{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T00:51:37Z","timestamp":1760230297396,"version":"build-2065373602"},"reference-count":51,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T00:00:00Z","timestamp":1658102400000},"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":["41901302","222300420115"],"award-info":[{"award-number":["41901302","222300420115"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Henan","award":["41901302","222300420115"],"award-info":[{"award-number":["41901302","222300420115"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Synthetic Aperture Radar (SAR) is a high-resolution radar that operates all day and in all weather conditions, so it has been widely used in various fields of science and technology. Ship detection using SAR images has become important research in marine applications. However, in complex scenes, ships are easily submerged in sea clutter, which cause missed detection. Due to this, strong sidelobes in SAR images generate false targets and reduce the detection accuracy. To solve these problems, a ship detection method based on eigensubspace projection (ESSP) in SAR images is proposed. First, the image is reconstructed into a new observation matrix along the azimuth direction, and the phase space matrix of the reconstructed image is constructed by using the Hankel characteristic, which preliminarily determines the approximate position of the ship. Then, the autocorrelation matrix of the reconstructed image is decomposed by eigenvalue decomposition (EVD). According to the size of the eigenvalues, the corresponding eigenvectors are divided into two parts, which constitute the basis of the ship subspace and the clutter subspace. Finally, the original image is projected into the ship subspace, and the ship data in the ship subspace are rearranged to obtain the precise position of the ship with significantly suppressed clutter. To verify the effectiveness of the proposed method, the ESSP method is compared with other detection methods on four images at different sea conditions. The results show that the detection accuracy of the ESSP method reaches 89.87% in complex scenes. Compared with other methods, the proposed method can extract ship targets from sea clutter more accurately and reduce the number of false alarms, which has obvious advantages in terms of detection accuracy and timeliness.<\/jats:p>","DOI":"10.3390\/rs14143441","type":"journal-article","created":{"date-parts":[[2022,7,19]],"date-time":"2022-07-19T00:19:21Z","timestamp":1658189961000},"page":"3441","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Novel Method for SAR Ship Detection Based on Eigensubspace Projection"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7098-7029","authenticated-orcid":false,"given":"Gaofeng","family":"Shu","sequence":"first","affiliation":[{"name":"College of Computer and Information Engineering, Henan University, Kaifeng 475004, China"},{"name":"Henan Engineering Research Center of Intelligent Technology and Application, Henan University, Kaifeng 475004, China"},{"name":"Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng 475004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiahui","family":"Chang","sequence":"additional","affiliation":[{"name":"College of Computer and Information Engineering, Henan University, Kaifeng 475004, China"},{"name":"Henan Engineering Research Center of Intelligent Technology and Application, Henan University, Kaifeng 475004, China"},{"name":"Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng 475004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Lu","sequence":"additional","affiliation":[{"name":"Land Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing 100048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qing","family":"Wang","sequence":"additional","affiliation":[{"name":"Air Force Research Institute, Beijing 100085, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4358-6449","authenticated-orcid":false,"given":"Ning","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer and Information Engineering, Henan University, Kaifeng 475004, China"},{"name":"Henan Engineering Research Center of Intelligent Technology and Application, Henan University, Kaifeng 475004, China"},{"name":"Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng 475004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1010","DOI":"10.1109\/36.508418","article-title":"An Automatic Ship and Ship Wake Detection System for Spaceborne SAR Images in Coastal Regions","volume":"34","author":"Eldhuset","year":"1996","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","unstructured":"Jie, L., Ji, W., Bo, S., Zhu, S., and Luo, W. (2002, January 24\u201328). Research on Ground Based Microwave Signature Measurement Technology for Spaceborne SAR Applications. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Toronto, ON, Canada."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1401","DOI":"10.1109\/LGRS.2020.2999506","article-title":"A Novel False Alarm Suppression Method for CNN-Based SAR Ship Detector","volume":"18","author":"Yang","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_4","first-page":"34","article-title":"Survey of Research Progress on Target Detection and Discrimination of Single-channel SAR Images for Complex Scenes","volume":"9","author":"Du","year":"2020","journal-title":"J. Radars"},{"key":"ref_5","unstructured":"Greenspan, M., Pham, L., and Tardella, N. (1998, January 14). Development and Evaluation of a Real Time SAR ATR System. Proceedings of the IEEE National Conference on Radar, Dallas, TX, USA."},{"key":"ref_6","first-page":"1356","article-title":"Survey of the Study on Ship and Wake Detection in SAR Imagery","volume":"31","author":"Chong","year":"2003","journal-title":"Acta Electron. Sin."},{"key":"ref_7","unstructured":"Li, J., Tian, J., Gao, P., and Li, L. (October, January 26). Ship Detection and Fine-Grained Recognition in Large-Format Remote Sensing Images Based on Convolutional Neural Network. Proceedings of the IEEE International Symposium on Geoscience and Remote Sensing (IGARSS), Waikoloa, HI, USA."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"730","DOI":"10.1109\/LGRS.2016.2540809","article-title":"Superpixel-Based CFAR Target Detection for High-Resolution SAR Images","volume":"13","author":"Yu","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_9","first-page":"499","article-title":"An Improved Bilateral CFAR Ship Detection Algorithm for SAR Image in Complex Environment","volume":"10","author":"Ai","year":"2021","journal-title":"J. Radars"},{"key":"ref_10","first-page":"717","article-title":"Overview of Techniques for Improving High-resolution Spaceborne SAR Imaging and Image Quality","volume":"8","author":"Li","year":"2019","journal-title":"J. Radars"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"730","DOI":"10.1080\/2150704X.2020.1763501","article-title":"An Adaptive-trimming-depth Based CFAR Detector of Heterogeneous Environment in SAR Imagery","volume":"11","author":"Ai","year":"2020","journal-title":"Remote Sens. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1131","DOI":"10.1109\/JSTSP.2018.2874165","article-title":"Adaptive L1-Norm Principal-Component Analysis with Online Outlier Rejection","volume":"12","author":"Markopoulos","year":"2018","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.1109\/TIP.2018.2874289","article-title":"Moving Object Detection in Complex Scene Using Spatiotemporal Structured-Sparse RPCA","volume":"28","author":"Javed","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1109\/LGRS.2017.2777264","article-title":"Low-Rank Plus Sparse Decomposition and Localized Radon Transform for Ship-Wake Detection in Synthetic Aperture Radar Images","volume":"15","author":"Biondi","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_15","first-page":"1","article-title":"Review of Ship Detection in Polarimetric Synthetic Aperture Imagery","volume":"10","author":"Liu","year":"2021","journal-title":"J. Radars"},{"key":"ref_16","first-page":"1","article-title":"Ship Detection Using PolSAR Images Based on Simulated Annealing by Fuzzy Matching","volume":"19","author":"Zou","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Xi, Y., Zhang, X., Lai, Q., Li, W., and Lang, H. (2016, January 10\u201315). A New PolSAR Ship Detection Metric Fused by Polarimetric Similarity and the Third Eigenvalue of the Coherency Matrix. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729019"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3737","DOI":"10.1109\/JSTARS.2019.2923009","article-title":"CFAR Ship Detection Methods Using Compact Polarimetric SAR in a K-Wishart Distribution","volume":"12","author":"Liu","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Fan, Q., Chen, F., Cheng, M., Wang, C., and Li, J. (2018, January 22\u201327). A Modified Framework for Ship Detection from Compact Polarization SAR Image. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518763"},{"key":"ref_20","first-page":"11","article-title":"Performance of a High-Resolution Polarimetric SAR Automatic Target Recognition System","volume":"6","author":"Novak","year":"1992","journal-title":"Linc. Lab. J."},{"key":"ref_21","first-page":"57","article-title":"Survey of Ship Detection Technology Based on Deep Learning","volume":"42","author":"Li","year":"2021","journal-title":"J. Ordnance Equip. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Kang, M., Ji, K., Leng, X., and Lin, Z. (2017). Contextual Region-Based Convolutional Neural Network with Multilayer Fusion for SAR Ship Detection. Remote Sens., 9.","DOI":"10.3390\/rs9080860"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ma, M., Chen, J., Liu, W., and Yang, W. (2018). Ship Classification and Detection Based on CNN Using GF-3 SAR Images. Remote Sens., 10.","DOI":"10.3390\/rs10122043"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"104848","DOI":"10.1109\/ACCESS.2019.2930939","article-title":"A Deep Neural Network Based on an Attention Mechanism for SAR Ship Detection in Multiscale and Complex Scenarios","volume":"7","author":"Chen","year":"2019","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wu, D., Du, X., and Wang, K. (2018, January 27\u201329). An Effective Approach for Underwater Sonar Image Denoising Based on Sparse Representation. Proceedings of the IEEE International Conference on Image, Vision and Computing (ICIVC), Chongqing, China.","DOI":"10.1109\/ICIVC.2018.8492877"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Soganli, A., and Cetin, M. (2015, January 16\u201319). Sparsity-driven SAR Image Reconstruction via Low-rank Sparse Matrix Decomposition. Proceedings of the Signal Processing and Communications Applications Conference, Malatya, Turkey.","DOI":"10.1109\/SIU.2015.7130347"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhang, S., Zhang, Y., Chou, Y., Wang, Z., Shi, Y., and Sun, Z. (2021, January 23\u201326). Analysis of the Multispectral and SAR Image. Proceedings of the IEEE International Conference on Computer and Communication Systems (ICCCS), Chengdu, China.","DOI":"10.1109\/ICCCS52626.2021.9449213"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"215904","DOI":"10.1109\/ACCESS.2020.3041372","article-title":"A Novel Salient Feature Fusion Method for Ship Detection in Synthetic Aperture Radar Images","volume":"8","author":"Zhang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2738","DOI":"10.1109\/JSTARS.2020.2997081","article-title":"Kuang, G. Attention Receptive Pyramid Network for Ship Detection in SAR Images","volume":"13","author":"Zhao","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhang, S., Wu, R., Xu, K., Wang, J., and Sun, W. (2019). R-CNN-Based Ship Detection from High Resolution Remote Sensing Imagery. Remote Sens., 11.","DOI":"10.3390\/rs11060631"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1049\/iet-rsn:20060006","article-title":"Knowledge-Based Recursive Least Squares Techniques for Heterogeneous Clutter Suppression","volume":"1","author":"Miao","year":"2007","journal-title":"IET Radar Sonar Navig."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1109\/LGRS.2018.2884507","article-title":"Sea Clutter Suppression for Shipborne HF Radar Using Cross-Loop\/Monopole Array","volume":"16","author":"Zhao","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1109\/LGRS.2018.2875705","article-title":"Radar Detection of Small Target in Sea Clutter Using Orthogonal Projection","volume":"16","author":"Yang","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_34","first-page":"767","article-title":"Narrow Band Interference Suppression for SAR Using Eigen-Subspace Based Filtering","volume":"27","author":"Zhou","year":"2005","journal-title":"J. Electron. Inf. Technol."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhang, T., Zhang, X., Li, J., Xu, X., Wang, B., Zhan, X., Xu, Y., Ke, X., Zeng, T., and Su, H. (2021). SAR Ship Detection Dataset (SSDD): Official Release and Comprehensive Data Analysis. Remote Sens., 13.","DOI":"10.3390\/rs13183690"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1109\/JSTARS.2017.2755672","article-title":"OpenSARShip: A Dataset Dedicated to Sentinel-1 Ship Interpretation","volume":"11","author":"Huang","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_37","first-page":"852","article-title":"AIR-SARShip-1.0: High-resolution SAR ship detection dataset","volume":"8","author":"Sun","year":"2019","journal-title":"J. Radars"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1109\/TGRS.2015.2451311","article-title":"Robust CFAR Detector Based on Truncated Statistics in Multiple-Target Situations","volume":"54","author":"Tao","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1397","DOI":"10.1109\/LGRS.2018.2838263","article-title":"Superpixel-Level CFAR Detectors for Ship Detection in SAR Imagery","volume":"15","author":"Pappas","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1109\/MSP.2018.2826566","article-title":"Robust Subspace Learning: Robust PCA, Robust Subspace Tracking, and Robust Subspace Recovery","volume":"35","author":"Vaswani","year":"2018","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1970392.1970395","article-title":"Robust Principal Component Analysis","volume":"58","author":"Candes","year":"2011","journal-title":"J. ACM"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1007\/BF01581204","article-title":"On the Douglas-Rachford Splitting Method and the Proximal Point Algorithm for Maximal Monotone Operators","volume":"55","author":"Eckstein","year":"1992","journal-title":"Math. Program."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1109\/TIP.2019.2927458","article-title":"Jointly Using Low-Rank and Sparsity Priors for Sparse Inverse Synthetic Aperture Radar Imaging","volume":"29","author":"Qiu","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"4810","DOI":"10.1109\/TIP.2018.2845123","article-title":"Robust Foreground Estimation via Structured Gaussian Scale Mixture Modeling","volume":"27","author":"Shi","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_45","first-page":"2506","article-title":"Clutter Suppression Method for Short Range Slow Moving Target Detection","volume":"40","author":"Zheng","year":"2018","journal-title":"J. Electron. Inf. Technol."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Nguyen, L.H., Ton, T., Wong, D., and Soumekh, M. (2004, January 2). Adaptive Coherent Suppression of Multiple Wide-bandwidth RFI Sources in SAR. Proceedings of the SPIE\u2014The International Society for Optical Engineering, Orlando, FL, USA.","DOI":"10.1117\/12.542466"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1109\/29.1516","article-title":"Adaptive eigensubspace algorithms for direction or frequency estimation and tracking","volume":"36","author":"Yang","year":"1988","journal-title":"IEEE Trans. Acoust."},{"key":"ref_48","unstructured":"Sun, Y., Zhang, B., Wang, C., and Wu, F. (2012, January 16\u201318). Ship detection Based on Eigenvalue-eigenvector Decomposition and OS-CFAR Detector. Proceedings of the International Conference on Computer Vision in Remote Sensing, Xiamen, China."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Santhanam, A., and Rahman, M. (2006, January 5\u20139). Moving Vehicle Classification Using Eigenspace. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems, Beijing, China.","DOI":"10.1109\/IROS.2006.281792"},{"key":"ref_50","unstructured":"Wu, J., Cao, X., Chen, Y., and Sun, J. (October, January China). Tow Ship Interference Suppression for Towed Array Sonar via Subspace Reconstruction. Proceedings of the International Conference on Wireless Communications and Signal Processing (WCSP), Hangzhou."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Liu, G., Zhang, X., and Meng, J. (2019). A Small Ship Target Detection Method Based on Polarimetric SAR. Remote Sens., 11.","DOI":"10.3390\/rs11242938"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/14\/3441\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:52:44Z","timestamp":1760140364000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/14\/3441"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,18]]},"references-count":51,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["rs14143441"],"URL":"https:\/\/doi.org\/10.3390\/rs14143441","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,7,18]]}}}