{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,5]],"date-time":"2026-09-05T05:48:46Z","timestamp":1788587326573,"version":"build-2803163510"},"reference-count":47,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,6,18]],"date-time":"2021-06-18T00:00:00Z","timestamp":1623974400000},"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":["61971444"],"award-info":[{"award-number":["61971444"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2019MF019"],"award-info":[{"award-number":["ZR2019MF019"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Graduate Innovation Project of China University of Petroleum (East China)","award":["YCX2020099"],"award-info":[{"award-number":["YCX2020099"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>We investigate the problem of training an oil spill detection model with small data. Most existing machine-learning-based oil spill detection models rely heavily on big training data. However, big amounts of oil spill observation data are difficult to access in practice. To address this limitation, we developed a multiscale conditional adversarial network (MCAN) consisting of a series of adversarial networks at multiple scales. The adversarial network at each scale consists of a generator and a discriminator. The generator aims at producing an oil spill detection map as authentically as possible. The discriminator tries its best to distinguish the generated detection map from the reference data. The training procedure of MCAN commences at the coarsest scale and operates in a coarse-to-fine fashion. The multiscale architecture comprehensively captures both global and local oil spill characteristics, and the adversarial training enhances the model\u2019s representational power via the generated data. These properties empower the MCAN with the capability of learning with small oil spill observation data. Empirical evaluations validate that our MCAN trained with four oil spill observation images accurately detects oil spills in new images.<\/jats:p>","DOI":"10.3390\/rs13122378","type":"journal-article","created":{"date-parts":[[2021,6,18]],"date-time":"2021-06-18T11:19:20Z","timestamp":1624015160000},"page":"2378","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":50,"title":["Oil Spill Detection with Multiscale Conditional Adversarial Networks with Small-Data Training"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1507-1429","authenticated-orcid":false,"given":"Yongqing","family":"Li","sequence":"first","affiliation":[{"name":"College of Control Science and Engineering, China University of Petroleum (East China), Qingdao 266580, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7695-1150","authenticated-orcid":false,"given":"Xinrong","family":"Lyu","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8002-5341","authenticated-orcid":false,"given":"Alejandro C.","family":"Frery","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Victoria University of Wellington, Wellington 6140, New Zealand"},{"name":"Key Lab of Intelligent Perception and Image Understanding of the Ministry of Education, Xidian University, Xi\u2019an 710126, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3949-985X","authenticated-orcid":false,"given":"Peng","family":"Ren","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Fingas, M., and Brown, C.E. (2018). A review of oil spill remote sensing. Sensors, 18.","DOI":"10.3390\/s18010091"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Yue, Z., Gao, F., Xiong, Q., Wang, J., Huang, T., Yang, E., and Zhou, H. (2019). A novel semi-supervised convolutional neural network method for synthetic aperture radar image recognition. Cogn. Comput., 1\u201312.","DOI":"10.1007\/s12559-019-09639-x"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1000","DOI":"10.1109\/ACCESS.2017.2777444","article-title":"Visual Saliency Modeling for River Detection in High-Resolution SAR Imagery","volume":"6","author":"Gao","year":"2017","journal-title":"IEEE Access"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"746","DOI":"10.1109\/TGRS.2006.887019","article-title":"Oil spill detection in Radarsat and Envisat SAR images","volume":"45","author":"Solberg","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2810","DOI":"10.1109\/JSTARS.2017.2671403","article-title":"An image-segmentation-based framework to detect oil slicks from moving vessels in the Southern African oceans using SAR imagery","volume":"10","author":"Mdakane","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1080\/2150704X.2019.1587197","article-title":"Oil spill extraction by X-band marine radar using texture analysis and adaptive thresholding","volume":"10","author":"Liu","year":"2019","journal-title":"Remote Sens. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1080\/2150704X.2021.1892852","article-title":"Oil spill extraction from X-band marine radar images by power fitting of radar echoes","volume":"12","author":"Liu","year":"2021","journal-title":"Remote Sens. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.marpolbul.2014.10.041","article-title":"Utilization of a genetic algorithm for the automatic detection of oil spill from RADARSAT-2 SAR satellite data","volume":"89","author":"Marghany","year":"2014","journal-title":"Mar. Pollut. Bull."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2389","DOI":"10.1109\/JSTARS.2018.2827996","article-title":"Oil Spill Detection in Synthetic Aperture Radar Images Using Lipschitz-Regularity and Multiscale Techniques","volume":"11","author":"Ajadi","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"5862","DOI":"10.1109\/TGRS.2016.2574561","article-title":"Polarimetric analysis of compact-polarimetry SAR architectures for sea oil slick observation","volume":"54","author":"Buono","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4190","DOI":"10.1109\/TGRS.2017.2690001","article-title":"Analysis of evolving oil spills in full-polarimetric and hybrid-polarity SAR","volume":"55","author":"Espeseth","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2046","DOI":"10.1109\/TPAMI.2011.274","article-title":"SAR Image Segmentation Based on Level Set Approach GA0 Model","volume":"34","author":"Marques","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1109\/LGRS.2015.2497716","article-title":"Dual smoothing for marine oil spill segmentation","volume":"13","author":"Ren","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1102","DOI":"10.1109\/JOE.2018.2842538","article-title":"Energy minimization with one dot fuzzy initialization for marine oil spill segmentation","volume":"44","author":"Ren","year":"2018","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1858","DOI":"10.1109\/JSTARS.2018.2833485","article-title":"Segmenting Oil Spills from Blurry Images Based on Alternating Direction Method of Multipliers","volume":"11","author":"Chen","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Guo, H., Wu, D., and An, J. (2017). Discrimination of oil slicks and lookalikes in polarimetric SAR images using CNN. Sensors, 17.","DOI":"10.3390\/s17081837"},{"key":"ref_17","first-page":"146","article-title":"Oil spill detection using synthetic aperture radar images and feature selection in shape space","volume":"30","author":"Guo","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.cageo.2014.07.015","article-title":"Automatic decision support system based on SAR data for oil spill detection","volume":"72","author":"Mera","year":"2014","journal-title":"Comput. Geosci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.marpolbul.2013.05.022","article-title":"Automatic Synthetic Aperture Radar based oil spill detection and performance estimation via a semi-automatic operational service benchmark","volume":"73","author":"Singha","year":"2013","journal-title":"Mar. Pollut. Bull."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2517","DOI":"10.1109\/JSTARS.2013.2244061","article-title":"Oil Spill Mapping and Measurement in the Gulf of Mexico With Textural Classifier Neural Network Algorithm (TCNNA)","volume":"6","author":"MacDonald","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2282","DOI":"10.1109\/36.868885","article-title":"Neural networks for oil spill detection using ERS-SAR data","volume":"38","author":"Petrocchi","year":"2000","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1109\/LGRS.2007.907174","article-title":"Classifiers and confidence estimation for oil spill detection in Envisat ASAR images","volume":"5","author":"Brekke","year":"2008","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2355","DOI":"10.1109\/JSTARS.2013.2251864","article-title":"Satellite Oil Spill Detection Using Artificial Neural Networks","volume":"6","author":"Singha","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.rse.2013.10.012","article-title":"A comparative study of different classification techniques for marine oil spill identification using RADARSAT-1 imagery","volume":"141","author":"Xu","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2427","DOI":"10.1109\/TGRS.2013.2261076","article-title":"Fully Automatic Dark-Spot Detection From SAR Imagery With the Combination of Nonadaptive Weibull Multiplicative Model and Pulse-Coupled Neural Networks","volume":"52","author":"Taravat","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1007\/s12559-018-9563-z","article-title":"A New Algorithm of SAR Image Target Recognition Based on Improved Deep Convolutional Neural Network","volume":"11","author":"Gao","year":"2019","journal-title":"Cogn. Comput."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Al-Ruzouq, R., Gibril, M.B.A., Shanableh, A., Kais, A., Hamed, O., Al-Mansoori, S., and Khalil, M.A. (2020). Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review. Remote Sens., 12.","DOI":"10.3390\/rs12203338"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Krestenitis, M., Orfanidis, G., Ioannidis, K., Avgerinakis, K., Vrochidis, S., and Kompatsiaris, I. (2019). Oil Spill Identification from Satellite Images Using Deep Neural Networks. Remote Sens., 11.","DOI":"10.3390\/rs11151762"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Gallego, A.J., Gil, P., Pertusa, A., and Fisher, R.B. (2019). Semantic Segmentation of SLAR Imagery with Convolutional LSTM Selectional AutoEncoders. Remote Sens., 11.","DOI":"10.3390\/rs11121402"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Gallego, A.J., Gil, P., Pertusa, A., and Fisher, R.B. (2018). Segmentation of oil spills on side-looking airborne radar imagery with autoencoders. Sensors, 18.","DOI":"10.3390\/s18030797"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5217","DOI":"10.1109\/TGRS.2018.2812619","article-title":"Two-stage convolutional neural network for ship and spill detection using SLAR images","volume":"56","author":"Gallego","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Shaban, M., Salim, R., Abu Khalifeh, H., Khelifi, A., Shalaby, A., El-Mashad, S., Mahmoud, A., Ghazal, M., and El-Baz, A. (2021). A Deep-Learning Framework for the Detection of Oil Spills from SAR Data. Sensors, 21.","DOI":"10.3390\/s21072351"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Li, Y., Yang, X., Ye, Y., Cui, L., Jia, B., Jiang, Z., and Wang, S. (2018). Detection of Oil Spill Through Fully Convolutional Network. Geo-Spatial Knowledge and Intelligence, Springer.","DOI":"10.1007\/978-981-13-0893-2_38"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully Convolutional Networks for Semantic Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_35","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 Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_37","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2021, June 16). Generative Adversarial Networks. Available online: https:\/\/arxiv.org\/abs\/1406.2661."},{"key":"ref_38","unstructured":"Arjovsky, M., Chintala, S., and Bottou, L. (2021, June 16). Wasserstein GAN. Available online: https:\/\/arxiv.org\/abs\/1701.07875."},{"key":"ref_39","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A.C. (2021, June 16). Improved Training of Wasserstein Gans. Available online: https:\/\/arxiv.org\/abs\/1704.00028."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Shaham, T.R., Dekel, T., and Michaeli, T. (2019, January 27\u201328). Singan: Learning a generative model from a single natural image. Proceedings of the IEEE International Conference on Computer Vision, Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00467"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Jiang, Z., Ma, Y., and Yang, J. (2020). Inversion of the Thickness of Crude Oil Film Based on an OG-CNN Model. J. Mar. Sci. Eng., 8.","DOI":"10.3390\/jmse8090653"},{"key":"ref_42","unstructured":"Mirza, M., and Osindero, S. (2014). Conditional Generative Adversarial Nets. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., and Efros, A.A. (2017, January 21\u201326). Image-to-image translation with conditional adversarial networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"4973","DOI":"10.1109\/TGRS.2018.2803038","article-title":"Oil Spill Segmentation via Adversarial f-Divergence Learning","volume":"56","author":"Yu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1080\/2151237X.2007.10129236","article-title":"Adaptive Thresholding using the Integral Image","volume":"12","author":"Bradley","year":"2007","journal-title":"J. Graph. Tools"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2007","DOI":"10.1109\/TIP.2011.2146190","article-title":"A Level Set Method for Image Segmentation in the Presence of Intensity Inhomogeneities with Application to MRI","volume":"20","author":"Li","year":"2011","journal-title":"IEEE Trans. Image Process."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"4988","DOI":"10.1109\/JSTARS.2020.3019418","article-title":"A Badging System for Reproducibility and Replicability in Remote Sensing Research","volume":"13","author":"Frery","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/12\/2378\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:18:14Z","timestamp":1760163494000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/12\/2378"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,18]]},"references-count":47,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2021,6]]}},"alternative-id":["rs13122378"],"URL":"https:\/\/doi.org\/10.3390\/rs13122378","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,18]]}}}