{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T18:47:27Z","timestamp":1767034047426,"version":"build-2065373602"},"reference-count":42,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2024,2,12]],"date-time":"2024-02-12T00:00:00Z","timestamp":1707696000000},"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":["62027801"],"award-info":[{"award-number":["62027801"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Research Foundation of the Key Laboratory of Spaceborne Information Intelligent Interpretation","award":["62027801"],"award-info":[{"award-number":["62027801"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In complex environments, the clutter statistical characteristics of synthetic aperture radar (SAR) are inconstant, and the constant detection performance of a false alarm rate (CFAR) detector based on a clutter statistical model is also hard to achieve. As a result, the overestimated threshold leads to a degradation in detection probability. To this end, this paper proposes a SAR ship detector different from CFAR detectors, which is independent of traditional clutter statistical distribution models and the probability of a false alarm (PFA). The proposed detector aims to raise the ship detection probability and alleviate interference from complex environments such as multi-target areas, shores, and breakwaters. It estimates clutter-truncated thresholds based on clutter intensity statistics (CIS). Firstly, three statistical parameters, including the mean, standard deviation, and maximum intensity of background clutter contaminated by outliers, are calculated; secondly, these parameters are utilized to estimate the clutter-truncated threshold using the novel CIS; and finally, the pixel under test is determined according to the CIS detection rule. Compared with CFAR-based algorithms, CIS obtains a high probability of detection in complex environments. As for other aspects, the CIS detector is insensitive to the structure of the detection window, as well as the size. It is also computationally efficient due to its simple calculations. The superiority of the CIS detector is validated on scene-differed SAR images from the DSSDD dataset.<\/jats:p>","DOI":"10.3390\/rs16040664","type":"journal-article","created":{"date-parts":[[2024,2,12]],"date-time":"2024-02-12T08:16:52Z","timestamp":1707725812000},"page":"664","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A New Synthetic Aperture Radar Ship Detector Based on Clutter Intensity Statistics in Complex Environments"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4333-4468","authenticated-orcid":false,"given":"Minqin","family":"Liu","sequence":"first","affiliation":[{"name":"National Key Laboratory of Space Integrated Information System, Institute of Software, Chinese Academy of Sciences, Beijing 100089, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5481-980X","authenticated-orcid":false,"given":"Bo","family":"Zhu","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Space Integrated Information System, Institute of Software, Chinese Academy of Sciences, Beijing 100089, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1785-4024","authenticated-orcid":false,"given":"Hongbing","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing 100084, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"806","DOI":"10.1109\/LGRS.2010.2048697","article-title":"A new CFAR ship detection algorithm based on 2-D joint log-normal distribution in SAR images","volume":"7","author":"Ai","year":"2010","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Lin, C., Tang, S., Zhang, L., and Guo, P. (2018). Focusing High-Resolution Airborne SAR with Topography Variations Using an Extended BPA Based on a Time\/Frequency Rotation Principle. Remote Sens., 10.","DOI":"10.3390\/rs10081275"},{"key":"ref_3","first-page":"511","article-title":"Optimizing the minimum cost flow algorithm for the phase unwrapping process in SAR radar","volume":"62","author":"Dudczyk","year":"2014","journal-title":"Bull. Pol. Acad. Sci. Tech. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Hu, Y., Li, Y., and Pan, Z. (2021). A Dual-Polarimetric SAR Ship Detection Dataset and a Memory-Augmented Autoencoder-Based Detection Method. Sensors, 21.","DOI":"10.3390\/s21248478"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1109\/TAES.2017.2651599","article-title":"CFAR Detection in clutter with a Kronecker covariance structure","volume":"53","author":"Raghavan","year":"2017","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"19401","DOI":"10.1109\/ACCESS.2019.2897358","article-title":"Research on a New Comprehensive CFAR (Comp-CFAR) Processing Method","volume":"7","author":"Liu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_7","first-page":"2165","article-title":"The state-of-the-art in ship detection in synthetic aperture radar imagery","volume":"35","author":"Crisp","year":"2004","journal-title":"Org. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1080\/713821561","article-title":"A Model for non-Rayleigh scattering statistics","volume":"31","author":"Oliver","year":"1984","journal-title":"Opt. Acta Int. J. Opt."},{"key":"ref_9","unstructured":"Gu, X., Fu, K., and Qiu, X. (2017). Basics of SAR Image Interpretation, Science Press."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1685","DOI":"10.1109\/TGRS.2008.2006504","article-title":"An adaptive and fast CFAR algorithm based on automatic censoring for target detection in high-resolution SAR images","volume":"47","author":"Gao","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"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":"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. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"69742","DOI":"10.1109\/ACCESS.2020.2985637","article-title":"Efficient low-cost ship detection for SAR imagery based on simplified U-Net","volume":"8","author":"Mao","year":"2020","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2738","DOI":"10.1109\/JSTARS.2020.2997081","article-title":"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_15","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Chen, H., Zhang, F., Tang, B., Yin, Q., and Sun, X. (2018). Slim and Efficient Neural Network Design for Resource-Constrained SAR Target Recognition. Remote Sens., 10.","DOI":"10.3390\/rs10101618"},{"key":"ref_18","first-page":"4102111","article-title":"Automatic SAR Ship Detection Based on Multifeature Fusion Network in Spatial and Frequency Domains","volume":"61","author":"Wang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Cui, Z., Cao, Z., and Yang, J. (2022, January 17\u201322). Feature-transferable pyramid network for dense multi-scale object detection in SAR images. Proceedings of the IGARSS 2022\u20132022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia.","DOI":"10.1109\/IGARSS46834.2022.9884747"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"8983","DOI":"10.1109\/TGRS.2019.2923988","article-title":"Dense attention pyramid networks for multi-scale ship detection in SAR images","volume":"57","author":"Cui","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Li, L., Lv, M., Jia, Z., and Ma, H. (2023). Sparse Representation-Based Multi-Focus Image Fusion Method via Local Energy in Shearlet Domain. Sensors, 23.","DOI":"10.3390\/s23062888"},{"key":"ref_22","first-page":"414","article-title":"Adaptive detection mode with threshold control as a function of spatially sampled clutter-level estimates","volume":"29","author":"Finn","year":"1968","journal-title":"RCA Rev."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"608","DOI":"10.1109\/TAES.1983.309350","article-title":"Radar CFAR Thresholding in clutter and multiple target situations","volume":"19","author":"Rohling","year":"1983","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1109\/TAES.1980.308885","article-title":"Detectability loss due to \u201cgreatest of\u201d selection in a cell-averaging CFAR","volume":"16","author":"Hansen","year":"1980","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"750","DOI":"10.1109\/TAES.1978.308625","article-title":"Range resolution of targets using automatic detectors","volume":"14","author":"Trunk","year":"1978","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1109\/7.869503","article-title":"Intelligent CFAR processor based on data variability","volume":"36","author":"Smith","year":"2000","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_27","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":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ai, J., Yang, X., Zhou, F., Dong, Z., Jia, L., and Yan, H. (2017). A Correlation-based joint CFAR detector using adaptively-truncated statistics in SAR imagery. Sensors, 17.","DOI":"10.3390\/s17040686"},{"key":"ref_29","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_30","doi-asserted-by":"crossref","first-page":"2376","DOI":"10.1109\/JSTARS.2018.2820078","article-title":"Area ratio invariant feature group for ship detection in SAR imagery","volume":"11","author":"Leng","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1109\/LGRS.2010.2098434","article-title":"On the iterative censoring for target detection in SAR images","volume":"8","author":"Cui","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_32","first-page":"7","article-title":"Improved 2P-CFAR SAR ship detection method","volume":"40","author":"Chang","year":"2021","journal-title":"Foreign Electron. Meas. Technol."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wu, R. (2021). Two-Parameter CFAR Ship Detection Algorithm Based on Rayleigh Distribution in SAR Images. Preprints, 2021120280.","DOI":"10.20944\/preprints202112.0280.v1"},{"key":"ref_34","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_35","doi-asserted-by":"crossref","first-page":"2039","DOI":"10.1109\/TITS.2019.2911692","article-title":"Outliers-Robust CFAR Detector of Gaussian Clutter Based on the Truncated-Maximum-Likelihood-Estimator in SAR Imagery","volume":"21","author":"Ai","year":"2020","journal-title":"IEEE Trans. Intell. Transp."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"7062","DOI":"10.1109\/TGRS.2020.2979449","article-title":"Parameter Estimation for a Compound Radar Clutter Model With Trimodal Discrete Texture","volume":"58","author":"Bocquet","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Sana, S., Ahsan, F., and Khan, S. (2017, January 2). Design and implementation of multi-mode CFAR processor. Proceedings of the 19th International Multi-Topic Conf (INMIC), Islamabad, Pakistan.","DOI":"10.1109\/INMIC.2016.7840109"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3446","DOI":"10.1109\/TGRS.2010.2046330","article-title":"A Novel hierarchical method of ship detection from spaceborne optical image based on shape and texture features","volume":"48","author":"Zhu","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1109\/LGRS.2016.2631638","article-title":"Synthetic aperture radar ship detection using haar-like features","volume":"14","author":"Schwegmann","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"20881","DOI":"10.1109\/ACCESS.2018.2825376","article-title":"A Densely connected end-to-end neural network for multiscale and Multiscene SAR ship detection","volume":"6","author":"Jiao","year":"2018","journal-title":"IEEE Access"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","article-title":"An Introduction to ROC analysis","volume":"27","author":"Fawcett","year":"2006","journal-title":"Pattern Recogn. Lett."},{"key":"ref_42","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"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/4\/664\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:58:58Z","timestamp":1760104738000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/4\/664"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,12]]},"references-count":42,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["rs16040664"],"URL":"https:\/\/doi.org\/10.3390\/rs16040664","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2024,2,12]]}}}