{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,22]],"date-time":"2026-02-22T07:58:28Z","timestamp":1771747108854,"version":"3.50.1"},"reference-count":32,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,4,19]],"date-time":"2023-04-19T00:00:00Z","timestamp":1681862400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Foreign Talent Program of the Ministry of Science and Technology of China","award":["G2022186003L"],"award-info":[{"award-number":["G2022186003L"]}]},{"name":"Foreign Talent Program of the Ministry of Science and Technology of China","award":["2023YFH0057"],"award-info":[{"award-number":["2023YFH0057"]}]},{"name":"Foreign Talent Program of the Ministry of Science and Technology of China","award":["23ZDYF3125"],"award-info":[{"award-number":["23ZDYF3125"]}]},{"name":"Foreign Talent Program of the Ministry of Science and Technology of China","award":["2021PTJS23"],"award-info":[{"award-number":["2021PTJS23"]}]},{"name":"Sichuan Science and Technology Program","award":["G2022186003L"],"award-info":[{"award-number":["G2022186003L"]}]},{"name":"Sichuan Science and Technology Program","award":["2023YFH0057"],"award-info":[{"award-number":["2023YFH0057"]}]},{"name":"Sichuan Science and Technology Program","award":["23ZDYF3125"],"award-info":[{"award-number":["23ZDYF3125"]}]},{"name":"Sichuan Science and Technology Program","award":["2021PTJS23"],"award-info":[{"award-number":["2021PTJS23"]}]},{"name":"Sichuan Science and Technology Program","award":["G2022186003L"],"award-info":[{"award-number":["G2022186003L"]}]},{"name":"Sichuan Science and Technology Program","award":["2023YFH0057"],"award-info":[{"award-number":["2023YFH0057"]}]},{"name":"Sichuan Science and Technology Program","award":["23ZDYF3125"],"award-info":[{"award-number":["23ZDYF3125"]}]},{"name":"Sichuan Science and Technology Program","award":["2021PTJS23"],"award-info":[{"award-number":["2021PTJS23"]}]},{"name":"Fundamental Research Funds for the Central Universities, Southwest Minzu University","award":["G2022186003L"],"award-info":[{"award-number":["G2022186003L"]}]},{"name":"Fundamental Research Funds for the Central Universities, Southwest Minzu University","award":["2023YFH0057"],"award-info":[{"award-number":["2023YFH0057"]}]},{"name":"Fundamental Research Funds for the Central Universities, Southwest Minzu University","award":["23ZDYF3125"],"award-info":[{"award-number":["23ZDYF3125"]}]},{"name":"Fundamental Research Funds for the Central Universities, Southwest Minzu University","award":["2021PTJS23"],"award-info":[{"award-number":["2021PTJS23"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Semantic segmentation is a growing topic in high-resolution remote sensing image processing. The information in remote sensing images is complex, and the effectiveness of most remote sensing image semantic segmentation methods depends on the number of labels; however, labeling images requires significant time and labor costs. To solve these problems, we propose a semi-supervised semantic segmentation method based on dual cross-entropy consistency and a teacher\u2013student structure. First, we add a channel attention mechanism to the encoding network of the teacher model to reduce the predictive entropy of the pseudo label. Secondly, the two student networks share a common coding network to ensure consistent input information entropy, and a sharpening function is used to reduce the information entropy of unsupervised predictions for both student networks. Finally, we complete the alternate training of the models via two entropy-consistent tasks: (1) semi-supervising student prediction results via pseudo-labels generated from the teacher model, (2) cross-supervision between student models. Experimental results on publicly available datasets indicate that the suggested model can fully understand the hidden information in unlabeled images and reduce the information entropy in prediction, as well as reduce the number of required labeled images with guaranteed accuracy. This allows the new method to outperform the related semi-supervised semantic segmentation algorithm at half the proportion of labeled images.<\/jats:p>","DOI":"10.3390\/e25040681","type":"journal-article","created":{"date-parts":[[2023,4,19]],"date-time":"2023-04-19T03:20:39Z","timestamp":1681874439000},"page":"681","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Semi-Supervised Semantic Segmentation of Remote Sensing Images Based on Dual Cross-Entropy Consistency"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8189-8208","authenticated-orcid":false,"given":"Mengtian","family":"Cui","sequence":"first","affiliation":[{"name":"College of Computer Science and Engineering, Southwest Minzu University, Chengdu 610041, China"}]},{"given":"Kai","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Southwest Minzu University, Chengdu 610041, China"}]},{"given":"Yulan","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Southwest Minzu University, Chengdu 610041, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2904-2349","authenticated-orcid":false,"given":"Dany","family":"Kamuhanda","sequence":"additional","affiliation":[{"name":"Department of Science Mathematics and Physical Education, College of Education, University of Rwanda, Kigali P.O. Box 3900, Rwanda"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7733-6221","authenticated-orcid":false,"given":"Claudio J.","family":"Tessone","sequence":"additional","affiliation":[{"name":"Department of Informatics, University of Zurich, Andreasstrasse 15, CH-8050 Zurich, Switzerland"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4018\/JOEUC.20211101.oa29","article-title":"A survey of semantic construction and application of satellite remote sensing images and data","volume":"33","author":"Lu","year":"2021","journal-title":"J. Organ. End User Comput. (JOEUC)"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"103240","DOI":"10.1016\/j.ijggc.2020.103240","article-title":"Feasibility of using the P-Cable high-resolution 3D seismic system in detecting and monitoring CO2 leakage","volume":"106","author":"Waage","year":"2021","journal-title":"Int. J. Greenh. Gas Control."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.isprsjprs.2016.03.008","article-title":"Optical remotely sensed time series data for land cover classification: A review","volume":"116","author":"White","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Lu, B., Dao, P.D., Liu, J., He, Y., and Shang, J. (2020). Recent advances of hyperspectral imaging technology and applications in agriculture. Remote Sens., 12.","DOI":"10.3390\/rs12162659"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2387","DOI":"10.1109\/JSTARS.2021.3052869","article-title":"Research progress on few-shot learning for remote sensing image interpretation","volume":"14","author":"Sun","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Chen, Z., Zhang, T., and Ouyang, C. (2018). End-to-end airplane detection using transfer learning in remote sensing images. Remote Sens., 10.","DOI":"10.3390\/rs10010139"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1109\/TBDATA.2015.2497270","article-title":"Weakly semi-supervised deep learning for multi-label image annotation","volume":"1","author":"Wu","year":"2015","journal-title":"IEEE Trans. Big Data"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.rse.2018.04.050","article-title":"Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery","volume":"214","author":"Huang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_9","first-page":"1","article-title":"Spectral\u2013spatial anomaly detection of hyperspectral data based on improved isolation forest","volume":"60","author":"Song","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Zhang, Z., Wu, C., Zhang, Z., He, T., Zhang, H., Manmatha, R., Li, M., and Smola, A.J. (2021). Improving Semantic Segmentation via Efficient Self-Training. IEEE Trans. Pattern Anal. Mach. Intell.","DOI":"10.1109\/TPAMI.2021.3138337"},{"key":"ref_11","unstructured":"Tarvainen, A., and Valpola, H. (2017, January 4\u20139). Mean Teachers Are Better Role Models: Weight-Averaged Consistency Targets Improve Semi-Supervised Deep Learning Results. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Luo, Y., Zhu, J., Li, M., Ren, Y., and Zhang, B. (2018, January 18\u201323). Smooth neighbors on teacher graphs for semi-supervised learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00927"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ke, Z., Qiu, D., Li, K., Yan, Q., and Lau, R.W. (2020, January 23\u201328). Guided collaborative training for pixel-wise semi-supervised learning. Proceedings of the Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK.","DOI":"10.1007\/978-3-030-58601-0_26"},{"key":"ref_14","unstructured":"Zou, Y., Zhang, Z., Zhang, H., Li, C.L., Bian, X., Huang, J.B., and Pfister, T. (2020, January 26\u201330). PseudoSeg: Designing Pseudo Labels for Semantic Segmentation. Proceedings of the International Conference on Learning Representations, Addis Ababa, Ethiopia."},{"key":"ref_15","first-page":"596","article-title":"Fixmatch: Simplifying semi-supervised learning with consistency and confidence","volume":"33","author":"Sohn","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Chen, X., Yuan, Y., Zeng, G., and Wang, J. (2021, January 20\u201325). Semi-supervised semantic segmentation with cross pseudo supervision. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00264"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wu, Y., Xu, M., Ge, Z., Cai, J., and Zhang, L. (October, January 27). Semi-supervised left atrium segmentation with mutual consistency training. Proceedings of the Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2021: 24th International Conference, Strasbourg, France.","DOI":"10.1007\/978-3-030-87196-3_28"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Chen, S., Bortsova, G., Garc\u00eda-Uceda Ju\u00e1rez, A., Van Tulder, G., and De Bruijne, M. (2019, January 13\u201317). Multi-task attention-based semi-supervised learning for medical image segmentation. Proceedings of the Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2019: 22nd International Conference, Shenzhen, China.","DOI":"10.1007\/978-3-030-32248-9_51"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ouali, Y., Hudelot, C., and Tami, M. (2020, January 13\u201319). Semi-supervised semantic segmentation with cross-consistency training. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01269"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wu, J., Fan, H., Zhang, X., Lin, S., and Li, Z. (2021, January 5\u20139). Semi-supervised semantic segmentation via entropy minimization. Proceedings of the 2021 IEEE International Conference on Multimedia and Expo (ICME), Shenzhen, China.","DOI":"10.1109\/ICME51207.2021.9428304"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Vu, T.H., Jain, H., Bucher, M., Cord, M., and P\u00e9rez, P. (2019, January 15\u201320). Advent: Adversarial entropy minimization for domain adaptation in semantic segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00262"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Nie, D., Gao, Y., Wang, L., and Shen, D. (2018, January 16\u201320). ASDNet: Attention based semi-supervised deep networks for medical image segmentation. Proceedings of the Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2018: 21st International Conference, Granada, Spain.","DOI":"10.1007\/978-3-030-00937-3_43"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"101733","DOI":"10.1016\/j.media.2020.101733","article-title":"Semi-supervised WCE image classification with adaptive aggregated attention","volume":"64","author":"Guo","year":"2020","journal-title":"Med Image Anal."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1038\/s41592-018-0261-2","article-title":"U-Net: Deep learning for cell counting, detection, and morphometry","volume":"16","author":"Falk","year":"2019","journal-title":"Nat. Methods"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"897","DOI":"10.1109\/JSTARS.2020.3038057","article-title":"Pan-sharpening based on convolutional neural network by using the loss function with no-reference","volume":"14","author":"Xiong","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Petrovai, A., and Nedevschi, S. (2022, January 18\u201324). Exploiting pseudo labels in a self-supervised learning framework for improved monocular depth estimation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00163"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1366","DOI":"10.1109\/TCSVT.2020.3004854","article-title":"Learning hadamard-product-propagation for image dehazing and beyond","volume":"31","author":"Liu","year":"2020","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/B978-0-444-53859-8.00003-5","article-title":"The cross-entropy method for optimization","volume":"Volume 31","author":"Botev","year":"2013","journal-title":"Handbook of Statistics"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"111322","DOI":"10.1016\/j.rse.2019.111322","article-title":"Land-cover classification with high-resolution remote sensing images using transferable deep models","volume":"237","author":"Tong","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"He, T., Zhang, Z., Zhang, H., Zhang, Z., Xie, J., and Li, M. (2019, January 15\u201320). Bag of tricks for image classification with convolutional neural networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00065"},{"key":"ref_31","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_32","doi-asserted-by":"crossref","first-page":"108777","DOI":"10.1016\/j.patcog.2022.108777","article-title":"Dmt: Dynamic mutual training for semi-supervised learning","volume":"130","author":"Feng","year":"2022","journal-title":"Pattern Recognit."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/25\/4\/681\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:18:40Z","timestamp":1760123920000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/25\/4\/681"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,19]]},"references-count":32,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,4]]}},"alternative-id":["e25040681"],"URL":"https:\/\/doi.org\/10.3390\/e25040681","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,19]]}}}