{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T16:53:03Z","timestamp":1770915183358,"version":"3.50.1"},"reference-count":63,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,10]],"date-time":"2022-11-10T00:00:00Z","timestamp":1668038400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61901487"],"award-info":[{"award-number":["61901487"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61871384"],"award-info":[{"award-number":["61871384"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61921001"],"award-info":[{"award-number":["61921001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2021JJ40699"],"award-info":[{"award-number":["2021JJ40699"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2021TQ0084"],"award-info":[{"award-number":["2021TQ0084"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Hunan Province","award":["61901487"],"award-info":[{"award-number":["61901487"]}]},{"name":"Natural Science Foundation of Hunan Province","award":["61871384"],"award-info":[{"award-number":["61871384"]}]},{"name":"Natural Science Foundation of Hunan Province","award":["61921001"],"award-info":[{"award-number":["61921001"]}]},{"name":"Natural Science Foundation of Hunan Province","award":["2021JJ40699"],"award-info":[{"award-number":["2021JJ40699"]}]},{"name":"Natural Science Foundation of Hunan Province","award":["2021TQ0084"],"award-info":[{"award-number":["2021TQ0084"]}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["61901487"],"award-info":[{"award-number":["61901487"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["61871384"],"award-info":[{"award-number":["61871384"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["61921001"],"award-info":[{"award-number":["61921001"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2021JJ40699"],"award-info":[{"award-number":["2021JJ40699"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2021TQ0084"],"award-info":[{"award-number":["2021TQ0084"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Ballistic target recognition is of great significance for space attack and defense. The micro-motion features, which contain spatial and motion information, can be regarded as the foundation of the recognition of ballistic targets. To take full advantage of the micro-motion information of ballistic targets, this paper proposes a method based on feature fusion to recognize ballistic targets. The proposed method takes two types of data as input: the time\u2013range (TR) map and the time\u2013frequency (TF) spectrum. An improved feature extraction module based on 1D convolution and time self-attention is applied first to extract the multi-level features at each time instant and the global temporal information. Then, to efficiently fuse the features extracted from the TR map and TF spectrum, deep generalized canonical correlation analysis with center loss (DGCCA-CL) is proposed to transform the extracted features into a hidden space. The proposed DGCCA-CL possesses better performance in two aspects: small intra-class distance and compact representation, which is crucial to the fusion of multi-modality data. At last, the attention mechanism-based classifier which can adaptively focus on the important features is employed to give the target types. Experiment results show that the proposed method outperforms other network-based recognition methods.<\/jats:p>","DOI":"10.3390\/rs14225678","type":"journal-article","created":{"date-parts":[[2022,11,10]],"date-time":"2022-11-10T21:33:02Z","timestamp":1668115982000},"page":"5678","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Recognition of Ballistic Targets by Fusing Micro-Motion Features with Networks"],"prefix":"10.3390","volume":"14","author":[{"given":"Lei","family":"Yang","sequence":"first","affiliation":[{"name":"College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4674-2985","authenticated-orcid":false,"given":"Wenpeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Weidong","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1313","DOI":"10.1109\/TAES.2014.110545","article-title":"Three-Dimensional Precession Feature Extraction of Space Targets","volume":"50","author":"Luo","year":"2014","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2352","DOI":"10.1109\/TGRS.2008.2010854","article-title":"High-Resolution Three-Dimensional Imaging of Spinning Space Debris","volume":"47","author":"Bai","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1268","DOI":"10.1049\/iet-rsn.2018.5237","article-title":"Convolutional neural network for classifying space target of the same shape by using RCS time series","volume":"12","author":"Chen","year":"2018","journal-title":"IET Radar Sonar Navig."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3150067","article-title":"ISAR Imaging of Target Exhibiting Micro-Motion with Sparse Aperture via Model-Driven Deep Network","volume":"60","author":"Mai","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Lund\u00e9n, J., and Koivunen, V. (2016, January 2\u20136). Deep learning for HRRP-based target recognition in multistatic radar systems. Proceedings of the 2016 IEEE Radar Conference (RadarConf), Philadelphia, PA, USA.","DOI":"10.1109\/RADAR.2016.7485271"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1109\/TAES.2006.1603402","article-title":"Micro-Doppler Effect in Radar: Phenomenon, Model, and Simulation Study","volume":"42","author":"Chen","year":"2006","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2087","DOI":"10.1109\/TGRS.2009.2034367","article-title":"Micro-Doppler Effect Analysis and Feature Extraction in ISAR Imaging with Stepped-Frequency Chirp Signals","volume":"48","author":"Luo","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1109\/LGRS.2016.2633426","article-title":"Micromotion Feature Extraction and Distinguishing of Space Group Targets","volume":"14","author":"Zhao","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"11839","DOI":"10.1109\/JSEN.2019.2937995","article-title":"Parametric Representation and Application of Micro-Doppler Characteristics for Cone-Shaped Space Targets","volume":"19","author":"Ai","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2948","DOI":"10.1109\/JSEN.2022.3141213","article-title":"Micro-Doppler Based Target Recognition with Radars: A Review","volume":"22","author":"Hanif","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_11","unstructured":"Guo, X., Ng, C.S., de Jong, E., and Smits, A.B. (2019, January 2\u20134). Micro-Doppler based mini-UAV detection with low-cost distributed radar in dense urban environment. Proceedings of the 2019 16th European Radar Conference (EuRAD), Paris, France."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"012013","DOI":"10.1088\/1742-6596\/2213\/1\/012013","article-title":"Research on Micro-motion Modeling and Feature Extraction of Passive Bistatic Radar Based on CMMB Signal","volume":"2213","author":"Xia","year":"2022","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"5217","DOI":"10.1109\/TAP.2022.3172759","article-title":"Cone-Shaped Space Target Inertia Characteristics Identification by Deep Learning with Compressed Dataset","volume":"70","author":"Wang","year":"2022","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1243","DOI":"10.1109\/TAES.2019.2928611","article-title":"Efficient discrimination of ballistic targets with micromotions","volume":"56","author":"Choi","year":"2020","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1088","DOI":"10.1109\/TAES.2017.2665258","article-title":"On model, algorithms, and experiment for micro-Doppler-based recognition of ballistic targets","volume":"53","author":"Persico","year":"2017","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3168","DOI":"10.1109\/TAES.2019.2905281","article-title":"Novel Classification Algorithm for Ballistic Target Based on HRRP Frame","volume":"55","author":"Persico","year":"2019","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1049\/iet-rsn.2016.0271","article-title":"Multi-aspect micro-Doppler signatures for attitude-independent L\/N quotient estimation and its application to helicopter classification","volume":"11","author":"Zhang","year":"2017","journal-title":"IET Radar Sonar Navig."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1049\/el.2017.4317","article-title":"Detection of multiple micro-drones via cadence velocity diagram analysis","volume":"54","author":"Zhang","year":"2018","journal-title":"Electron. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1109\/LGRS.2016.2624820","article-title":"Drone classification using convolutional neural networks with merged Doppler images","volume":"14","author":"Kim","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_20","first-page":"1","article-title":"Classification of Space Micromotion Targets with Similar Shapes at Low SNR","volume":"19","author":"Wang","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/LGRS.2015.2491329","article-title":"Human Detection and Activity Classification Based on Micro-Doppler Signatures Using Deep Convolutional Neural Networks","volume":"13","author":"Kim","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wei, N., Zhang, L., and Zhang, X. (2022). A Weighted Decision-Level Fusion Architecture for Ballistic Target Classification in Midcourse. Phase. Sens., 22.","DOI":"10.3390\/s22176649"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3116","DOI":"10.1109\/TAES.2022.3145303","article-title":"Fusion Recognition of Space Targets with Micromotion","volume":"58","author":"Tian","year":"2022","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Lee, J.I., Kim, N., Min, S., Kim, J., Jeong, D.K., and Seo, D.W. (2022). Space Target Classification Improvement by Generating Micro-Doppler Signatures Considering Incident Angle. Sensors, 22.","DOI":"10.3390\/s22041653"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Jung, K., Lee, J.-I., Kim, N., Oh, S., and Seo, D.-W. (2021). Classification of Space Objects by Using Deep Learning with Micro-Doppler Signature Images. Sensors, 21.","DOI":"10.3390\/s21134365"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"487","DOI":"10.5515\/KJKIEES.2019.30.6.487","article-title":"Efficient recognition method for ballistic warheads by the fusion of feature vectors based on flight phase","volume":"30","author":"Choi","year":"2019","journal-title":"J. Korean Inst. Electromagn. Eng. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Tang, X., Zhang, W., Yu, Y., Turner, K., Derr, T., Wang, M., and Ntoutsi, E. (2021). Interpretable visual understanding with cognitive attention network. International Conference on Artificial Neural Networks, Springer.","DOI":"10.1007\/978-3-030-86362-3_45"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"5281","DOI":"10.1109\/TCSVT.2022.3142771","article-title":"Expansion-squeeze-excitation fusion network for elderly activity recognition","volume":"32","author":"Shu","year":"2022","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_29","unstructured":"Zhang, J., Yu, Y., Tang, S., Wu, J., and Li, W. (2021). Variational Autoencoder with CCA for Audio-Visual Cross-Modal Retrieval. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Tahmoush, D. (2020, January 21\u201325). Micro-range micro-Doppler for classification. Proceedings of the 2020 IEEE Radar Conference (RadarConf20), Florence, Italy.","DOI":"10.1109\/RadarConf2043947.2020.9266570"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wang, S., Song, J., Lien, J., Poupyrev, I., and Hilliges, O. (2016, January 16\u201319). Interacting with soli: Exploring fine-grained dynamic gesture recognition in the radio-frequency spectrum. Proceedings of the 29th Annual Symposium on User Interface Software and Technology, Tokyo, Japan.","DOI":"10.1145\/2984511.2984565"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Liu, Q., Zhang, X., and Liu, Y. (2022, January 20\u201322). Hierarchical Sequential Feature Extraction Network for Radar Target Recognition Based on HRRP. Proceedings of the 7th International Conference on Signal and Image Processing (ICSIP), Suzhou, China.","DOI":"10.1109\/ICSIP55141.2022.9886234"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Han, L., and Feng, C. (2020). Micro-Doppler-based space target recognition with a one-dimensional parallel network. Int. J. Antennas Propag., 128\u2013135.","DOI":"10.1155\/2020\/8013802"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"806","DOI":"10.1016\/j.aeue.2011.01.013","article-title":"Automatic classification of radar targets with micro-motions using entropy segmentation and time-frequency features","volume":"65","author":"Lei","year":"2011","journal-title":"AEU-Int. J. Electron. Commun."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1109\/TCDS.2021.3071170","article-title":"Comparing recognition performance and robustness of multimodal deep learning models for multimodal emotion recognition","volume":"14","author":"Liu","year":"2022","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"103894","DOI":"10.1016\/j.infrared.2021.103894","article-title":"MMF: A Multi-scale MobileNet based fusion method for infrared and visible image","volume":"119","author":"Liu","year":"2021","journal-title":"Infrared Phys. Technol."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Liang, T., Lin, G., Feng, L., Zhang, Y., and Lv, F. (2021, January 11\u201317). Attention is not Enough: Mitigating the Distribution Discrepancy in Asynchronous Multimodal Sequence Fusion. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00804"},{"key":"ref_38","unstructured":"Hou, M., Tang, J., Zhang, J., Kong, W., and Zhao, Q. (2019, January 8\u201314). Deep multimodal multilinear fusion with high-order polynomial pooling. Proceedings of the Advances in Neural Information Processing Systems 32 (NeurIPS 2019), Vancouver, BC, Canada."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Nguyen, D.K., and Okatani, T. (2018, January 18\u201321). Improved fusion of visual and language representations by dense symmetric co-attention for visual question answering. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00637"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"An, B., Zhang, W., and Liu, Y. (2021, January 9\u201311). Hand gesture recognition method based on dual-channel convolutional neural network. Proceedings of the 6th International Conference on Intelligent Computing and Signal Processing (ICSP), Xi\u2019an, China.","DOI":"10.1109\/ICSP51882.2021.9408844"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhou, P., Yang, W., Chen, W., Wang, Y., and Jia, J. (2019, January 12\u201317). Modality attention for end-to-end audio-visual speech recognition. Proceedings of the ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK.","DOI":"10.1109\/ICASSP.2019.8683733"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zadeh, A., Chen, M., Poria, S., Cambria, E., and Morency, L.-P. (2017). Tensor fusion network for multimodal sentiment analysis. arXiv.","DOI":"10.18653\/v1\/D17-1115"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Qiu, J.-L., Liu, W., and Lu, B.-L. (2018). Multi-view emotion recognition using deep canonical correlation analysis. International Conference on Neural Information Processing, Springer.","DOI":"10.1007\/978-3-030-04221-9_20"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"986","DOI":"10.1049\/ell2.12311","article-title":"Feature fusion for inverse synthetic aperture radar image classification via learning shared hidden space","volume":"57","author":"Lin","year":"2021","journal-title":"Electron. Lett."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"972","DOI":"10.1109\/TGRS.2018.2863743","article-title":"High-Resolution Radar Imaging in Complex Environments Based on Bayesian Learning with Mixture Models","volume":"57","author":"Bai","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Han, X., Zhong, Y., Cao, L., and Zhang, L. (2017). Pre-trained AlexNetarchitecture with pyramid pooling and supervision for highspatial resolution remote sensing image scene classification. Remote Sens., 9.","DOI":"10.3390\/rs9080848"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1162\/neco_a_01039","article-title":"Statistics of Visual Responses to Image Object Stimuli from Primate AIT Neurons to DNN Neurons","volume":"30","author":"Dong","year":"2018","journal-title":"Neural Comput."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going Deeper with Convolutions. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_50","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017, January 4\u20139). Attention Is All You Need. Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS\u201917), Long Beach, CA, USA."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Hotelling, H. (1992). Relations Between Two Sets of Variates. Breakthr. Stat., 162\u2013190.","DOI":"10.1007\/978-1-4612-4380-9_14"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1002\/1097-4679(196110)17:4<331::AID-JCLP2270170402>3.0.CO;2-D","article-title":"Generalized Canonical Correlations and Their Applications to Experimental Data","volume":"17","author":"Horst","year":"1961","journal-title":"J. Clin. Psychol."},{"key":"ref_53","unstructured":"Andrew, G., Arora, R., Bilmes, J., and Livescu, K. (2013, January 16\u201321). Deep canonical correlation analysis. Proceedings of the 30th International Conference on Machine Learning, Atlanta, GA, USA."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Benton, A., Khayrallah, H., Gujral, B., Reisinger, D.A., Zhang, S., and Arora, R. (2019, January 15). Deep Generalized Canonical Correlation Analysis. Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019), Florence, Italy.","DOI":"10.18653\/v1\/W19-4301"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"de Santana Correia, A., and Colombini, E.L. (2022). Attention, please! A survey of neural attention models in deep learning. Artif. Intell. Rev., 1\u201388.","DOI":"10.1007\/s10462-022-10148-x"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Tang, W., Yu, L., Wei, Y., and Tong, P. (2019, January 11\u201313). Radar Target Recognition of Ballistic Missile in Complex Scene. Proceedings of the 2019 IEEE International Conference on Signal, Information and Data Processing (ICSIDP), Chongqing, China.","DOI":"10.1109\/ICSIDP47821.2019.9172943"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Dai, J., and Wang, J. (2016, January 10\u201313). Recognition of Warheads Based on Features of Range Profiles in Ballistic Missile Defense. Proceedings of the 2016 CIE International Conference on Radar (RADAR), Guangzhou, China.","DOI":"10.1109\/RADAR.2016.8059177"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1680","DOI":"10.1109\/TAES.2014.120772","article-title":"Imaging of Rotation-Symmetric Space Targets Based on Electromagnetic Modeling","volume":"50","author":"Bai","year":"2014","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Yu, T., Meng, J., and Yuan, J. (2018, January 18\u201322). Multi-View Harmonized Bilinear Network for 3D Object Recognition. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00027"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Su, H., Maji, S., Kalogerakis, E., and Learned-Miller, E. (2015, January 7\u201313). Multi-View Convolutional Neural Networks for 3D Shape Recognition. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.114"},{"key":"ref_61","unstructured":"Joze HR, V., Shaban, A., Iuzzolino, M.L., and Koishida, K. (2021, January 20\u201325). MMTM: Multimodal transfer module for CNN fusion. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA."},{"key":"ref_62","unstructured":"Fu, Z., Liu, F., Wang, H., Qi, J., Fu, X., Zhou, A., and Li, Z. (2022). A cross-modal fusion network based on self-attention and residual structure for multimodal emotion recognition. arXiv."},{"key":"ref_63","first-page":"2579","article-title":"Visualizing data using t SNE","volume":"9","author":"Hinton","year":"2008","journal-title":"J. Mach. Learn. Res."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/22\/5678\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:13:51Z","timestamp":1760145231000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/22\/5678"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,10]]},"references-count":63,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["rs14225678"],"URL":"https:\/\/doi.org\/10.3390\/rs14225678","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,10]]}}}