{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T08:28:34Z","timestamp":1765268914819,"version":"build-2065373602"},"reference-count":38,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,10]],"date-time":"2023-05-10T00:00:00Z","timestamp":1683676800000},"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>With the capability of capturing a target\u2019s two-dimensional information, Inverse Synthetic Aperture Radar (ISAR) imaging is widely used in Radar Automatic Target Recognition. However, changes in the ship target\u2019s attitude can lead to the scatterers\u2019 rotation, occlusion, and angle glint, reducing the accuracy of ISAR image recognition. To solve this problem, we proposed a Triangle Preserving level-set-assisted Triangle-Points Affine Transform Reconstruction (TP-TATR) for ISAR ship target recognition. Firstly, three geometric points as initial information were extracted from the preprocessed ISAR images based on the ship features. Combined with these points, the Triangle Preserving level-set (TP) method robustly extracted the fitting triangle of targets depending on the intrinsic structure of the ship target. Based on the extracted triangle, the TP-TATR adjusted all the ship targets from the training and test data to the same attitude, thereby alleviating the attitude sensitivity. Finally, we created templates by averaging the adjusted training data and matched the test data with the templates for recognition. Experiments based on the simulated and measured data indicate that the accuracies of the TP-TATR method are 87.70% and 90.03%, respectively, which are higher than those of the contrast algorithms and have a statistical difference. These demonstrate the effectiveness and robustness of our proposed TP-TATR method.<\/jats:p>","DOI":"10.3390\/rs15102507","type":"journal-article","created":{"date-parts":[[2023,5,11]],"date-time":"2023-05-11T01:37:24Z","timestamp":1683769044000},"page":"2507","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Automatic ISAR Ship Detection Using Triangle-Points Affine Transform Reconstruction Algorithm"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2412-8415","authenticated-orcid":false,"given":"Xinfei","family":"Jin","sequence":"first","affiliation":[{"name":"School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2065-631X","authenticated-orcid":false,"given":"Fulin","family":"Su","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7578-416X","authenticated-orcid":false,"given":"Hongxu","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9252-7285","authenticated-orcid":false,"given":"Zihan","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Deng","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Marghany, M. (2021). Nonlinear Ocean Dynamics: Synthetic Aperture Radar, Elsevier.","DOI":"10.1016\/B978-0-12-821796-2.00013-6"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"6046","DOI":"10.1002\/2014JC010173","article-title":"Quad-polarization SAR features of ocean currents","volume":"119","author":"Kudryavtsev","year":"2014","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e2020JC016946","DOI":"10.1029\/2020JC016946","article-title":"Retrieval of ocean wave heights from spaceborne SAR in the Arctic Ocean with a neural network","volume":"126","author":"Wu","year":"2021","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.marpolbul.2014.10.041","article-title":"Utilization of a genetic algorithm for the automatic detection of oil spill from RADARSAT-2 SAR satellite data","volume":"89","author":"Marghany","year":"2014","journal-title":"Mar. Pollut. Bull."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4379","DOI":"10.1109\/JSTARS.2019.2949006","article-title":"Ship velocity estimation from ship wakes detected using convolutional neural networks","volume":"12","author":"Kang","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Yang, Q., Deng, B., Qin, Y., and Wang, H. (2019). Estimation of Translational Motion Parameters in Terahertz Interferometric Inverse Synthetic Aperture Radar (InISAR) Imaging Based on a Strong Scattering Centers Fusion Technique. Remote Sens., 11.","DOI":"10.3390\/rs11101221"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"9912","DOI":"10.1109\/TGRS.2019.2930112","article-title":"Robust pol-ISAR target recognition based on ST-MC-DCNN","volume":"57","author":"Bai","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhao, W., Heng, A., Rosenberg, L., Nguyen, S.T., Hamey, L., and Orgun, M. (2022, January 21\u201325). ISAR Ship Classification Using Transfer Learning. Proceedings of the 2022 IEEE Radar Conference (RadarConf22), New York City, NY, USA.","DOI":"10.1109\/RadarConf2248738.2022.9764304"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4256","DOI":"10.1109\/TCYB.2019.2933224","article-title":"Real-world ISAR object recognition using deep multimodal relation learning","volume":"50","author":"Xue","year":"2019","journal-title":"IEEE Trans. Cybern."},{"key":"ref_10","first-page":"1","article-title":"SAISAR-Net: A robust sequential adjustment ISAR image classification network","volume":"60","author":"Xue","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"23432","DOI":"10.1109\/ACCESS.2021.3056671","article-title":"A deformation robust ISAR image satellite target recognition method based on PT-CCNN","volume":"9","author":"Lu","year":"2021","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ni, P., Liu, Y., Pei, H., Du, H., Li, H., and Xu, G. (2022). CLISAR-Net: A Deformation-Robust ISAR Image Classification Network Using Contrastive Learning. Remote Sens., 15.","DOI":"10.3390\/rs15010033"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Karine, A., Toumi, A., Khenchaf, A., and El Hassouni, M. (2018, January 21\u201324). Target recognition in ISAR images based on relative phases of complex wavelet coefficients and sparse classification. Proceedings of the 2018 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), Sousse, Tunisia.","DOI":"10.1109\/ATSIP.2018.8364505"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1611","DOI":"10.1109\/TAP.2005.846780","article-title":"Efficient classification of ISAR images","volume":"53","author":"Kim","year":"2005","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1726","DOI":"10.1109\/TAES.2015.140184","article-title":"Efficient classification of ISAR images using 2D Fourier transform and polar mapping","volume":"51","author":"Park","year":"2015","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"950","DOI":"10.1109\/TAES.2017.2667284","article-title":"Improved classification performance using ISAR images and trace transform","volume":"53","author":"Lee","year":"2017","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Saidi, M.N., Daoudi, K., Khenchaf, A., Hoeltzener, B., and Aboutajdine, D. (2009, January 12\u201317). Automatic target recognition of aircraft models based on ISAR images. Proceedings of the 2009 IEEE International Geoscience and Remote Sensing Symposium, Cape Town, South Africa.","DOI":"10.1109\/IGARSS.2009.5417469"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3412582","DOI":"10.1155\/2020\/3412582","article-title":"A fast recognition method for space targets in ISAR images based on local and global structural fusion features with lower dimensions","volume":"2020","author":"Yang","year":"2020","journal-title":"Int. J. Aerosp. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Toumi, A., and Khenchaf, A. (2016, January 21\u201323). Target recognition using IFFT and MUSIC ISAR images. Proceedings of the 2016 2nd International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), Monastir, Tunisia.","DOI":"10.1109\/ATSIP.2016.7523151"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Bozkurt, H., and Erer, I. (2017, January 19\u201322). Information preserving preprocessing for improved radar target classification accuracy. Proceedings of the 2017 8th International Conference on Recent Advances in Space Technologies (RAST), Istanbul, Turkey.","DOI":"10.1109\/RAST.2017.8003014"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"868","DOI":"10.1109\/TGRS.2010.2060731","article-title":"Cross-range scaling algorithm for ISAR images using 2-D Fourier transform and polar mapping","volume":"49","author":"Park","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Elyounsi, A., Tlijani, H., and Bouhlel, M.S. (2016, January 19\u201321). Shape detection by mathematical morphology techniques for radar target classification. Proceedings of the 2016 17th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA), Sousse, Tunisia.","DOI":"10.1109\/STA.2016.7952078"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Cexus, J.C., Toumi, A., and Riahi, M. (2020, January 2\u20135). Target recognition from ISAR image using polar mapping and shape matrix. Proceedings of the 2020 5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), Sousse, Tunisia.","DOI":"10.1109\/ATSIP49331.2020.9231528"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Sathyendra, H.M., and Stephan, B.D. (2015, January 10\u201315). Data fusion analysis for maritime automatic target recognition with designation confidence metrics. Proceedings of the 2015 IEEE Radar Conference (RadarCon), Arlington, VA, USA.","DOI":"10.1109\/RADAR.2015.7130971"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Kurowska, A., Kulpa, J.S., Giusti, E., and Conti, M. (2017, January 12\u201314). Classification results of ISAR sea targets based on their two features. Proceedings of the 2017 Signal Processing Symposium (SPSympo), Jachranka, Poland.","DOI":"10.1109\/SPS.2017.8053645"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Jarabo-Amores, P., Giusti, E., Rosa-Zurera, M., Bacci, A., Capria, A., and Mata-Moya, D. (2017, January 11\u201313). Target classification using passive radar ISAR imagery. Proceedings of the 2017 European Radar Conference (EURAD), Nuremberg, Germany.","DOI":"10.23919\/EURAD.2017.8249170"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Manno-Kovacs, A., Giusti, E., Berizzi, F., and Kov\u00e1cs, L. (2018, January 23\u201327). Automatic target classification in passive ISAR range-crossrange images. Proceedings of the 2018 IEEE Radar Conference (RadarConf18), Oklahoma City, OK, USA.","DOI":"10.1109\/RADAR.2018.8378558"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Kawahara, T., Toda, S., Mikami, A., and Tanabe, M. (2012, January 7\u201311). Automatic ship recognition robust against aspect angle changes and occlusions. Proceedings of the 2012 IEEE Radar Conference, Atlanta, GA, USA.","DOI":"10.1109\/RADAR.2012.6212258"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"6777","DOI":"10.1049\/joe.2019.0315","article-title":"Robust method for ship recognition based on ISAR imaging using 3D model","volume":"2019","author":"Xie","year":"2019","journal-title":"J. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1109\/83.902291","article-title":"Active contours without edges","volume":"10","author":"Chan","year":"2001","journal-title":"IEEE Trans. Image Process."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Feng, J., Xiao, Y., and Wang, E. (2014, January 13\u201315). Rectangle object segmentation based on shape preserving and CV variational level set. Proceedings of the International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition, Beijing, China.","DOI":"10.1117\/12.2073059"},{"key":"ref_32","unstructured":"Bailey, H., Blackwell, F., Lowery, C., and Ratkovic, J. (1976). Image Correlation: Part 1. Simulation and Analysis, Rand Corp.. Technical Report."},{"key":"ref_33","unstructured":"Wehner, D.R. (1987). High Resolution Radar, Artech House."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Jin, X., and Su, F. (2022, January 5\u20137). Aircraft Recognition Using ISAR Image Based on Quadrangle-points Affine Transform. Proceedings of the 2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Beijing, China.","DOI":"10.1109\/CISP-BMEI56279.2022.9980267"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Xu, D., Bie, B., Sun, G.C., Xing, M., and Pascazio, V. (2020). ISAR Image Matching and Three-Dimensional Scattering Imaging Based on Extracted Dominant Scatterers. Remote Sens., 12.","DOI":"10.3390\/rs12172699"},{"key":"ref_36","unstructured":"Chan, T., and Zhu, W. (2005, January 20\u201325). Level set based shape prior segmentation. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), San Diego, CA, USA."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Davis, J., and Goadrich, M. (2006, January 25\u201329). The relationship between Precision-Recall and ROC curves. Proceedings of the 23rd International Conference on Machine Learning, Pittsburgh, PA, USA.","DOI":"10.1145\/1143844.1143874"},{"key":"ref_38","first-page":"28","article-title":"The generalization of \u2018STUDENT\u2019S\u2019problem when several different population varlances are involved","volume":"34","author":"Welch","year":"1947","journal-title":"Biometrika"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/10\/2507\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:32:15Z","timestamp":1760124735000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/10\/2507"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,10]]},"references-count":38,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["rs15102507"],"URL":"https:\/\/doi.org\/10.3390\/rs15102507","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,5,10]]}}}