{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T03:23:06Z","timestamp":1771039386528,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,6,17]],"date-time":"2022-06-17T00:00:00Z","timestamp":1655424000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["61671354"],"award-info":[{"award-number":["61671354"]}]},{"name":"National Natural Science Foundation of China","award":["61701379"],"award-info":[{"award-number":["61701379"]}]},{"name":"111 Project","award":["61671354"],"award-info":[{"award-number":["61671354"]}]},{"name":"111 Project","award":["61701379"],"award-info":[{"award-number":["61701379"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The detection of ships on the open sea is an important issue for both military and civilian fields. As an active microwave imaging sensor, synthetic aperture radar (SAR) is a useful device in marine supervision. To extract small and weak ships precisely in the marine areas, polarimetric synthetic aperture radar (PolSAR) data have been used more and more widely. We propose a new PolSAR ship detection method which is based on a keypoint detector, referred to as a PolSAR-SIFT keypoint detector, and a patch variation indicator in this paper. The PolSAR-SIFT keypoint detector proposed in this paper is inspired by the SAR-SIFT keypoint detector. We improve the gradient definition in the SAR-SIFT keypoint detector to adapt to the properties of PolSAR data by defining a new gradient based on the distance measurement of polarimetric covariance matrices. We present the application of PolSAR-SIFT keypoint detector to the detection of ship targets in PolSAR data by combining the PolSAR-SIFT keypoint detector with the patch variation indicator we proposed before. The keypoints extracted by the PolSAR-SIFT keypoint detector are usually located in regions with corner structures, which are likely to be ship regions. Then, the patch variation indicator is used to characterize the context information of the extracted keypoints, and the keypoints located on the sea area are filtered out by setting a constant false alarm rate threshold for the patch variation indicator. Finally, a patch centered on each filtered keypoint is selected. Then, the detection statistics in the patch are calculated. The detection statistics are binarized according to the local threshold set by the detection statistic value of the keypoint to complete the ship detection. Experiments on three data sets obtained from the RADARSAT-2 and AIRSAR quad-polarization data demonstrate that the proposed detector is effective for ship detection.<\/jats:p>","DOI":"10.3390\/rs14122900","type":"journal-article","created":{"date-parts":[[2022,6,17]],"date-time":"2022-06-17T11:45:44Z","timestamp":1655466344000},"page":"2900","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["PolSAR Ship Detection Based on a SIFT-like PolSAR Keypoint Detector"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3939-6298","authenticated-orcid":false,"given":"Mingfei","family":"Gu","sequence":"first","affiliation":[{"name":"National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7993-2809","authenticated-orcid":false,"given":"Yinghua","family":"Wang","sequence":"additional","affiliation":[{"name":"National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongwei","family":"Liu","sequence":"additional","affiliation":[{"name":"National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Penghui","family":"Wang","sequence":"additional","affiliation":[{"name":"National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1109\/7.249129","article-title":"Optimal polarimetric processing for enhanced target detection","volume":"29","author":"Novak","year":"1993","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1114","DOI":"10.1109\/LGRS.2012.2231048","article-title":"Dual-polarimetric TerraSAR-X SAR data for target at sea observation","volume":"10","author":"Velotto","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1219","DOI":"10.1109\/JSTARS.2013.2247741","article-title":"A notch filter for ship detection with polarimetric SAR data","volume":"6","author":"Marino","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1109\/JSTARS.2018.2879906","article-title":"Extended geometrical perturbation based detectors for PolSAR image target detection in heterogeneously patched regions","volume":"12","author":"Yang","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_5","first-page":"4001405","article-title":"A new form of the polarimetric notch filter","volume":"19","author":"Liu","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1780","DOI":"10.1109\/LGRS.2015.2425873","article-title":"PolSAR ship detection based on superpixel-level scattering mechanism distribution features","volume":"12","author":"Wang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1725","DOI":"10.1109\/LGRS.2017.2731049","article-title":"PolSAR ship detection using local scattering mechanism difference based on regression kernel","volume":"14","author":"He","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1109\/LGRS.2017.2789204","article-title":"A novel automatic PolSAR ship detection method based on superpixel-level local information measurement","volume":"15","author":"He","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4874","DOI":"10.1109\/TGRS.2020.3022181","article-title":"PolSAR ship detection based on neighborhood polarimetric covariance matrix","volume":"59","author":"Liu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Gu, M., Wang, Y., Liu, H., and Wang, P. (2021, January 15\u201319). PolSAR ship detection via a refined polarimetric notch filter. Proceedings of the CIE International Conference on Radar (RADAR), Haikou, China.","DOI":"10.1109\/Radar53847.2021.10027942"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"103317","DOI":"10.1109\/ACCESS.2020.2999472","article-title":"PolSAR target detection via reflection symmetry and a Wishart classifier","volume":"8","author":"Gu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1109\/7.18677","article-title":"Studies of target detection algorithms that use polarimetric radar data","volume":"25","author":"Novak","year":"1989","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_13","first-page":"5202218","article-title":"The Polarimetric detection optimization filter and its statistical test for ship detection","volume":"60","author":"Liu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"8570","DOI":"10.1109\/TGRS.2019.2921629","article-title":"Novel polarimetric contrast enhancement method based on minimal clutter to signal ratio subspace","volume":"57","author":"Yang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"4756","DOI":"10.1109\/TGRS.2013.2284359","article-title":"Uniform polarimetric matrix rotation theory and its applications","volume":"52","author":"Chen","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Chen, S.W., and Wang, X.S. (2016, January 10\u201315). Polarimetric coherence pattern: A visualization tool for PolSAR data investigation. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7730958"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1109\/LGRS.2020.2976477","article-title":"PolSAR ship detection based on polarimetric correlation pattern","volume":"18","author":"Cui","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Li, H., Cui, X., and Chen, S. (2021). PolSAR Ship detection with optimal polarimetric rotation domain features and SVM. Remote Sens., 13.","DOI":"10.3390\/rs13193932"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3423","DOI":"10.1109\/JSTARS.2019.2925833","article-title":"A saliency detector for polarimetric SAR ship detection using similarity test","volume":"12","author":"Cui","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2891","DOI":"10.1109\/JSTARS.2016.2521709","article-title":"Saliency detector for SAR images based on pattern recurrence","volume":"9","author":"Wang","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Chen, S., Tao, C., Wang, X., and Xiao, S. (2018, January 1\u20134). Polarimetric SAR targets detection and classification with deep convolutional neural network. Proceedings of the 2018 Progress in Electromagnetics Research Symposium (PIERS-Toyama), Toyama, Japan.","DOI":"10.23919\/PIERS.2018.8597856"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Fan, W., Zhou, F., Bai, X., Tao, M., and Tian, T. (2019). Ship detection using deep convolutional neural networks for PolSAR images. Remote Sens., 11.","DOI":"10.3390\/rs11232862"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"6623","DOI":"10.1109\/TGRS.2020.2978268","article-title":"A patch-to-pixel convolutional neural network for small ship detection with PolSAR images","volume":"58","author":"Jin","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","first-page":"4015805","article-title":"Ship detection using PolSAR images based on simulated annealing by fuzzy matching","volume":"19","author":"Zou","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive image features from scale-invariant keypoints","volume":"60","author":"Lowe","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"931","DOI":"10.1109\/LGRS.2016.2554606","article-title":"Unsupervised SAR image change detection based on SIFT keypoints and region information","volume":"13","author":"Wang","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1109\/TGRS.2014.2323552","article-title":"SAR-SIFT: A SIFT-Like algorithm for SAR images","volume":"53","author":"Dellinger","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zou, B., Li, H., Zhang, L., and Cheng, Y. (2019, January 26\u201329). A SAR-SIFT like algorithm for PolSAR image registration. Proceedings of the 2019 6th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR), Xiamen, China.","DOI":"10.1109\/APSAR46974.2019.9048486"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2732","DOI":"10.1109\/TGRS.2010.2041242","article-title":"Three-component model-based decomposition for polarimetric SAR data","volume":"48","author":"An","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","unstructured":"Lee, J., and Pottier, E. (2009). Polarimetric Radar Imaging: From Basics to Applications, CRC Press."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.isprsjprs.2017.11.022","article-title":"Skipping the real world: Classification of PolSAR images without explicit feature extraction","volume":"140","author":"Hellwich","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1628","DOI":"10.1109\/JSTARS.2013.2256881","article-title":"Iterative bilateral filtering of polarimetric SAR data","volume":"6","author":"Guillaso","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1543","DOI":"10.1109\/TSMCB.2009.2020688","article-title":"Kernel bandwidth estimation for nonparametric modeling","volume":"39","author":"Bors","year":"2009","journal-title":"IEEE Trans. Syst. Man Cybern. Part B Cybern."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/S0031-3203(96)00079-9","article-title":"Parametric and non-parametric unsupervised cluster analysis","volume":"30","author":"Roberts","year":"1997","journal-title":"Pattern Recognit."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Tao, Y., Lang, H., and Shi, H. (2018, January 22\u201327). A new scattering similarity based metric for ship detection in Pol-SAR image. Proceedings of the 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8651433"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/12\/2900\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:33:59Z","timestamp":1760139239000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/12\/2900"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,17]]},"references-count":35,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["rs14122900"],"URL":"https:\/\/doi.org\/10.3390\/rs14122900","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,17]]}}}