{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T06:22:38Z","timestamp":1772086958388,"version":"3.50.1"},"reference-count":25,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2022,8,20]],"date-time":"2022-08-20T00:00:00Z","timestamp":1660953600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the 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"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Due to the complexity of ocean environments, inhomogeneous phenomenon always exist in SAR images of oil spills on the sea surface. In order to address this issue, a universal parameter adaptive Gamma-Log net for detecting oil spills in inhomogeneous SAR images is proposed in this paper. The Gamma-Log net consists of an image feature division module, a correction parameter extraction module, a Gamma-Log correction module and a feature integration module. The normalized input image features are divided into four blocks for correction in the image feature division module. According to the input characteristics, the Gamma-Log correction input parameters are obtained in the correction parameter extraction module. Subsequently, an adaptive method is introduced to adjust the parameters independently by the network to improve efficiency. Then, the input features are corrected in the Gamma-Log correction module by Gamma correction and logarithmic correction. Both correction methods can adjust the gray imbalance in the image and change the overall gray value and contrast. The separated feature blocks are finally reunited together by the feature integration module. In order to avoid information loss, an attention mechanism is added to this module. In the experiments, by adding Gamma-Log Net to multiple semantic segmentation networks, the MIoU and dice indicators increased to some extent, and the HD distance(Hausdorff-95) decreased. Our work demonstrates that the Gamma-Log net can be helpful for oil spill detection in inhomogeneous SAR images.<\/jats:p>","DOI":"10.3390\/rs14164074","type":"journal-article","created":{"date-parts":[[2022,8,22]],"date-time":"2022-08-22T01:56:40Z","timestamp":1661133400000},"page":"4074","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Gamma-Log Net for Oil Spill Detection in Inhomogeneous SAR Images"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0414-5576","authenticated-orcid":false,"given":"Jundong","family":"Liu","sequence":"first","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China"}]},{"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"}]},{"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"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0402-001X","authenticated-orcid":false,"given":"Christos","family":"Grecos","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Arkansas State University, Jonesboro, AR 72401, USA"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3902","DOI":"10.1080\/01431161.2019.1711239","article-title":"Domain adaptation for unsupervised change detection of multisensor multitemporal remote-sensing images","volume":"41","author":"Farahani","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Stan, S., and Rostami, M. (2021, January 2\u20139). Unsupervised Model Adaptation for Continual Semantic Segmentation. Proceedings of the AAAI Conference on Artificial Intelligence, Online.","DOI":"10.1609\/aaai.v35i3.16362"},{"key":"ref_3","first-page":"102769","article-title":"A domain adaptation neural network for change detection with heterogeneous optical and SAR remote sensing images","volume":"109","author":"Zhang","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"638","DOI":"10.1109\/JBHI.2022.3140853","article-title":"Margin Preserving Self-Paced Contrastive Learning Towards Domain Adaptation for Medical Image Segmentation","volume":"26","author":"Liu","year":"2022","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1157","DOI":"10.1080\/01431160512331326558","article-title":"Automatic detection of oil spills from SAR images","volume":"26","author":"Nirchio","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Benito-Ortiz, M.C., de la Mata-Moya, D., Jarabo-Amores, M.P., Maganto-Pascual, M., and del Hoyo, P.G. (2018, January 1\u20133). Multi-resolution Technique-Based Oil Spill Look-Alikes Detection in X-Band SAR Data. Proceedings of the Advances in Intelligent Systems and Computing, Chengdu, China.","DOI":"10.1007\/978-981-13-1165-9_67"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Alattas, R. (July, January 30). Oil spill detection in SAR images using minimum cross-entropy thresholding. Proceedings of the 2014 7th International Congress on Image and Signal Processing, Cherbourg, France.","DOI":"10.1109\/CISP.2014.7003870"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Fan, Y., ping Rui, X., Zhang, G., Yu, T., Xu, X., and Poslad, S. (2021). Feature Merged Network for Oil Spill Detection Using SAR Images. Remote Sens., 13.","DOI":"10.3390\/rs13163174"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Liu, P., Li, Y., Liu, B., Chen, P., and Xu, J. (2019). Semi-Automatic Oil Spill Detection on X-Band Marine Radar Images Using Texture Analysis, Machine Learning, and Adaptive Thresholding. Remote Sens., 11.","DOI":"10.3390\/rs11070756"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Lyu, X. (2018, January 14\u201316). Oil Spill Detection Based on Features and Extreme Learning Machine Method in SAR Images. Proceedings of the 2018 3rd International Conference on Mechanical, Control and Computer Engineering (ICMCCE), Huhhot, China.","DOI":"10.1109\/ICMCCE.2018.00123"},{"key":"ref_11","unstructured":"Yekeen, S.T., and Balogun, A.L.B. (September, January 31). Automated Marine Oil Spill Detection Using Deep Learning Instance Segmentation Model. Proceedings of the ISPRS\u2014International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Online."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Baek, W.K., and Jung, H.S. (2021). Performance Comparison of Oil Spill and Ship Classification from X-Band Dual- and Single-Polarized SAR Image Using Support Vector Machine, Random Forest, and Deep Neural Network. Remote Sens., 13.","DOI":"10.3390\/rs13163203"},{"key":"ref_13","unstructured":"Taravat, A., and Frate, F.D. (September, January 25). Weibull Multiplicative Model And Machine Learning Models for Full-Automatic Dark-Spot Detection from Sar Images. Proceedings of the ISPRS\u2014International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Melbourne, Australia."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ronci, F., Avolio, C., Donna, M.D., Zavagli, M., Piccialli, V., and Costantini, M. (2020, January 21\u201325). An adversarial learning approach for oil spill detection from SAR images. Proceedings of the 2020 IEEE Radar Conference (RadarConf20), Florence, Italy.","DOI":"10.1109\/RadarConf2043947.2020.9266475"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"716","DOI":"10.2112\/JCOASTRES-D-20-00080.1","article-title":"A Dynamic Marine Oil Spill Prediction Model Based on Deep Learning","volume":"37","author":"Wang","year":"2021","journal-title":"J. Coast. Res."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Shaban, M., Salim, R., Khalifeh, H.A., Khelifi, A., Shalaby, A.M., El-Mashad, S.Y., Mahmoud, A.M., Ghazal, M., and El-Baz, A.S. (2021). A Deep-Learning Framework for the Detection of Oil Spills from SAR Data. Sensors, 21.","DOI":"10.3390\/s21072351"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Benito-Ortiz, M.C., de la Mata-Moya, D., Jarabo-Amores, M.P., del Rey-Maestre, N., and del Hoyo, P.G. (2019, January 2\u20136). Generalized Gamma Distribution SAR Sea Clutter Modelling for Oil Spill Candidates Detection. Proceedings of the 2019 27th European Signal Processing Conference (EUSIPCO), A Coruna, Spain.","DOI":"10.23919\/EUSIPCO.2019.8903047"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-Net: Convolutional Networks for Biomedical Image Segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_19","first-page":"3","article-title":"UNet++: A Nested U-Net Architecture for Medical Image Segmentation","volume":"Volume 11045","author":"Zhou","year":"2018","journal-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Proceedings of the 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, 20 September 2018"},{"key":"ref_20","unstructured":"Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M.J., Heinrich, M.P., Misawa, K., Mori, K., McDonagh, S.G., Hammerla, N.Y., and Kainz, B. (2018). Attention U-Net: Learning Where to Look for the Pancreas. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Krestenitis, M., Orfanidis, G.A., Ioannidis, K., Avgerinakis, K., Vrochidis, S., and Kompatsiaris, Y. (2019). Oil Spill Identification from Satellite Images Using Deep Neural Networks. Remote Sens., 11.","DOI":"10.3390\/rs11151762"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Krestenitis, M., Orfanidis, G.A., Ioannidis, K., Avgerinakis, K., Vrochidis, S., and Kompatsiaris, Y. (2019, January 8\u201311). Early Identification of Oil Spills in Satellite Images Using Deep CNNs. Proceedings of the International Conference on Multimedia Modeling, Thessaloniki, Greece.","DOI":"10.3390\/rs11151762"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1016\/S0031-3203(01)00070-X","article-title":"Segmentation of SAR images","volume":"35","author":"Zaart","year":"2002","journal-title":"Pattern Recognit."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Alom, M.Z., Hasan, M., Yakopcic, C., Taha, T.M., and Asari, V.K. (2018). Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation. arXiv.","DOI":"10.1109\/NAECON.2018.8556686"},{"key":"ref_25","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 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/16\/4074\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:12:48Z","timestamp":1760141568000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/16\/4074"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,20]]},"references-count":25,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["rs14164074"],"URL":"https:\/\/doi.org\/10.3390\/rs14164074","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,20]]}}}