{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T03:50:01Z","timestamp":1768708201192,"version":"3.49.0"},"reference-count":53,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,3,3]],"date-time":"2023-03-03T00:00:00Z","timestamp":1677801600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Integovernmental cooperation in international science and technology innovation of the Ministry of Science and Technology","award":["2021YFE0102000"],"award-info":[{"award-number":["2021YFE0102000"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The performance of a semantic segmentation model for remote sensing (RS) images pre-trained on an annotated dataset greatly decreases when testing on another unannotated dataset because of the domain gap. Adversarial generative methods, e.g., DualGAN, are utilized for unpaired image-to-image translation to minimize the pixel-level domain gap, which is one of the common approaches for unsupervised domain adaptation (UDA). However, the existing image translation methods face two problems when performing RS image translation: (1) ignoring the scale discrepancy between two RS datasets, which greatly affects the accuracy performance of scale-invariant objects; (2) ignoring the characteristic of real-to-real translation of RS images, which brings an unstable factor for the training of the models. In this paper, ResiDualGAN is proposed for RS image translation, where an in-network resizer module is used for addressing the scale discrepancy of RS datasets and a residual connection is used for strengthening the stability of real-to-real images translation and improving the performance in cross-domain semantic segmentation tasks. Combined with an output space adaptation method, the proposed method greatly improves the accuracy performance on common benchmarks, which demonstrates the superiority and reliability of ResiDualGAN. At the end of the paper, a thorough discussion is conducted to provide a reasonable explanation for the improvement of ResiDualGAN. Our source code is also available.<\/jats:p>","DOI":"10.3390\/rs15051428","type":"journal-article","created":{"date-parts":[[2023,3,6]],"date-time":"2023-03-06T01:35:30Z","timestamp":1678066530000},"page":"1428","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["ResiDualGAN: Resize-Residual DualGAN for Cross-Domain Remote Sensing Images Semantic Segmentation"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1728-3281","authenticated-orcid":false,"given":"Yang","family":"Zhao","sequence":"first","affiliation":[{"name":"Institute of Remote Sensing and Geographic Information System, Peking University, Beijing 100871, China"},{"name":"Deyang Institute of Smart Agriculture (DISA), TaiShan North Road 290, Deyang 618099, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3692-0453","authenticated-orcid":false,"given":"Peng","family":"Guo","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Geographic Information System, Peking University, Beijing 100871, China"},{"name":"Deyang Institute of Smart Agriculture (DISA), TaiShan North Road 290, Deyang 618099, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0624-5100","authenticated-orcid":false,"given":"Zihao","family":"Sun","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Geographic Information System, Peking University, Beijing 100871, China"},{"name":"Deyang Institute of Smart Agriculture (DISA), TaiShan North Road 290, Deyang 618099, China"}]},{"given":"Xiuwan","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Geographic Information System, Peking University, Beijing 100871, China"},{"name":"Deyang Institute of Smart Agriculture (DISA), TaiShan North Road 290, Deyang 618099, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1633-6652","authenticated-orcid":false,"given":"Han","family":"Gao","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Geographic Information System, Peking University, Beijing 100871, China"},{"name":"Deyang Institute of Smart Agriculture (DISA), TaiShan North Road 290, Deyang 618099, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gao, H., Guo, J., Guo, P., and Chen, X. (2021). Classification of Very-High-Spatial-Resolution Aerial Images Based on Multiscale Features with Limited Semantic Information. Remote Sens., 13.","DOI":"10.3390\/rs13030364"},{"key":"ref_2","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20138). ImageNet Classification with Deep Convolutional Neural Networks. Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_3","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"},{"key":"ref_4","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015, Springer."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_6","first-page":"1","article-title":"Weakly Supervised Semantic Segmentation in Aerial Imagery via Explicit Pixel-Level Constraints","volume":"60","author":"Zhou","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","first-page":"1","article-title":"Unsupervised single-scene semantic segmentation for Earth observation","volume":"60","author":"Saha","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Pan, X., Xu, J., Zhao, J., and Li, X. (2022). Hierarchical Object-Focused and Grid-Based Deep Unsupervised Segmentation Method for High-Resolution Remote Sensing Images. Remote Sens., 14.","DOI":"10.3390\/rs14225768"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"114417","DOI":"10.1016\/j.eswa.2020.114417","article-title":"A review of deep learning methods for semantic segmentation of remote sensing imagery","volume":"169","author":"Yuan","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/s13735-017-0141-z","article-title":"A review of semantic segmentation using deep neural networks","volume":"7","author":"Guo","year":"2018","journal-title":"Int. J. Multimed. Inf. Retr."},{"key":"ref_11","first-page":"1","article-title":"Curriculum-Style Local-to-Global Adaptation for Cross-Domain Remote Sensing Image Segmentation","volume":"60","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.isprsjprs.2021.02.009","article-title":"Learning deep semantic segmentation network under multiple weakly-supervised constraints for cross-domain remote sensing image semantic segmentation","volume":"175","author":"Li","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"8407","DOI":"10.1109\/JSTARS.2021.3105421","article-title":"Weakly-Supervised Domain Adaptation With Adversarial Entropy for Building Segmentation in Cross-Domain Aerial Imagery","volume":"14","author":"Yao","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Bousmalis, K., Silberman, N., Dohan, D., Erhan, D., and Krishnan, D. (2017, January 21\u201326). Unsupervised pixel-level domain adaptation with generative adversarial networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.18"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Shrivastava, A., Pfister, T., Tuzel, O., Susskind, J., Wang, W., and Webb, R. (2017, January 21\u201326). Learning from simulated and unsupervised images through adversarial training. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.241"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yan, Z., Yu, X., Qin, Y., Wu, Y., Han, X., and Cui, S. (2021, January 20\u201324). Pixel-level Intra-domain Adaptation for Semantic Segmentation. Proceedings of the 29th ACM International Conference on Multimedia, Virtual Event.","DOI":"10.1145\/3474085.3475174"},{"key":"ref_17","unstructured":"Ganin, Y., and Lempitsky, V. (2015, January 7\u20139). Unsupervised Domain Adaptation by Backpropagation. Proceedings of the 32nd International Conference on Machine Learning, PMLR, Lille, France."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Tsai, Y.H., Hung, W.C., Schulter, S., Sohn, K., Yang, M.H., and Chandraker, M. (2018, January 18\u201323). Learning to adapt structured output space for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00780"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Vedaldi, A., Bischof, H., Brox, T., and Frahm, J.M. (2020, January 23\u201328). Classes Matter: A Fine-Grained Adversarial Approach to Cross-Domain Semantic Segmentation. Proceedings of the Computer Vision\u2014ECCV 2020, Glasgow, UK.","DOI":"10.1007\/978-3-030-58592-1"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zou, Y., Yu, Z., Kumar, B., and Wang, J. (2018, January 8\u201314). Unsupervised domain adaptation for semantic segmentation via class-balanced self-training. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01219-9_18"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Tranheden, W., Olsson, V., Pinto, J., and Svensson, L. (2021, January 3\u20138). Dacs: Domain adaptation via cross-domain mixed sampling. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACV48630.2021.00142"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Hoyer, L., Dai, D., and Van Gool, L. (2022, January 18\u201324). Daformer: Improving network architectures and training strategies for domain-adaptive semantic segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00969"},{"key":"ref_23","first-page":"139","article-title":"Generative adversarial nets","volume":"27","author":"Goodfellow","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., and Efros, A.A. (2017, January 22\u201329). Unpaired image-to-image translation using cycle-consistent adversarial networks. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref_25","first-page":"5998","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Benjdira, B., Bazi, Y., Koubaa, A., and Ouni, K. (2019). Unsupervised Domain Adaptation Using Generative Adversarial Networks for Semantic Segmentation of Aerial Images. Remote Sens., 11.","DOI":"10.3390\/rs11111369"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3816","DOI":"10.1109\/TGRS.2020.3020804","article-title":"Generative Adversarial Network-Based Full-Space Domain Adaptation for Land Cover Classification From Multiple-Source Remote Sensing Images","volume":"59","author":"Ji","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1896","DOI":"10.1109\/LGRS.2020.3010591","article-title":"An End-to-End Network for Remote Sensing Imagery Semantic Segmentation via Joint Pixel- and Representation-Level Domain Adaptation","volume":"18","author":"Shi","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Shi, T., Li, Y., and Zhang, Y. (2021, January 11\u201316). Rotation Consistency-Preserved Generative Adversarial Networks for Cross-Domain Aerial Image Semantic Segmentation. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9554606"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"7178","DOI":"10.1109\/TGRS.2020.2980417","article-title":"ColorMapGAN: Unsupervised domain adaptation for semantic segmentation using color mapping generative adversarial networks","volume":"58","author":"Tasar","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","first-page":"1","article-title":"Domain Adaptation for Remote Sensing Image Semantic Segmentation: An Integrated Approach of Contrastive Learning and Adversarial Learning","volume":"60","author":"Bai","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.isprsjprs.2021.08.004","article-title":"Appearance based deep domain adaptation for the classification of aerial images","volume":"180","author":"Wittich","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Yi, Z., Zhang, H., Tan, P., and Gong, M. (2017, January 22\u201329). Dualgan: Unsupervised dual learning for image-to-image translation. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.310"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., and Schiele, B. (2016, January 27\u201330). The cityscapes dataset for semantic urban scene understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.350"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Leibe, B., Matas, J., Sebe, N., and Welling, M. (2016, January 11\u201314). Playing for Data: Ground Truth from Computer Games. Proceedings of the Computer Vision\u2014ECCV 2016, Amsterdam, The Netherlands. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-319-46493-0"},{"key":"ref_36","unstructured":"ISPRS WG III\/4 (2023, January 03). ISPRS 2D Semantic Labeling Contest. Available online: https:\/\/www2.isprs.org\/commissions\/comm2\/wg4\/benchmark\/semantic-labeling."},{"key":"ref_37","unstructured":"Arjovsky, M., Chintala, S., and Bottou, L. (2017, January 6\u201311). Wasserstein generative adversarial networks. Proceedings of the 34th International Conference on Machine Learning, PMLR, Sydney, Australia."},{"key":"ref_38","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A. (2017). Improved Training of Wasserstein GANs. arXiv."},{"key":"ref_39","unstructured":"Ulyanov, D., Vedaldi, A., and Lempitsky, V. (2016). Instance normalization: The missing ingredient for fast stylization. arXiv."},{"key":"ref_40","unstructured":"Maas, A.L., Hannun, A.Y., and Ng, A.Y. (2013, January 17\u201319). Rectifier nonlinearities improve neural network acoustic models. Proceedings of the 30th International Conference on Machine Learning, Atlanta, GA, USA."},{"key":"ref_41","unstructured":"Nair, V., and Hinton, G.E. (2010, January 21\u201324). Rectified linear units improve restricted boltzmann machines. Proceedings of the 27th International Conference on Machine Learning (ICML-10), Haifa, Israel."},{"key":"ref_42","unstructured":"Ioffe, S., and Szegedy, C. (2015). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv."},{"key":"ref_43","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv."},{"key":"ref_44","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_45","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_46","unstructured":"Diederik, P.K., and Ba, J. (2017). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_47","first-page":"26","article-title":"Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude","volume":"4","author":"Tieleman","year":"2012","journal-title":"COURSERA Neural Netw. Mach. Learn."},{"key":"ref_48","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Hinton","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1010","DOI":"10.1109\/TITS.2018.2838132","article-title":"SINet: A scale-insensitive convolutional neural network for fast vehicle detection","volume":"20","author":"Hu","year":"2018","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Gao, Y., Guo, S., Huang, K., Chen, J., Gong, Q., Zou, Y., Bai, T., and Overett, G. (2017, January 11\u201314). Scale optimization for full-image-CNN vehicle detection. Proceedings of the 2017 IEEE Intelligent Vehicles Symposium (IV), Los Angeles, CA, USA.","DOI":"10.1109\/IVS.2017.7995812"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Chaurasia, A., and Culurciello, E. (2017, January 10\u201313). Linknet: Exploiting encoder representations for efficient semantic segmentation. Proceedings of the 2017 IEEE Visual Communications and Image Processing (VCIP), St. Petersburg, FL, USA.","DOI":"10.1109\/VCIP.2017.8305148"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Talebi, H., and Milanfar, P. (2021). Learning to Resize Images for Computer Vision Tasks. arXiv.","DOI":"10.1109\/ICCV48922.2021.00055"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Zheng, C., Cham, T.J., and Cai, J. (2018, January 8\u201314). T2net: Synthetic-to-realistic translation for solving single-image depth estimation tasks. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_47"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/5\/1428\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:46:57Z","timestamp":1760122017000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/5\/1428"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,3]]},"references-count":53,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["rs15051428"],"URL":"https:\/\/doi.org\/10.3390\/rs15051428","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,3]]}}}