{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T16:18:47Z","timestamp":1776183527009,"version":"3.50.1"},"reference-count":52,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T00:00:00Z","timestamp":1724544000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"publisher","award":["62222102"],"award-info":[{"award-number":["62222102"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"publisher","award":["62171023"],"award-info":[{"award-number":["62171023"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Polarimetric high-resolution range profile (HRRP), with its rich polarimetric and spatial information, has become increasingly important in radar automatic target recognition (RATR). This study proposes an interpretable target-aware vision Transformer (ITAViT) for polarimetric HRRP target recognition with a novel attention loss. In ITAViT, we initially fuse the polarimetric features and the amplitude of polarimetric HRRP with a polarimetric preprocessing layer (PPL) to obtain the feature map as the input of the subsequent network. The vision Transformer (ViT) is then used as the backbone to automatically extract both local and global features. Most importantly, we introduce a novel attention loss to optimize the alignment between the attention map and the HRRP span. Thus, it can improve the difference between the target and the background, and enable the model to more effectively focus on real target areas. Experiments on a simulated X-band dataset demonstrate that our proposed ITAViT outperforms comparative models under various experimental conditions. Ablation studies highlight the effectiveness of polarimetric preprocessing and attention loss. Furthermore, the visualization of the self-attention mechanism suggests that attention loss enhances the interpretability of the network.<\/jats:p>","DOI":"10.3390\/rs16173135","type":"journal-article","created":{"date-parts":[[2024,8,26]],"date-time":"2024-08-26T03:14:31Z","timestamp":1724642071000},"page":"3135","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["An Interpretable Target-Aware Vision Transformer for Polarimetric HRRP Target Recognition with a Novel Attention Loss"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-8820-4352","authenticated-orcid":false,"given":"Fan","family":"Gao","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5940-1824","authenticated-orcid":false,"given":"Ping","family":"Lang","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunmao","family":"Yeh","sequence":"additional","affiliation":[{"name":"Beijing Institute of Radio Measurement, Beijing 100854, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhangfeng","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing Institute of Radio Measurement, Beijing 100854, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0154-8986","authenticated-orcid":false,"given":"Dawei","family":"Ren","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2324","DOI":"10.1109\/TGRS.2019.2947634","article-title":"What, Where, and How to Transfer in SAR Target Recognition Based on Deep CNNs","volume":"58","author":"Huang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2174","DOI":"10.1109\/TGRS.2020.3003264","article-title":"FEC: A Feature Fusion Framework for SAR Target Recognition Based on Electromagnetic Scattering Features and Deep CNN Features","volume":"59","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Jiang, W., Wang, Y., Li, Y., Lin, Y., and Shen, W. (2023). Radar target characterization and deep learning in radar automatic target recognition: A review. Remote Sens., 15.","DOI":"10.3390\/rs15153742"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4226","DOI":"10.1109\/TAES.2022.3159295","article-title":"Automatic Target Recognition Based on RCS and Angular Diversity for Multistatic Passive Radar","volume":"58","author":"Cao","year":"2022","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1761","DOI":"10.1109\/TMTT.2022.3218304","article-title":"Angular Resolved RCS and Doppler Analysis of Human Body Parts in Motion","volume":"71","author":"Abadpour","year":"2023","journal-title":"IEEE Trans. Microw. Theory Tech."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"17932","DOI":"10.1109\/JSEN.2022.3194527","article-title":"Comparative Analysis of Radar-Cross-Section- Based UAV Recognition Techniques","volume":"22","author":"Ezuma","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1109\/THMS.2020.3036637","article-title":"Gesture-Radar: A Dual Doppler Radar Based System for Robust Recognition and Quantitative Profiling of Human Gestures","volume":"51","author":"Wang","year":"2021","journal-title":"IEEE Trans. Hum.-Mach. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Xu, X., Feng, C., and Han, L. (2022). Classification of Radar Targets with Micro-Motion Based on RCS Sequences Encoding and Convolutional Neural Network. Remote Sens., 14.","DOI":"10.3390\/rs14225863"},{"key":"ref_9","first-page":"5104415","article-title":"Human Activity Classification Based on Moving Orientation Determining Using Multistatic Micro-Doppler Radar Signals","volume":"60","author":"Qiao","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wang, H., Xing, C., Yin, J., and Yang, J. (2022). Land cover classification for polarimetric SAR images based on vision transformer. Remote Sens., 14.","DOI":"10.3390\/rs14184656"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Li, J., Yu, Z., Yu, L., Cheng, P., Chen, J., and Chi, C. (2023). A comprehensive survey on SAR ATR in deep-learning era. Remote Sens., 15.","DOI":"10.3390\/rs15051454"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"5205814","DOI":"10.1109\/TGRS.2024.3363436","article-title":"Revisiting Local and Global Descriptor-Based Metric Network for Few-Shot SAR Target Classification","volume":"62","author":"Zheng","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4872","DOI":"10.1109\/TAES.2020.3005654","article-title":"Automatic target recognition based on alignments of three-dimensional interferometric ISAR images and CAD models","volume":"56","author":"Cai","year":"2020","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"12132","DOI":"10.1109\/JSTARS.2021.3128938","article-title":"Meta-Learner-Based Stacking Network on Space Target Recognition for ISAR Images","volume":"14","author":"Zhang","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"4008305","DOI":"10.1109\/LGRS.2024.3388427","article-title":"Complex-Valued Multiscale Vision Transformer on Space Target Recognition by ISAR Image Sequence","volume":"21","author":"Yuan","year":"2024","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSP.2010.2088391","article-title":"Radar HRRP Statistical Recognition With Local Factor Analysis by Automatic Bayesian Ying-Yang Harmony Learning","volume":"59","author":"Shi","year":"2011","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1026","DOI":"10.1109\/TAES.2019.2925472","article-title":"Statistical Modeling With Label Constraint for Radar Target Recognition","volume":"56","author":"Du","year":"2020","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.patcog.2018.10.014","article-title":"Convolutional factor analysis model with application to radar automatic target recognition","volume":"87","author":"Chen","year":"2019","journal-title":"Pattern Recognit."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5795","DOI":"10.1109\/TSP.2020.3027470","article-title":"Variational Temporal Deep Generative Model for Radar HRRP Target Recognition","volume":"68","author":"Guo","year":"2020","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1995","DOI":"10.1109\/TSP.2021.3065847","article-title":"Tensor RNN with Bayesian Nonparametric Mixture for Radar HRRP Modeling and Target Recognition","volume":"69","author":"Chen","year":"2021","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1109\/8.233138","article-title":"Using range profiles as feature vectors to identify aerospace objects","volume":"41","author":"Li","year":"1993","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1931","DOI":"10.1109\/TSP.2007.912283","article-title":"Radar HRRP Statistical Recognition: Parametric Model and Model Selection","volume":"56","author":"Du","year":"2008","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3546","DOI":"10.1109\/TSP.2012.2191965","article-title":"Noise Robust Radar HRRP Target Recognition Based on Multitask Factor Analysis with Small Training Data Size","volume":"60","author":"Du","year":"2012","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2359","DOI":"10.1109\/TSP.2005.849161","article-title":"Radar HRRP target recognition based on higher order spectra","volume":"53","author":"Du","year":"2005","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_26","unstructured":"Pan, M., Du, L., Wang, P., Liu, H., and Bao, Z. (2011, January 24\u201327). Multi-task hidden Markov model for radar automatic target recognition. Proceedings of the 2011 IEEE CIE International Conference on Radar, Chengdu, China."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1725","DOI":"10.1049\/el.2016.3060","article-title":"Radar HRRP recognition based on discriminant deep autoencoders with small training data size","volume":"52","author":"Mian","year":"2016","journal-title":"Electron. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.sigpro.2019.01.006","article-title":"Factorized discriminative conditional variational auto-encoder for radar HRRP target recognition","volume":"158","author":"Du","year":"2019","journal-title":"Signal Process."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"29406","DOI":"10.1109\/JSEN.2023.3327552","article-title":"Patch-Wise Autoencoder Based on Transformer for Radar High-Resolution Range Profile Target Recognition","volume":"23","author":"Zhang","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1186\/s13634-019-0603-y","article-title":"Convolutional neural networks for radar HRRP target recognition and rejection","volume":"2019","author":"Wan","year":"2019","journal-title":"Eurasip J. Adv. Signal Process."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Fu, Z., Li, S., Li, X., Dan, B., and Wang, X. (2020). A neural network with convolutional module and residual structure for radar target recognition based on high-resolution range profile. Sensors, 20.","DOI":"10.3390\/s20030586"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"108497","DOI":"10.1016\/j.sigpro.2022.108497","article-title":"Target-attentional CNN for radar automatic target recognition with HRRP","volume":"196","author":"Chen","year":"2022","journal-title":"Signal Process."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1016\/j.sigpro.2018.09.041","article-title":"Target-aware recurrent attentional network for radar HRRP target recognition","volume":"155","author":"Xu","year":"2019","journal-title":"Signal Process."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"7884","DOI":"10.1109\/JSEN.2020.3044314","article-title":"Polarimetric HRRP recognition based on ConvLSTM with self-attention","volume":"21","author":"Zhang","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_35","first-page":"5100814","article-title":"Radar HRRP target recognition model based on a stacked CNN\u2013Bi-RNN with attention mechanism","volume":"60","author":"Pan","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Diao, Y., Liu, S., Gao, X., and Liu, A. (2022, January 17\u201322). Position Embedding-Free Transformer for Radar HRRP Target Recognition. Proceedings of the IGARSS 2022\u20132022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia.","DOI":"10.1109\/IGARSS46834.2022.9883766"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"141679","DOI":"10.1109\/ACCESS.2019.2942425","article-title":"Geometrical Structure Classification of Target HRRP Scattering Centers Based on Dual Polarimetric H\/\u03b1 Features","volume":"7","author":"Long","year":"2019","journal-title":"IEEE Access"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Yang, W., Zhou, Q., Yuan, M., Li, Y., Wang, Y., and Zhang, L. (2023). Dual-band polarimetric HRRP recognition via a brain-inspired multi-channel fusion feature extraction network. Front. Neurosci., 17.","DOI":"10.3389\/fnins.2023.1252179"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1049\/ell2.12225","article-title":"Polarimetric HRRP recognition based on feature-guided Transformer model","volume":"57","author":"Zhang","year":"2021","journal-title":"Electron. Lett."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1109\/LGRS.2015.2511060","article-title":"CFAR Detection of Moving Range-Spread Target in White Gaussian Noise Using Waveform Contrast","volume":"13","author":"Yang","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_41","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2021, January 4). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. Proceedings of the International Conference on Learning Representations, Vienna, Austria."},{"key":"ref_42","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017). Attention is all you need. arXiv."},{"key":"ref_43","first-page":"30392","article-title":"Early convolutions help transformers see better","volume":"34","author":"Xiao","year":"2021","journal-title":"Proc. Adv. Neural Inf. Process. Syst."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Gao, F., Ren, D., Yin, J., and Yang, J. (2024, January 7\u201312). Polarimetric HRRP recognition using vision Transformer with polarimetric preprocessing and attention loss. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium proceedings (IGARSS), Athens, Greece.","DOI":"10.1109\/IGARSS53475.2024.10640945"},{"key":"ref_45","first-page":"108","article-title":"Digital processing of synthetic aperture radar data","volume":"1","author":"Cumming","year":"2005","journal-title":"Artech House"},{"key":"ref_46","unstructured":"Akyildiz, Y., and Moses, R.L. (1999, January 20\u201324). Scattering center model for SAR imagery. Proceedings of the SAR Image Analysis, Modeling, and Techniques II, Florence, Italy."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"3501905","DOI":"10.1109\/LGRS.2023.3246051","article-title":"An efficient radon Fourier transform-based coherent integration method for target detection","volume":"20","author":"Lang","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Lee, J.S., and Pottier, E. (2017). Polarimetric Radar Imaging: From Basics to Applications, CRC Press.","DOI":"10.1201\/9781420054989"},{"key":"ref_49","unstructured":"He, L. (2024, June 10). The Full Derivation of Transformer Gradient. GitHub Repository. Available online: https:\/\/github.com\/Say-Hello2y\/Transformer-attention."},{"key":"ref_50","unstructured":"Potter, L., Nehrbass, J., and Dungan, K. (2009). CVDomes: A Data Set of Simulated X-Band Signatures of Civilian Vehicles, Air Force Research Laboratory."},{"key":"ref_51","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Hinton","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1109\/7.766939","article-title":"Statistical analyses of measured radar ground clutter data","volume":"35","author":"Billingsley","year":"1999","journal-title":"IEEE Trans. Aerosp. Electron. Syst."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/17\/3135\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:42:28Z","timestamp":1760110948000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/17\/3135"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,25]]},"references-count":52,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["rs16173135"],"URL":"https:\/\/doi.org\/10.3390\/rs16173135","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,25]]}}}