{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T06:09:19Z","timestamp":1774937359576,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2018,3,5]],"date-time":"2018-03-05T00:00:00Z","timestamp":1520208000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Major challenges for automatic ship detection in optical remote sensing (ORS) images include cloud, wave, island, wake clutters, and even the high variability of targets. This paper presents a practical ship detection scheme to resolve these existing issues. The scheme contains two main coarse-to-fine stages: prescreening and discrimination. In the prescreening stage, we construct a novel visual saliency detection method according to the difference of statistical characteristics between highly non-uniform regions which allude to regions of interest (ROIs) and homogeneous backgrounds. It can serve as a guide for locating candidate regions. In this way, not only can the targets be precisely detected, but false alarms are also significantly reduced. In the discrimination stage, to get a better representation of the target, both shape and texture features characterizing the ship target are extracted and concatenated as a feature vector for subsequent classification. Moreover, the combined feature is invariant to the rotation. Finally, a trainable Gaussian support vector machine (SVM) classifier is performed to validate real ships out of ship candidates. We demonstrate the superior performance of the proposed hierarchical detection method with detailed comparisons to existing efforts.<\/jats:p>","DOI":"10.3390\/rs10030400","type":"journal-article","created":{"date-parts":[[2018,3,6]],"date-time":"2018-03-06T07:37:25Z","timestamp":1520321845000},"page":"400","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":66,"title":["Ship Detection in Optical Remote Sensing Images Based on Saliency and a Rotation-Invariant Descriptor"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2935-422X","authenticated-orcid":false,"given":"Chao","family":"Dong","sequence":"first","affiliation":[{"name":"Key Laboratory of Airborne Optical Imaging and Measurement, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Jinghong","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Airborne Optical Imaging and Measurement, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"}]},{"given":"Fang","family":"Xu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Airborne Optical Imaging and Measurement, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,3,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"960","DOI":"10.1007\/s10489-011-0307-y","article-title":"A target-based color space for sea target detection","volume":"36","author":"Mirghasemi","year":"2012","journal-title":"Appl. Intell."},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1109\/LGRS.2013.2273552","article-title":"Ship Detection From Optical Satellite Images Based on Sea Surface Analysis","volume":"11","author":"Yang","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1109\/LGRS.2009.2031826","article-title":"Characterization of a Bayesian Ship Detection Method in Optical Satellite Images","volume":"7","author":"Proia","year":"2010","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4511","DOI":"10.1109\/TGRS.2013.2282355","article-title":"Ship Detection in High-Resolution Optical Imagery Based on Anomaly Detector and Local Shape Feature","volume":"52","author":"Shi","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3325","DOI":"10.1109\/TGRS.2014.2374218","article-title":"Object detection in optical remote sensing images based on weakly supervised learning and high-level feature learning","volume":"53","author":"Han","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"5832","DOI":"10.1109\/TGRS.2016.2572736","article-title":"Ship Detection in Spaceborne Optical Image with SVD Networks","volume":"54","author":"Zou","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1174","DOI":"10.1109\/TGRS.2014.2335751","article-title":"Compressed-Domain Ship Detection on Spaceborne Optical Image Using Deep Neural Network and Extreme Learning Machine","volume":"53","author":"Tang","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Lin, H.N., Shi, Z.W., and Zou, Z.X. (2017). Maritime Semantic Labeling of Optical Remote Sensing Images with Multi-Scale Fully Convolutional Network. Remote Sens., 9.","DOI":"10.3390\/rs9050480"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1254","DOI":"10.1109\/34.730558","article-title":"A model of saliency-based visual attention for rapid scene analysis","volume":"20","author":"Itti","year":"1998","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_11","first-page":"545","article-title":"Graph-based visual saliency","volume":"19","author":"Harel","year":"2006","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1109\/LGRS.2011.2180695","article-title":"A Visual Search Inspired Computational Model for Ship Detection in Optical Satellite Images","volume":"9","author":"Bi","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"6446","DOI":"10.1016\/j.eswa.2014.03.033","article-title":"A remote sensing ship recognition method based on dynamic probability generative model","volume":"41","author":"Guo","year":"2014","journal-title":"Expert Syst. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Hou, X.D., and Zhang, L.Q. (2007, January 17\u201322). Saliency Detection: A Spectral Residual Approach. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, USA.","DOI":"10.1109\/CVPR.2007.383267"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1451","DOI":"10.1109\/LGRS.2015.2408355","article-title":"Unsupervised Ship Detection Based on Saliency and S-HOG Descriptor from Optical Satellite Images","volume":"12","author":"Qi","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Xu, F., Liu, J.H., Sun, M.C., Zeng, D.D., and Wang, X. (2017). A Hierarchical Maritime Target Detection Method for Optical Remote Sensing Imagery. Remote Sens., 9.","DOI":"10.3390\/rs9030280"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Achanta, R., Hemami, S., Estrada, F., and Suesstrunk, S. (2009, January 20\u201325). Frequency-Tuned Salient Region Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Miami Beach, FL, USA.","DOI":"10.1109\/CVPR.2009.5206596"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1007\/s11801-017-7014-9","article-title":"Ship detection in optical remote sensing image based on visual saliency and AdaBoost classifier","volume":"13","author":"Wang","year":"2017","journal-title":"Optoelectron. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Takacs, G., Chandrasekhar, V., Tsai, S., Chen, D., Grzeszczuk, R., and Girod, B. (2010, January 13\u201318). Unified Real-Time Tracking and Recognition with Rotation-Invariant Fast Features. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5540116"},{"key":"ref_20","unstructured":"Hong, X.P., Chang, H., Shan, S.G., Chen, X.L., and Gao, W. (2009, January 20\u201325). Sigma Set: A Small Second Order Statistical Region Descriptor. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Miami Beach, FL, USA."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-Vector Networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1219","DOI":"10.1007\/s11760-016-0879-4","article-title":"Adaptive ship detection in SAR images using variance WIE-based method","volume":"10","author":"Wang","year":"2016","journal-title":"Signal Image Video Process."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1186\/1687-6180-2014-45","article-title":"Fast target detection method for high-resolution SAR images based on variance weighted information entropy","volume":"1","author":"Cao","year":"2014","journal-title":"EURASIP J. Adv. Signal Process."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Leng, X.G., Ji, K.F., Zhou, S.L., Xing, X.W., and Zou, H.X. (2016). An Adaptive Ship Detection Scheme for Spaceborne SAR Imagery. Sensors, 16.","DOI":"10.3390\/s16091345"},{"key":"ref_25","unstructured":"Qin, Y., Lu, H.C., Xu, Y.Q., and Wang, H. (2015, January 7\u201312). Saliency Detection via Cellular Automata. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4173","DOI":"10.1109\/TGRS.2012.2189011","article-title":"A Hierarchical Ship Detection Scheme for High-Resolution SAR Images","volume":"50","author":"Wang","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.isprsjprs.2016.03.014","article-title":"A survey on object detection in optical remote sensing images","volume":"117","author":"Cheng","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/0169-7439(87)80084-9","article-title":"Principal Component Analysis","volume":"2","author":"Wold","year":"1987","journal-title":"Chemometr. Intell. Lab. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A threshold selection method from gray-level histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man Cyber."},{"key":"ref_30","unstructured":"Dalal, N., and Triggs, B. (2005, January 20\u201325). Histograms of Oriented Gradients for Human Detection. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1007\/s11263-013-0634-z","article-title":"Rotation-Invariant HOG Descriptors Using Fourier Analysis in Polar and Spherical Coordinates","volume":"106","author":"Liu","year":"2014","journal-title":"Int. J. Comput. Vis."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"7405","DOI":"10.1109\/TGRS.2016.2601622","article-title":"Learning Rotation-Invariant Convolutional Neural Networks for Object Detection in VHR Optical Remote Sensing Images","volume":"54","author":"Cheng","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Tuzel, O., Porikli, F., and Meer, P. (2006, January 7\u201313). Region Covariance: A Fast Descriptor for Detection and Classification. Proceedings of the European Conference on Computer Vision, Graz, Austria.","DOI":"10.1007\/11744047_45"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/3\/400\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T14:57:35Z","timestamp":1760194655000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/3\/400"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,3,5]]},"references-count":33,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2018,3]]}},"alternative-id":["rs10030400"],"URL":"https:\/\/doi.org\/10.3390\/rs10030400","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,3,5]]}}}