-infrared radiation characteristics of aircraft. Machines, 10.","DOI":"10.3390\/machines10010044"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3124","DOI":"10.1364\/AO.386417","article-title":"Small-target judging method based on the effective image pixels for measuring infrared radiation characteristics","volume":"59","author":"Wang","year":"2020","journal-title":"Appl. Opt."},{"key":"ref_8","first-page":"37","article-title":"Correlation between infrared radiation characteristic signals and target maneuvering modes","volume":"38","author":"Kou","year":"2018","journal-title":"Acta Opt. Sin."},{"key":"ref_9","first-page":"89","article-title":"Study on the infrared radiation characteristics of the sky background","volume":"Volume 9678","author":"Chen","year":"2015","journal-title":"Proceedings of the AOPC 2015: Telescope and Space Optical Instrumentation"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ejor.2019.03.018","article-title":"Advances in Bayesian decision making in reliability","volume":"282","author":"Insua","year":"2020","journal-title":"Eur. J. Oper. Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"218","DOI":"10.21037\/atm.2016.03.37","article-title":"Introduction to machine learning: K-nearest neighbors","volume":"4","author":"Zhang","year":"2016","journal-title":"Ann. Transl. Med."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1565","DOI":"10.1038\/nbt1206-1565","article-title":"What is a support vector machine?","volume":"24","author":"Noble","year":"2006","journal-title":"Nat. Biotechnol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.patcog.2017.10.013","article-title":"Recent advances in convolutional neural networks","volume":"77","author":"Gu","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_14","unstructured":"Chung, J., Gulcehre, C., Cho, K.H., and Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep learning in neural networks: An overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Netw."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"111514","DOI":"10.1016\/j.jss.2022.111514","article-title":"SCGRU: A general approach for identifying multiple classes of self-admitted technical debt with text generation oversampling","volume":"195","author":"Zhu","year":"2023","journal-title":"J. Syst. Softw."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Cai, J., Zhang, K., and Jiang, H. (2023). Power Quality Disturbance Classification Based on Parallel Fusion of CNN and GRU. Energies, 16.","DOI":"10.3390\/en16104029"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yadav, H., Shah, P., Gandhi, N., Vyas, T., Nair, A., Desai, S., Gohil, L., Tanwar, S., Sharma, R., and Marina, V. (2023). CNN and Bidirectional GRU-Based Heartbeat Sound Classification Architecture for Elderly People. Mathematics, 11.","DOI":"10.3390\/math11061365"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kim, A.R., Kim, H.S., Kang, C.H., and Kim, S.Y. (2023). The design of the 1D CNN\u2013GRU network based on the RCS for classification of multiclass missiles. Remote Sens., 15.","DOI":"10.3390\/rs15030577"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/S1079-4042(09)04205-2","article-title":"Calculation of the radiation characteristics of blackbody radiation sources","volume":"42","author":"Prokhorov","year":"2009","journal-title":"Exp. Methods Phys. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.infrared.2018.07.005","article-title":"Modeling of the Mid-wave Infrared Radiation Characteristics of the Sea surface based on Measured Data","volume":"93","author":"Yuan","year":"2018","journal-title":"Infrared Phys. Technol."},{"key":"ref_22","unstructured":"Bagnall, A., Dau, H.A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P., and Keog, E. (2018). The UEA Multivariate Time Series Classification Archive, 2018. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.paerosci.2007.06.002","article-title":"Infrared signature studies of aerospace vehicles","volume":"43","author":"Mahulikar","year":"2007","journal-title":"Prog. Aerosp. Sci."},{"key":"ref_24","first-page":"1051","article-title":"The standard infrared radiation model","volume":"81","author":"Ludwig","year":"1981","journal-title":"AIAA Paper"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.aop.2018.06.004","article-title":"A new approach to the generalization of Planck\u2019s law of black-body radiation","volume":"395","author":"Choudhury","year":"2018","journal-title":"Ann. Phys."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.neucom.2021.03.091","article-title":"A review on the attention mechanism of deep learning","volume":"452","author":"Niu","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV) 2018, Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201322). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_30","unstructured":"Zhang, A., Lipton, Z.C., Li, M., and Smola, A.J. (2021). Dive Into Deep Learning. arXiv."},{"key":"ref_31","unstructured":"Glorot, X., and Bengio, Y. (2010, January 13\u201315). Understanding the difficulty of training deep feedforward neural networks. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics\u2014JMLR Workshop and Conference Proceedings, Sardinia, Italy."},{"key":"ref_32","unstructured":"Srivastava, R.K., Greff, K., and Schmidhuber, J. (2015). Highway networks. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1007\/s10618-019-00619-1","article-title":"Deep Learning for Time Series Classification: A Review","volume":"33","author":"Forestier","year":"2019","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1007\/s10618-020-00727-3","article-title":"The Great Multivariate Time Series Classification Bake Off: A Review and Experimental Evaluation of Recent Algorithmic Advances","volume":"35","author":"Ruiz","year":"2021","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Foumani, N.M., Tan, C.W., Webb, G.I., and Salehi, M. (2023). Improving Position Encoding of Transformers for Multivariate Time Series Classification. arXiv.","DOI":"10.1007\/s10618-023-00948-2"},{"key":"ref_36","first-page":"1","article-title":"Statistical comparisons of classifiers over multiple data sets","volume":"7","year":"2006","journal-title":"J. Mach. Learn. Res."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/20\/4985\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:07:51Z","timestamp":1760130471000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/20\/4985"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,16]]},"references-count":36,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2023,10]]}},"alternative-id":["rs15204985"],"URL":"https:\/\/doi.org\/10.3390\/rs15204985","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,10,16]]}}}