{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T11:11:30Z","timestamp":1780053090567,"version":"3.54.0"},"reference-count":50,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2023,11,21]],"date-time":"2023-11-21T00:00:00Z","timestamp":1700524800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62161044"],"award-info":[{"award-number":["62161044"]}]},{"name":"National Natural Science Foundation of China","award":["11962025"],"award-info":[{"award-number":["11962025"]}]},{"name":"National Natural Science Foundation of China","award":["2021GG0140"],"award-info":[{"award-number":["2021GG0140"]}]},{"name":"National Natural Science Foundation of China","award":["2022ZD05"],"award-info":[{"award-number":["2022ZD05"]}]},{"name":"National Natural Science Foundation of China","award":["2023MS06003"],"award-info":[{"award-number":["2023MS06003"]}]},{"name":"National Natural Science Foundation of China","award":["2023KFYB06"],"award-info":[{"award-number":["2023KFYB06"]}]},{"name":"Science and Technology Project of Inner Mongolia","award":["62161044"],"award-info":[{"award-number":["62161044"]}]},{"name":"Science and Technology Project of Inner Mongolia","award":["11962025"],"award-info":[{"award-number":["11962025"]}]},{"name":"Science and Technology Project of Inner Mongolia","award":["2021GG0140"],"award-info":[{"award-number":["2021GG0140"]}]},{"name":"Science and Technology Project of Inner Mongolia","award":["2022ZD05"],"award-info":[{"award-number":["2022ZD05"]}]},{"name":"Science and Technology Project of Inner Mongolia","award":["2023MS06003"],"award-info":[{"award-number":["2023MS06003"]}]},{"name":"Science and Technology Project of Inner Mongolia","award":["2023KFYB06"],"award-info":[{"award-number":["2023KFYB06"]}]},{"name":"Natural Science Foundation of Inner Mongolia","award":["62161044"],"award-info":[{"award-number":["62161044"]}]},{"name":"Natural Science Foundation of Inner Mongolia","award":["11962025"],"award-info":[{"award-number":["11962025"]}]},{"name":"Natural Science Foundation of Inner Mongolia","award":["2021GG0140"],"award-info":[{"award-number":["2021GG0140"]}]},{"name":"Natural Science Foundation of Inner Mongolia","award":["2022ZD05"],"award-info":[{"award-number":["2022ZD05"]}]},{"name":"Natural Science Foundation of Inner Mongolia","award":["2023MS06003"],"award-info":[{"award-number":["2023MS06003"]}]},{"name":"Natural Science Foundation of Inner Mongolia","award":["2023KFYB06"],"award-info":[{"award-number":["2023KFYB06"]}]},{"name":"Key Laboratory of Infinite-dimensional Hamiltonian System and Its Algorithm Application (IMNU), the Ministry of Education","award":["62161044"],"award-info":[{"award-number":["62161044"]}]},{"name":"Key Laboratory of Infinite-dimensional Hamiltonian System and Its Algorithm Application (IMNU), the Ministry of Education","award":["11962025"],"award-info":[{"award-number":["11962025"]}]},{"name":"Key Laboratory of Infinite-dimensional Hamiltonian System and Its Algorithm Application (IMNU), the Ministry of Education","award":["2021GG0140"],"award-info":[{"award-number":["2021GG0140"]}]},{"name":"Key Laboratory of Infinite-dimensional Hamiltonian System and Its Algorithm Application (IMNU), the Ministry of Education","award":["2022ZD05"],"award-info":[{"award-number":["2022ZD05"]}]},{"name":"Key Laboratory of Infinite-dimensional Hamiltonian System and Its Algorithm Application (IMNU), the Ministry of Education","award":["2023MS06003"],"award-info":[{"award-number":["2023MS06003"]}]},{"name":"Key Laboratory of Infinite-dimensional Hamiltonian System and Its Algorithm Application (IMNU), the Ministry of Education","award":["2023KFYB06"],"award-info":[{"award-number":["2023KFYB06"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Internal Solitary Waves (ISWs) play a pivotal role in transporting energy and matter within the ocean and also pose substantial risks to ocean engineering, navigation, and underwater communication systems. Consequently, measures need to be adopted to alleviate their negative effects and minimize linked risks. An effective method entails extracting ISW positions from Synthetic Aperture Radar (SAR) data for precise trajectory prediction and efficient avoidance strategies. However, manual extraction of ISWs from SAR data is time-consuming and prone to inaccuracies. Hence, it is imperative to develop a high-precision, rapid, and automated ISW-extraction algorithm. In this paper, we introduce Middle Transformer U2-net (MTU2-net), an innovative model that integrates a distinctive loss function and Transformer to improve the accuracy of ISWs\u2019 extraction. The novel loss function enhances the model\u2019s capacity to extract bow waves, whereas the Transformer ensures coherence in ISW\u2019s patterns. By conducting experiments involving 762 image scenes, incorporating ISWs, from the South China Sea, we established a standardized dataset. The Mean Intersection over Union (MIoU) achieved on this dataset was 71.57%, surpassing the performance of other compared methods. The experimental outcomes showcase the remarkable performance of our proposed model in precisely extracting bow wave attributes from SAR data.<\/jats:p>","DOI":"10.3390\/rs15235441","type":"journal-article","created":{"date-parts":[[2023,11,21]],"date-time":"2023-11-21T12:12:13Z","timestamp":1700568733000},"page":"5441","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["MTU2-Net: Extracting Internal Solitary Waves from SAR Images"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0398-1452","authenticated-orcid":false,"given":"Saheya","family":"Barintag","sequence":"first","affiliation":[{"name":"School of Mathematical Sciences, Inner Mongolia Normal University, Huhhot 010028, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-2404-5606","authenticated-orcid":false,"given":"Zhijie","family":"An","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences, Inner Mongolia Normal University, Huhhot 010028, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8639-233X","authenticated-orcid":false,"given":"Qiyu","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences, Inner Mongolia University, Huhhot 010021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xu","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Oceanography and Atmosphere, Ocean University of China, Qingdao 266100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0415-8556","authenticated-orcid":false,"given":"Maoguo","family":"Gong","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tieyong","family":"Zeng","sequence":"additional","affiliation":[{"name":"Department of Mathematics, The Chinese University of Hong Kong, Satin, Hong Kong 999077, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1949","DOI":"10.1002\/lno.11162","article-title":"Internal waves influence the thermal and nutrient environment on a shallow coral reef","volume":"64","author":"Reid","year":"2019","journal-title":"Limnol. Oceanogr."},{"key":"ref_2","unstructured":"Fang, X., and Du, T. (2005). Fundamentals of Oceanic Internal Waves and Internal Waves in the China Seas, China Ocean University Press."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1029\/GL002i004p00128","article-title":"Near-simultaneous observations of intermittent internal waves on the continental shelf from ship and spacecraft","volume":"2","author":"Apel","year":"1975","journal-title":"Geophys. Res. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1007\/s00343-019-9028-6","article-title":"Detection of ocean internal waves based on Faster R-CNN in SAR images","volume":"38","author":"Bao","year":"2020","journal-title":"J. Oceanol. Limnol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"14480","DOI":"10.1080\/10106049.2022.2088860","article-title":"Stripe segmentation of oceanic internal waves in synthetic aperture radar images based on Mask R-CNN","volume":"37","author":"Zheng","year":"2022","journal-title":"Geocarto Int."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3230086","DOI":"10.1109\/LGRS.2022.3230086","article-title":"Oceanic Internal Wave Signature Extraction in the Sulu Sea by a Pixel Attention U-Net: PAU-Net","volume":"20","author":"Ma","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_7","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Yuan, L., Chen, Y., Wang, T., Yu, W., Shi, Y., Jiang, Z.H., Tay, F.E., Feng, J., and Yan, S. (2021, January 11\u201317). Tokens-to-token vit: Training vision Transformers from scratch on imagenet. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00060"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wang, W., Xie, E., Li, X., Fan, D.P., Song, K., Liang, D., Lu, T., Luo, P., and Shao, L. (2021, January 11\u201317). Pyramid vision Transformer: A versatile backbone for dense prediction without convolutions. Proceedings of the IEEE\/CVF international Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00061"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wu, H., Xiao, B., Codella, N., Liu, M., Dai, X., Yuan, L., and Zhang, L. (2021, January 11\u201317). Cvt: Introducing convolutions to vision Transformers. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00009"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Heo, B., Yun, S., Han, D., Chun, S., Choe, J., and Oh, S.J. (2021, January 11\u201317). Rethinking spatial dimensions of vision Transformers. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.01172"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Chen, Y., Gu, X., Liu, Z., and Liang, J. (2022). A fast inference vision Transformer for automatic pavement image classification and its visual interpretation method. Remote Sens., 14.","DOI":"10.3390\/rs14081877"},{"key":"ref_13","first-page":"4006405","article-title":"A Novel Lightweight Attention-Discarding Transformer for High Resolution SAR Image Classification","volume":"20","author":"Liu","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Touvron, H., Cord, M., Sablayrolles, A., Synnaeve, G., and J\u00e9gou, H. (2021, January 11\u201317). Going deeper with image Transformers. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00010"},{"key":"ref_15","unstructured":"Gong, C., Wang, D., Li, M., Chandra, V., and Liu, Q. (2021). Vision Transformers with patch diversification. arXiv."},{"key":"ref_16","unstructured":"Zhou, D., Kang, B., Jin, X., Yang, L., Lian, X., Jiang, Z., Hou, Q., and Feng, J. (2021). Deepvit: Towards deeper vision Transformer. arXiv."},{"key":"ref_17","unstructured":"Zhou, D., Shi, Y., Kang, B., Yu, W., Jiang, Z., Li, Y., Jin, X., Hou, Q., and Feng, J. (2021). Refiner: Refining Self-Attention for vision Transformers. arXiv."},{"key":"ref_18","unstructured":"Chu, X., Tian, Z., Zhang, B., Wang, X., Wei, X., Xia, H., and Shen, C. (2021). Conditional positional encodings for vision Transformers. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wu, K., Peng, H., Chen, M., Fu, J., and Chao, H. (2021, January 11\u201317). Rethinking and improving relative position encoding for vision Transformer. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00988"},{"key":"ref_20","unstructured":"Islam, M.A., Kowal, M., Jia, S., Derpanis, K.G., and Bruce, N.D. (2021). Position, padding and predictions: A deeper look at position information in cnns. arXiv."},{"key":"ref_21","unstructured":"Cordonnier, J.B., Loukas, A., and Jaggi, M. (2019). On the relationship between Self-Attention and convolutional layers. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 11\u201317). Swin Transformer: Hierarchical vision Transformer using shifted windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_23","first-page":"6575","article-title":"Volo: Vision outlooker for visual recognition","volume":"45","author":"Yuan","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_24","first-page":"15908","article-title":"Transformer in Transformer","volume":"34","author":"Han","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_25","first-page":"9355","article-title":"Twins: Revisiting the design of spatial attention in vision Transformers","volume":"34","author":"Chu","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_26","first-page":"3965","article-title":"Coatnet: Marrying convolution and attention for all data sizes","volume":"34","author":"Dai","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_27","first-page":"30392","article-title":"Early convolutions help Transformers see better","volume":"34","author":"Xiao","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhai, X., Kolesnikov, A., Houlsby, N., and Beyer, L. (2022, January 18\u201324). Scaling vision Transformers. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01179"},{"key":"ref_29","first-page":"8583","article-title":"Scaling vision with sparse mixture of experts","volume":"34","author":"Riquelme","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_30","unstructured":"Chen, M., Radford, A., Child, R., Wu, J., Jun, H., Luan, D., and Sutskever, I. (2020, January 13\u201318). Generative pretraining from pixels. Proceedings of the International Conference on Machine Learning, PMLR, Virtual."},{"key":"ref_31","first-page":"13165","article-title":"Mst: Masked self-supervised Transformer for visual representation","volume":"34","author":"Li","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_32","unstructured":"Bao, H., Dong, L., Piao, S., and Wei, F. (2021). Beit: Bert pre-training of image Transformers. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Chen, X., Xie, S., and He, K. (2021, January 11\u201317). An empirical study of training self-supervised vision Transformers. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00950"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Caron, M., Touvron, H., Misra, I., J\u00e9gou, H., Mairal, J., Bojanowski, P., and Joulin, A. (2021, January 11\u201317). Emerging properties in self-supervised vision Transformers. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00951"},{"key":"ref_35","unstructured":"Xie, Z., Lin, Y., Yao, Z., Zhang, Z., Dai, Q., Cao, Y., and Hu, H. (2021). Self-supervised learning with swin Transformers. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1584","DOI":"10.1093\/nsr\/nwaa047","article-title":"Deep-learning-based information mining from ocean remote-sensing imagery","volume":"7","author":"Li","year":"2020","journal-title":"Natl. Sci. Rev."},{"key":"ref_37","first-page":"103","article-title":"A fast internal wave detection method based on PCANet for ocean monitoring","volume":"28","author":"Wang","year":"2019","journal-title":"J. Intell. Syst."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhang, S., Liu, B., Li, X., and Xu, Q. (October, January 26). Automatic extraction of internal wave signature from multiple satellite sensors based on deep convolutional neural networks. Proceedings of the IGARSS 2020\u2014IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA.","DOI":"10.1109\/IGARSS39084.2020.9324489"},{"key":"ref_39","first-page":"8568","article-title":"Stripe segmentation of oceanic internal waves in SAR images based on SegNet","volume":"37","author":"Zheng","year":"2021","journal-title":"Geocarto Int."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1007\/s13131-022-2018-0","article-title":"Remote sensing survey and research on internal solitary waves in the South China Sea-Western Pacific-East Indian Ocean (SCS-WPAC-EIND)","volume":"41","author":"Meng","year":"2022","journal-title":"Acta Oceanol. Sin."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"e2022EA002528","DOI":"10.1029\/2022EA002528","article-title":"An Internal Waves Data Set From Sentinel-1 Synthetic Aperture Radar Imagery and Preliminary Detection","volume":"9","author":"Tao","year":"2022","journal-title":"Earth Space Sci."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Liu, H., and Hu, Q. (October, January 27). Transfuse: Fusing Transformers and cnns for medical image segmentation. Proceedings of the Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2021: 24th International Conference, Strasbourg, France. Proceedings, Part I 24.","DOI":"10.1007\/978-3-030-87193-2_2"},{"key":"ref_43","unstructured":"Dong, B., Wang, W., Fan, D.P., Li, J., Fu, H., and Shao, L. (2021). Polyp-pvt: Polyp segmentation with pyramid vision Transformers. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., and Wang, M. (2022, January 23\u201327). Swin-unet: UNet-like pure Transformer for medical image segmentation. Proceedings of the European Conference on Computer Vision, Tel Aviv, Israel.","DOI":"10.1007\/978-3-031-25066-8_9"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"107404","DOI":"10.1016\/j.patcog.2020.107404","article-title":"U2-Net: Going deeper with nested U-structure for salient object detection","volume":"106","author":"Qin","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1109\/TPAMI.1986.4767851","article-title":"A computational approach to edge detection","volume":"8","author":"Canny","year":"1986","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_47","unstructured":"Mlsna, P.A., and Rodriguez, J.J. (2009). The Essential Guide to Image Processing, Elsevier."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/0165-0270(88)90130-6","article-title":"Edge detection in images using Marr-Hildreth filtering techniques","volume":"26","author":"Smith","year":"1988","journal-title":"J. Neurosci. Methods"},{"key":"ref_49","unstructured":"Gao, W., Zhang, X., Yang, L., and Liu, H. (2010, January 9\u201311). An improved Sobel edge detection. Proceedings of the 2010 3rd International Conference on Computer Science and Information Technology, Chengdu, China."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"046602","DOI":"10.1063\/1.4916881","article-title":"Experiments on the structure and stability of mode-2 internal solitary-like waves propagating on an offset pycnocline","volume":"27","author":"Carr","year":"2015","journal-title":"Phys. Fluids"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/23\/5441\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:26:04Z","timestamp":1760131564000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/23\/5441"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,21]]},"references-count":50,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["rs15235441"],"URL":"https:\/\/doi.org\/10.3390\/rs15235441","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,21]]}}}