{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T16:05:46Z","timestamp":1780589146648,"version":"3.54.1"},"reference-count":44,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,10,23]],"date-time":"2022-10-23T00:00:00Z","timestamp":1666483200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62201004"],"award-info":[{"award-number":["62201004"]}]},{"name":"National Natural Science Foundation of China","award":["KJ2020A0030"],"award-info":[{"award-number":["KJ2020A0030"]}]},{"name":"National Natural Science Foundation of China","award":["2021B497"],"award-info":[{"award-number":["2021B497"]}]},{"name":"National Natural Science Foundation of China","award":["2020A009"],"award-info":[{"award-number":["2020A009"]}]},{"name":"Natural Science Foundation of Education Department of Anhui Province","award":["62201004"],"award-info":[{"award-number":["62201004"]}]},{"name":"Natural Science Foundation of Education Department of Anhui Province","award":["KJ2020A0030"],"award-info":[{"award-number":["KJ2020A0030"]}]},{"name":"Natural Science Foundation of Education Department of Anhui Province","award":["2021B497"],"award-info":[{"award-number":["2021B497"]}]},{"name":"Natural Science Foundation of Education Department of Anhui Province","award":["2020A009"],"award-info":[{"award-number":["2020A009"]}]},{"name":"Postdoctoral Fund of Anhui Province","award":["62201004"],"award-info":[{"award-number":["62201004"]}]},{"name":"Postdoctoral Fund of Anhui Province","award":["KJ2020A0030"],"award-info":[{"award-number":["KJ2020A0030"]}]},{"name":"Postdoctoral Fund of Anhui Province","award":["2021B497"],"award-info":[{"award-number":["2021B497"]}]},{"name":"Postdoctoral Fund of Anhui Province","award":["2020A009"],"award-info":[{"award-number":["2020A009"]}]},{"name":"Opening Foundation Key Laboratory of Intelligent Computing and Signal Processing","award":["62201004"],"award-info":[{"award-number":["62201004"]}]},{"name":"Opening Foundation Key Laboratory of Intelligent Computing and Signal Processing","award":["KJ2020A0030"],"award-info":[{"award-number":["KJ2020A0030"]}]},{"name":"Opening Foundation Key Laboratory of Intelligent Computing and Signal Processing","award":["2021B497"],"award-info":[{"award-number":["2021B497"]}]},{"name":"Opening Foundation Key Laboratory of Intelligent Computing and Signal Processing","award":["2020A009"],"award-info":[{"award-number":["2020A009"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Due to the complexity of sea surface environments, such as speckles and side lobes of ships, ship wake, etc., the detection of ship targets in synthetic aperture radar (SAR) images is still confronted with enormous challenges, especially for small ship targets. Aiming at the key problem of ship target detection in the complex environments, the article proposes a constant false alarm rate (CFAR) algorithm for SAR ship target detection based on the attention contrast mechanism of intensity and texture feature fusion. First of all, the local feature attention contrast enhancement is performed based on the intensity dissimilarity and the texture feature difference described by local binary pattern (LBP) between ship targets and sea clutter, so as to realize the target enhancement and background suppression. Furthermore, the adaptive CFAR ship target detection method based on generalized Gamma distribution (G\u0393D) which can fit the clutter well by the goodness-of-fit analyses is carried out. Finally, the public datasets HRSID and LS-SSDD-v1.0 are used to verify the effectiveness of the proposed detection method. A large number of experimental results show that the proposed method can suppress clutter background and speckle noise and improve the target-to-clutter rate (TCR) significantly, and has the relative high detection rate and low false alarm rate in the complex background and multi-target marine environments.<\/jats:p>","DOI":"10.3390\/s22218116","type":"journal-article","created":{"date-parts":[[2022,10,24]],"date-time":"2022-10-24T10:09:23Z","timestamp":1666606163000},"page":"8116","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Adaptive CFAR Method for SAR Ship Detection Using Intensity and Texture Feature Fusion Attention Contrast Mechanism"],"prefix":"10.3390","volume":"22","author":[{"given":"Nana","family":"Li","sequence":"first","affiliation":[{"name":"Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei 230601, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xueli","family":"Pan","sequence":"additional","affiliation":[{"name":"Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei 230601, China"},{"name":"East China Institute of Photo-Electron ICs, Suzhou 215163, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lixia","family":"Yang","sequence":"additional","affiliation":[{"name":"Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei 230601, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhixiang","family":"Huang","sequence":"additional","affiliation":[{"name":"Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei 230601, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenhua","family":"Wu","sequence":"additional","affiliation":[{"name":"Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei 230601, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guoqing","family":"Zheng","sequence":"additional","affiliation":[{"name":"East China Institute of Photo-Electron ICs, Suzhou 215163, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Allard, Y., Germain, M., and Bonneau, O. (2009). Ship Detection and Characterization Using Polarime SAR Data. Harbour Protection Through Data Fusion Technologies, Springer.","DOI":"10.1007\/978-1-4020-8883-4_29"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Chen, Z. (2009, January 24\u201326). A new type of automatic ship detection method. Proceedings of the 2009 5th International Conference on Wireless Communications, Networking and Mobile Computing, Beijing, China.","DOI":"10.1109\/WICOM.2009.5303174"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1378","DOI":"10.1016\/j.patcog.2006.01.019","article-title":"Fast detecting and locating groups of targets in high-resolution SAR images","volume":"40","author":"Gao","year":"2007","journal-title":"Pattern Recognit."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2039","DOI":"10.1109\/TITS.2019.2911692","article-title":"Outliers-robust CFAR detector of gaussian cutter based on the truncated-maximum-likelihood-estimator in SAR imagery","volume":"21","author":"Ai","year":"2020","journal-title":"IEEE Trans. Intell."},{"key":"ref_5","unstructured":"Song, J., and Qiu, J. (2017, January 25\u201327). Study on statistical characteristics of sea clutter based on measured data with large grazing angle. Proceedings of the International Conference on Computer Systems, Electronics and Control (ICCSEC), Dalian, China."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Mahapatra, D.K., Pradhan, K.R., and Ror, L.P. (2015, January 18\u201320). An experiment on MSTAR data for CFAR detection in lognormal and weibull distributed SAR clutter. Proceedings of the International Conference on Microwave, Optical and Communication Engineering (ICMOCE), Bhubaneswar, India.","DOI":"10.1109\/ICMOCE.2015.7489771"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Bentoumi, A., Mezache, A., and Kerba\u00e2, T.H. (2018, January 28\u201331). Performance of non-parametric CFAR detectors in log-normal and K radar clutter. Proceedings of the International Conference on Electrical Sciences and Technologies in Maghreb (CISTEM), Algiers, Algeria.","DOI":"10.1109\/CISTEM.2018.8613347"},{"key":"ref_8","unstructured":"Song, J., and Xiong, W. (2021, January 24\u201326). CFAR detection of HRRP of sea targets based on K distribution. Proceedings of the International Conference on Information Communication and Signal Processing (ICICSP), Shanghai, China."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1109\/LGRS.2008.915593","article-title":"Using SAR images to detect ships from sea clutter","volume":"5","author":"Liao","year":"2008","journal-title":"IEEE Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1372","DOI":"10.1109\/LGRS.2018.2838043","article-title":"Ship detection in SAR images based on lognormal \u03c1-metric","volume":"15","author":"Yang","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"806","DOI":"10.1109\/LGRS.2012.2224317","article-title":"A CFAR detection algorithm for generalized gamma distributed background in high-resolution SAR images","volume":"10","author":"Qin","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1812","DOI":"10.1109\/TGRS.2016.2634862","article-title":"Scheme of parameter estimation for generalized gamma distribution and its application to ship detection in SAR images","volume":"55","author":"Gao","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Benito-Ortiz, M.-C., Mata-Moya, D., Jarabo-Amores, M.-P., Rey-Maestre, N.d., and Gomez-del-Hoyo, P.-J. (2019, January 2\u20136). Generalized gamma distribution SAR sea clutter modelling for oil spill candicates detection. Proceedings of the European Signal Processing Conference (EUSIPCO), A Coruna, Spain.","DOI":"10.23919\/EUSIPCO.2019.8903047"},{"key":"ref_14","unstructured":"Shao, Z., Ji, W., Qian, C., and Yang, Y. (2021, January 3\u20135). Ship detection for SAR images with sea clutrer modles estimation. Proceedings of the China International SAR Symposium (CISS), Shanghai, China."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Farah, F., Laroussi, T., and Madjidi, H. (2022, January 8\u20139). A fast ship detection algorithm based on automatic censoring for multiple target situations in SAR images. Proceedings of the International Conference on Image and Signal Processing and their Applications (ISPA), Mostaganem, Algeria.","DOI":"10.1109\/ISPA54004.2022.9786336"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1109\/JOE.2017.2768198","article-title":"An adaptively truncated clutter statistics-based two-parameter CFAR detector in SAR imagery","volume":"43","author":"Ai","year":"2018","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ai, J., Yang, X., Dong, Z., Zhou, F., Jia, L., and Hou, L. (2017, January 8\u201312). A new two parameter CFAR ship detector in log-normal clutter. Proceedings of the IEEE Radar Conference (RadarConf), Seattle, WA, USA.","DOI":"10.1109\/RADAR.2017.7944196"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1872","DOI":"10.1109\/TAES.2021.3050654","article-title":"Robust CFAR ship detector based on bilateral-trimmed-statistics of complex ocean scenes in SAR imagery: A Closed-form solution","volume":"57","author":"Ai","year":"2021","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1049\/iet-rsn.2017.0471","article-title":"Ship detection using online update of clutter map based on fuzzy statistics and spatial property","volume":"12","author":"Pan","year":"2018","journal-title":"IET Radar Sonar Navig."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1925","DOI":"10.1109\/LGRS.2016.2618604","article-title":"A modified CFAR algorithm based on object proposals for ship target detection in SAR images","volume":"13","author":"Dai","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1536","DOI":"10.1109\/LGRS.2015.2412174","article-title":"A bilateral CFAR algorithm for ship detection in SAR images","volume":"12","author":"Leng","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"1834","DOI":"10.1109\/LGRS.2019.2913873","article-title":"Fast and automatic ship detection for SAR imagery based on multiscale contrast measure","volume":"16","author":"Wang","year":"2019","journal-title":"IEEE Geosci, Remote Sens. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1109\/LGRS.2016.2633548","article-title":"Ship detection for complex background SAR images based on a multiscale variance weighted image entropy method","volume":"14","author":"Wang","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_25","unstructured":"Sun, K., Li, Y., Li, C., Liang, Y., and Xing, M. (October, January 26). A two-step ship target detection method in high-resolution SAR image based on coarse-to-fine mechanism. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Waikoloa, HI, USA."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Qian, J., Yu, Y., and Bi, F. (2020, January 4\u20136). Multi-scale saliency-based ship detection in SAR images. Proceedings of the IET International Radar Conference (IET IRC 2020), Online Conference.","DOI":"10.1049\/icp.2021.0649"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Li, F., Li, S., Cheng, M., Li, Y., and Liu, Z. (2020, January 4\u20136). Contrast-based SAR ship detection in superpixel level. Proceedings of the IET International Radar Conference (IET IRC 2020), Online Conference.","DOI":"10.1049\/icp.2021.0721"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1590","DOI":"10.1109\/LGRS.2020.3005197","article-title":"A curvature-based saliency method for ship detection in SAR images","volume":"18","author":"Yang","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_29","first-page":"1","article-title":"Unsupervised ship detection for single-channel SAR images based on multiscale saliency and complex signal kurtosis","volume":"19","author":"Wang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1109\/TGRS.2016.2606481","article-title":"New hierarchical saliency filtering for fast ship detection in high-resolution SAR images","volume":"55","author":"Wang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Sun, K., Ma, L., Wang, F., and Liang, Y. (2021, January 3\u20135). Ship detection method based on frequency enhanced MSER for high resolution SAR image. Proceedings of the China International SAR Symposium (CISS), Shanghai, China.","DOI":"10.23919\/CISS51089.2021.9652304"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1109\/LGRS.2018.2873637","article-title":"Superpixel-based LCM detector for faint ships hidden in strong noise background SAR imagery","volume":"16","author":"Wang","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_33","first-page":"206","article-title":"Information geometry method for ship detection in SAR images","volume":"25","author":"Zhang","year":"2020","journal-title":"J. Image Graph."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Chang, Y.L., Anagaw, A., Chang, L., Wang, Y., Hsiao, C.Y., and Lee, W.H. (2019). Ship detection based on YOLOv2 for SAR imagery. Remote Sens., 11.","DOI":"10.3390\/rs11070786"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Tang, G., Liu, S., Fujino, I., Claramunt, C., Wang, Y., and Men, S. (2020). H-YOLO: A single-shot ship detection approach based on region of interest preselected network. Remote Sens., 12.","DOI":"10.3390\/rs12244192"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Tang, G., Zhuge, Y., Claramunt, C., and Men, S. (2021). N-YOLO: A SAR ship detection using noise-classifying and complete-target extraction. Remote Sens., 13.","DOI":"10.3390\/rs13050871"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Xie, F., Lin, B., and Liu, Y. (2022). Research on the coordinate attention mechanism fuse in a YOLOv5 deep learning detector for the SAR ship detection task. Sensors, 22.","DOI":"10.3390\/s22093370"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhu, H., Xie, Y., Huang, H., Jing, C., Rong, Y., and Wang, C. (2021). DB-YOLO: A duplicate bilateral YOLO network for multi-scale ship detection in SAR images. Sensors, 21.","DOI":"10.3390\/s21238146"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Ke, X., Zhang, X., Zhang, T., Shi, J., and Wei, S. (2021, January 11\u201316). SAR ship detection based on an improved faster R-CNN using deformable convolution. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9554697"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Chai, B., Chen, L., Shi, H., and He, C. (2021, January 22\u201324). Marine ship detection method for SAR image based on improved faster RCNN. Proceedings of the SAR in Big Data Era (BIGSARDATA), Nanjing, China.","DOI":"10.1109\/BIGSARDATA53212.2021.9574162"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Kang, M., Leng, X., Lin, Z., and Ji, K. (2017, January 18\u201321). A modified faster R-CNN based on CFAR algorithm for SAR ship detection. Proceedings of the International Workshop on Remote Sensing with Intelligent Processing (RSIP), Shanghai, China.","DOI":"10.1109\/RSIP.2017.7958815"},{"key":"ref_42","unstructured":"Ravi Kumar, Y.B., and Ravi Kumar, C.N. (2016, January 12\u201313). Local binary pattern: An improved LBP to extract nonuniform LBP patterns with Gabor filter to increase the rate of face similarity. Proceedings of the International Conference on Cognitive Computing and Information Processing (CCIP), Mysuru, India."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"120234","DOI":"10.1109\/ACCESS.2020.3005861","article-title":"HRSID: A high-resolution SAR images dataset for ship detection and instance segmentation","volume":"8","author":"Wei","year":"2020","journal-title":"IEEE Access"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zhang, T., Zhang, X., Ke, X., Zhan, X., Shi, J., Wei, S., Pan, D., Li, J., Su, H., and Zhou, Y. (2020). LS-SSDD-v1.0: A deep learning dataset dedicated to small ship detection from large-scale Sentinel-1 SAR Images. Remote Sens., 12.","DOI":"10.3390\/rs12182997"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/21\/8116\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:01:15Z","timestamp":1760144475000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/21\/8116"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,23]]},"references-count":44,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["s22218116"],"URL":"https:\/\/doi.org\/10.3390\/s22218116","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,23]]}}}