{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T08:29:10Z","timestamp":1775896150022,"version":"3.50.1"},"reference-count":30,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2019,12,2]],"date-time":"2019-12-02T00:00:00Z","timestamp":1575244800000},"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":["61801347, 61801344, 61522114, 61471284, 61571349, 61631019, and 61801390"],"award-info":[{"award-number":["61801347, 61801344, 61522114, 61471284, 61571349, 61631019, and 61801390"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2017M613076 and 2016M602775"],"award-info":[{"award-number":["2017M613076 and 2016M602775"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100016307","name":"NSAF","doi-asserted-by":"publisher","award":["U1430123"],"award-info":[{"award-number":["U1430123"]}],"id":[{"id":"10.13039\/501100016307","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["XJS17070, NSIY031403"],"award-info":[{"award-number":["XJS17070, NSIY031403"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Basic Research Plan in Shaanxi Province of China","award":["2018JM6051"],"award-info":[{"award-number":["2018JM6051"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Ship detection plays an important role in many remote sensing applications. However, the performance of the PolSAR ship detection may be degraded by the complicated scattering mechanism, multi-scale size of targets, and random speckle noise, etc. In this paper, we propose a ship detection method for PolSAR images based on modified faster region-based convolutional neural network (Faster R-CNN). The main improvements include proposal generation by adopting multi-level features produced by the convolution layers, which fits ships with different sizes, and the addition of a Deep Convolutional Neural Network (DCNN)-based classifier for training sample generation and coast mitigation. The proposed method has been validated by four measured datasets of NASA\/JPL airborne synthetic aperture radar (AIRSAR) and uninhabited aerial vehicle synthetic aperture radar (UAVSAR). Performance comparison with the modified constant false alarm rate (CFAR) detector and the Faster R-CNN has demonstrated that the proposed method can improve the detection probability while reducing the false alarm rate and missed detections.<\/jats:p>","DOI":"10.3390\/rs11232862","type":"journal-article","created":{"date-parts":[[2019,12,3]],"date-time":"2019-12-03T04:58:39Z","timestamp":1575349119000},"page":"2862","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["Ship Detection Using Deep Convolutional Neural Networks for PolSAR Images"],"prefix":"10.3390","volume":"11","author":[{"given":"Weiwei","family":"Fan","sequence":"first","affiliation":[{"name":"Key Laboratory of Electronic Information Countermeasure and Simulation Technology of Ministry of Education, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Feng","family":"Zhou","sequence":"additional","affiliation":[{"name":"Key Laboratory of Electronic Information Countermeasure and Simulation Technology of Ministry of Education, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Xueru","family":"Bai","sequence":"additional","affiliation":[{"name":"National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0329-7124","authenticated-orcid":false,"given":"Mingliang","family":"Tao","sequence":"additional","affiliation":[{"name":"School of Electronics and Information, Northwestern Polytechnical University, Xi\u2019an 710072, China"}]},{"given":"Tian","family":"Tian","sequence":"additional","affiliation":[{"name":"Key Laboratory of Electronic Information Countermeasure and Simulation Technology of Ministry of Education, Xidian University, Xi\u2019an 710071, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,2]]},"reference":[{"key":"ref_1","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_2","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":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Lin, H., Chen, H., Wang, H., Yin, J., and Yang, J. (2019). Ship Detection for PolSAR Images via Task-Driven Discriminative Dictionary Learning. Remote Sens., 11.","DOI":"10.3390\/rs11070769"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4585","DOI":"10.1109\/TGRS.2013.2282820","article-title":"An improved iterative censoring scheme for CFAR ship detection with SAR imagery","volume":"52","author":"An","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3892","DOI":"10.1109\/JSTARS.2014.2319195","article-title":"AIS-based evaluation of target detectors and SAR sensors characteristics for maritime surveillance","volume":"8","author":"Pelich","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","first-page":"319","article-title":"Ship detection in SAR imagery via variational Bayesian inference","volume":"13","author":"Song","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_7","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_8","doi-asserted-by":"crossref","first-page":"2887","DOI":"10.1109\/TGRS.2015.2506822","article-title":"A segmentation-based CFAR detection algorithm using truncated statistics","volume":"54","author":"Tao","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","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_10","doi-asserted-by":"crossref","unstructured":"Touzi, R. (2000, January 24\u201328). Calibrated polarimetric SAR data for ship detection. Proceedings of the International Geoscience Remote Sensing Symposium, (IGARSS), Honolulu, HI, USA.","DOI":"10.4095\/219697"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2039","DOI":"10.1109\/TGRS.2004.834654","article-title":"On the use of permanent symmetric scatters for ship characterization","volume":"42","author":"Touzi","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1383","DOI":"10.1109\/JSTARS.2013.2269996","article-title":"A new automatic ship detection method using L-band polarimetric SAR imagery","volume":"7","author":"Wei","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_13","unstructured":"Simonyan, K., and Zisserman, A. (2015, January 7\u20139). Very deep convolutional networks for large-scale image recognition. Proceedings of the 2015 International Conference Learning Representations (ICLR), New York, NY, USA."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 11\u201318). Fast r-cnn. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1109\/TGRS.2016.2612821","article-title":"Convolutional neural networks for large-scale remote-sensing image classification","volume":"55","author":"Maggiori","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kang, M., Leng, X., Lin, Z., and Ji, K. (2017, January 19\u201321). A modified faster R-CNN based on CFAR algorithm for SAR ship detection. Proceedings of the 2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP), Shanghai, China.","DOI":"10.1109\/RSIP.2017.7958815"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"751","DOI":"10.1109\/LGRS.2018.2882551","article-title":"Squeeze and Excitation Rank Faster R-CNN for Ship Detection in SAR Images","volume":"16","author":"Lin","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_20","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_21","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_22","doi-asserted-by":"crossref","unstructured":"Fan, Q., Chen, F., Cheng, M., Lou, S., Xiao, R., Zhang, B., Wang, C., and Li, J. (2019). Ship detection using a fully convolutional network with compact polarimetric sar images. Remote Sens., 11.","DOI":"10.3390\/rs11182171"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Cao, C., Zhang, J., Meng, J., Zhang, X., and Mao, X. (2019). Analysis of ship detection performance with full-compact-and dul-polarimetric sar. Remote Sens., 11.","DOI":"10.3390\/rs11182160"},{"key":"ref_24","unstructured":"Christian, S., Vincent, V., Sergey, L., Jon, S., and Zbigniew, W. (July, January 26). Rethinking the inception architecture for computer vision. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA."},{"key":"ref_25","unstructured":"Ken, C., Karen, S., Andrea, V., and Andrew, Z. (2014, January 1\u20135). Return of the devil in the details: Delving deep into convolutional nets. Proceedings of the British Machine Vision Conference (BMVC), Nottingham, UK."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., and Darrell, T. (2014, January 3\u20137). Caffe: Convolutional architecture for fast feature embedding. Proceedings of the 2014 ACM Conference on Multimedia, Orlando, FL, USA.","DOI":"10.1145\/2647868.2654889"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., and Maaten, L. (2017, January 21\u201326). Densely Connected Convolutional Networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_29","unstructured":"Lee, S., and Pottier, E. (2009). Polarimetric Imaging: From Basics to Applications, CRC Press."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1109\/LGRS.2010.2041322","article-title":"Ship detection using TerraSAR-X images in the campos basin (Brazil)","volume":"7","author":"Paes","year":"2010","journal-title":"IEEE Geosci. Remote Sens. Lett."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/23\/2862\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:39:18Z","timestamp":1760189958000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/23\/2862"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,2]]},"references-count":30,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2019,12]]}},"alternative-id":["rs11232862"],"URL":"https:\/\/doi.org\/10.3390\/rs11232862","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,2]]}}}