{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T23:59:09Z","timestamp":1768694349616,"version":"3.49.0"},"reference-count":42,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2020,12,21]],"date-time":"2020-12-21T00:00:00Z","timestamp":1608508800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Big data on Earth in Support of Ocean Sustainable Development Goals Research","award":["[XDA19090123]"],"award-info":[{"award-number":["[XDA19090123]"]}]},{"name":"Nation Key R&amp;D Program of China under Grant","award":["[2017YFC0821900]"],"award-info":[{"award-number":["[2017YFC0821900]"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Marine raft aquaculture (MFA) plays an important role in the marine economy and ecosystem. With the characteristics of covering a large area and being sparsely distributed in sea area, MFA monitoring suffers from the low efficiency of field survey and poor data of optical satellite imagery. Synthetic aperture radar (SAR) satellite imagery is currently considered to be an effective data source, while the state-of-the-art methods require manual parameter tuning under the guidance of professional experience. To preclude the limitation, this paper proposes a segmentation network combined with nonsubsampled contourlet transform (NSCT) to extract MFA areas using Sentinel-1 images. The proposed method is highlighted by several improvements based on the feature analysis of MFA. First, the NSCT was applied to enhance the contour and orientation features. Second, multiscale and asymmetric convolutions were introduced to fit the multisize and strip-like features more effectively. Third, both channel and spatial attention modules were adopted in the network architecture to overcome the problems of boundary fuzziness and area incompleteness. Experiments showed that the method can effectively extract marine raft culture areas. Although further research is needed to overcome the problem of interference caused by excessive waves, this paper provides a promising approach for periodical monitoring MFA in a large area with high efficiency and acceptable accuracy.<\/jats:p>","DOI":"10.3390\/rs12244182","type":"journal-article","created":{"date-parts":[[2020,12,21]],"date-time":"2020-12-21T09:41:41Z","timestamp":1608543701000},"page":"4182","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Combining Segmentation Network and Nonsubsampled Contourlet Transform for Automatic Marine Raft Aquaculture Area Extraction from Sentinel-1 Images"],"prefix":"10.3390","volume":"12","author":[{"given":"Yi","family":"Zhang","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Chengyi","family":"Wang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]},{"given":"Yuan","family":"Ji","sequence":"additional","affiliation":[{"name":"People\u2019s Liberation Army 91039 Troop, Beijing 102401, China"}]},{"given":"Jingbo","family":"Chen","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]},{"given":"Yupeng","family":"Deng","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Jing","family":"Chen","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Yongshi","family":"Jie","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,21]]},"reference":[{"key":"ref_1","unstructured":"FAO (2018). World Fisheries and Aquaculture Overview 2018, FAO Fisheries Department."},{"key":"ref_2","first-page":"825","article-title":"Ulva prolifera green-tide outbreaks and their environmental impact in the Yellow Sea, China","volume":"6","author":"Zhang","year":"2019","journal-title":"Neurosurgery"},{"key":"ref_3","unstructured":"Chen, X., and Fan, W. (2014). Theory and Technology of Fishery Remote Sensing Applications, Science China Press."},{"key":"ref_4","first-page":"164","article-title":"Evolution and development trend of marine raft cultivation model in China","volume":"31","author":"Deng","year":"2013","journal-title":"Chin. Fish. Econ."},{"key":"ref_5","first-page":"1","article-title":"Marine Floating Raft Aquaculture Detection of GF-3 PolSAR Images Based on Collective Multikernel Fuzzy Clustering","volume":"99","author":"Fan","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_6","unstructured":"Fan, J., Zhang, F., Zhao, D., Wen, S., and Wei, B. (2014, January 29). Information extraction of marine raft aquaculture based on high-resolution satellite remote sensing SAR images. Proceedings of the Second China Coastal Disaster Risk Analysis and Management Symposium, Hainan, China."},{"key":"ref_7","first-page":"1","article-title":"Review of SAR Oceanic Remote Sensing Technology","volume":"2","author":"Zhu","year":"2010","journal-title":"Mod. Radar"},{"key":"ref_8","first-page":"35","article-title":"Monitormethod of rafts cultivation by remote sense-A case of Changhai","volume":"27","author":"Chu","year":"2008","journal-title":"Mar. Environ. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Fan, J., Chu, J., Geng, J., and Zhang, F. (2015, January 26\u201331). Floating raft aquaculture information automatic extraction based on high resolution SAR images. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326676"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Hu, Y., Fan, J., and Wang, J. (2017, January 16\u201319). Target recognition of floating raft aquaculture in SAR image based on statistical region merging. Proceedings of the 2017 Seventh International Conference on Information Science and Technology (ICIST), Da Nang, Vietnam.","DOI":"10.1109\/ICIST.2017.7926798"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1109\/LGRS.2017.2648641","article-title":"Weighted fusion-based representation classifiers for marine floating raft detection of SAR images","volume":"14","author":"Geng","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_12","first-page":"593","article-title":"Research on marine floating raft aquaculture SAR image target recognition based on deep collaborative sparse coding network","volume":"42","author":"Geng","year":"2016","journal-title":"Acta Autom. Sin."},{"key":"ref_13","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 (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_14","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_15","unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A.L. (2014). Semantic image segmentation with deep convolutional nets and fully connected crfs. arXiv."},{"key":"ref_16","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","volume":"40","author":"Chen","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Lin, G., Milan, A., Shen, C., and Reid, I. (2017, January 21\u201326). Refinenet: Multi-path refinement networks for high-resolution semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.549"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid scene parsing network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Liu, Y., Cheng, M.M., Hu, X., Wang, K., and Bai, X. (2017, January 21\u201326). Richer convolutional features for edge detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.622"},{"key":"ref_21","first-page":"119","article-title":"Extracting raft aquaculture areas in Sanduao from high-resolution remote sensing images using RCF","volume":"41","author":"Yueming","year":"2019","journal-title":"Acta Oceanol. Sin."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Shi, T., Xu, Q., Zou, Z., and Shi, Z. (2018). Automatic raft labeling for remote sensing images via dual-scale homogeneous convolutional neural network. Remote Sens., 10.","DOI":"10.3390\/rs10071130"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Cui, B., Fei, D., Shao, G., Lu, Y., and Chu, J. (2019). Extracting raft aquaculture areas from remote sensing images via an improved U-net with a PSE structure. Remote Sens., 11.","DOI":"10.3390\/rs11172053"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3089","DOI":"10.1109\/TIP.2006.877507","article-title":"The nonsubsampled contourlet transform: Theory, design, and applications","volume":"15","author":"Zhou","year":"2006","journal-title":"IEEE Trans. Image Process."},{"key":"ref_25","first-page":"456","article-title":"National sea area use dynamic monitoring based on GF-3 SAR imagery","volume":"6","author":"Fan","year":"2017","journal-title":"J. Radars"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1109\/TAES.1987.310891","article-title":"Spectral properties of homogeneous and nonhomogeneous radar images","volume":"4","author":"Madsen","year":"1987","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_27","unstructured":"Geng, J. (2018). Research on Deep Learning Based Classification Method for SAR Remote Sensing Images. [Ph.D. Thesis, Dalian University of Technology]."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Jiao, J., Zhao, B., and Zhang, H. (2010, January 19\u201321). A new space image segmentation method based on the non-subsampled contourlet transform. Proceedings of the 2010 Symposium on Photonics and Optoelectronics, Chengdu, China.","DOI":"10.1109\/SOPO.2010.5504342"},{"key":"ref_29","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 (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_30","unstructured":"Ioffe, S., and Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A. (2016). Inception-v4, inception-resnet and the impact of residual connections on learning. arXiv.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and So Kweon, I. (2018, January 8\u201314). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., and Hu, Q. (2020, January 16\u201318). ECA-net: Efficient channel attention for deep convolutional neural networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"ref_35","unstructured":"Party History Research Office of the CPC Changhai County Committee (2018). Changhai Yearbook (Archive) 2017, Liaoning Nationality Publishing House."},{"key":"ref_36","first-page":"99","article-title":"Analysis of the Scale Structure of Mariculture Using Sea in Changhai County Based on GIS","volume":"33","author":"Yan","year":"2016","journal-title":"Ocean Dev. Manag."},{"key":"ref_37","first-page":"106","article-title":"Overview of Research on Synthetic Aperture Radar Image Denoising Algorithm","volume":"39","author":"Liu","year":"2018","journal-title":"J. Arms Equip. Eng."},{"key":"ref_38","unstructured":"Yu, W. (2014). SAR Image Segmentation Based on the Adaptive Frequency Domain Information and Deep Learning. [Master\u2019s Thesis, XIDIAN University]."},{"key":"ref_39","first-page":"13276","article-title":"A Fourier Perspective on Model Robustness in Computer Vision","volume":"32","author":"Yin","year":"2019","journal-title":"Adv. Neural Inform. Process. Syst."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Wang, H., Wu, X., Huang, Z., and Xing, E.P. (2020, January 16\u201318). High-frequency Component Helps Explain the Generalization of Convolutional Neural Networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00871"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1109\/TGRS.2014.2326886","article-title":"Conditional Random Fields for Multitemporal and Multiscale Classification of Optical Satellite Imagery","volume":"53","author":"Hoberg","year":"2015","journal-title":"IEEE Trans. Geoence Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A Survey on Transfer Learning","volume":"22","author":"Pan","year":"2009","journal-title":"IEEE Trans. Knowl. Data Eng."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/24\/4182\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:47:53Z","timestamp":1760179673000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/24\/4182"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,21]]},"references-count":42,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["rs12244182"],"URL":"https:\/\/doi.org\/10.3390\/rs12244182","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,12,21]]}}}