{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T16:22:46Z","timestamp":1775838166354,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2021,9,27]],"date-time":"2021-09-27T00:00:00Z","timestamp":1632700800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Researchers Supporting Project, King Saud University, Riyadh, Saudi Arabia","award":["RSP-2021\/69"],"award-info":[{"award-number":["RSP-2021\/69"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>We present a new method for multi-source semi-supervised domain adaptation in remote sensing scene classification. The method consists of a pre-trained convolutional neural network (CNN) model, namely EfficientNet-B3, for the extraction of highly discriminative features, followed by a classification module that learns feature prototypes for each class. Then, the classification module computes a cosine distance between feature vectors of target data samples and the feature prototypes. Finally, the proposed method ends with a Softmax activation function that converts the distances into class probabilities. The feature prototypes are also divided by a temperature parameter to normalize and control the classification module. The whole model is trained on both the unlabeled and labeled target samples. It is trained to predict the correct classes utilizing the standard cross-entropy loss computed over the labeled source and target samples. At the same time, the model is trained to learn domain invariant features using another loss function based on entropy computed over the unlabeled target samples. Unlike the standard cross-entropy loss, the new entropy loss function is computed on the model\u2019s predicted probabilities and does not need the true labels. This entropy loss, called minimax loss, needs to be maximized with respect to the classification module to learn features that are domain-invariant (hence removing the data shift), and at the same time, it should be minimized with respect to the CNN feature extractor to learn discriminative features that are clustered around the class prototypes (in other words reducing intra-class variance). To accomplish these maximization and minimization processes at the same time, we use an adversarial training approach, where we alternate between the two processes. The model combines the standard cross-entropy loss and the new minimax entropy loss and optimizes them jointly. The proposed method is tested on four RS scene datasets, namely UC Merced, AID, RESISC45, and PatternNet, using two-source and three-source domain adaptation scenarios. The experimental results demonstrate the strong capability of the proposed method to achieve impressive performance despite using only a few (six in our case) labeled target samples per class. Its performance is already better than several state-of-the-art methods, including RevGrad, ADDA, Siamese-GAN, and MSCN.<\/jats:p>","DOI":"10.3390\/rs13193861","type":"journal-article","created":{"date-parts":[[2021,9,27]],"date-time":"2021-09-27T22:16:38Z","timestamp":1632780998000},"page":"3861","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["SSDAN: Multi-Source Semi-Supervised Domain Adaptation Network for Remote Sensing Scene Classification"],"prefix":"10.3390","volume":"13","author":[{"given":"Tariq","family":"Lasloum","sequence":"first","affiliation":[{"name":"Advanced Lab for Intelligent Systems Research (ALISR), Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2164-043X","authenticated-orcid":false,"given":"Haikel","family":"Alhichri","sequence":"additional","affiliation":[{"name":"Advanced Lab for Intelligent Systems Research (ALISR), Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9287-0596","authenticated-orcid":false,"given":"Yakoub","family":"Bazi","sequence":"additional","affiliation":[{"name":"Advanced Lab for Intelligent Systems Research (ALISR), Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1846-1131","authenticated-orcid":false,"given":"Naif","family":"Alajlan","sequence":"additional","affiliation":[{"name":"Advanced Lab for Intelligent Systems Research (ALISR), Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.1109\/JPROC.2016.2598228","article-title":"Big Data for Remote Sensing: Challenges and Opportunities","volume":"104","author":"Chi","year":"2016","journal-title":"Proc. IEEE"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1560","DOI":"10.1109\/JPROC.2015.2449668","article-title":"Multimodal Classification of Remote Sensing Images: A Review and Future Directions","volume":"103","author":"Tuia","year":"2015","journal-title":"Proc. IEEE"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Cheng, G., Han, J., and Lu, X. (2017). Remote Sensing Image Scene Classification: Benchmark and State of the Art, IEEE.","DOI":"10.1109\/JPROC.2017.2675998"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3735","DOI":"10.1109\/JSTARS.2020.3005403","article-title":"Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities","volume":"13","author":"Cheng","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Bazi, Y., Rahhal, M.M.A., Alhichri, H., and Alajlan, N. (2019). Simple Yet Effective Fine-Tuning of Deep CNNs Using an Auxiliary Classification Loss for Remote Sensing Scene Classification. Remote. Sens., 11.","DOI":"10.3390\/rs11242908"},{"key":"ref_6","unstructured":"Tan, M., and Le, Q. (2019, January 24). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. Proceedings of the International Conference on Machine Learning, Available online: https:\/\/arxiv.org\/abs\/1905.11946."},{"key":"ref_7","unstructured":"Saito, K., Kim, D., Sclaroff, S., Darrell, T., and Saenko, K. Semi-Supervised Domain Adaptation via Minimax Entropy. Proceedings of the IEEE International Conference on Computer Vision, Available online: https:\/\/arxiv.org\/abs\/1904.06487."},{"key":"ref_8","unstructured":"Ganin, Y., and Lempitsky, V. Unsupervised Domain Adaptation by Backpropagation. Proceedings of the 32nd International Conference on International Conference on Machine Learning\u2014Volume 37."},{"key":"ref_9","unstructured":"Long, M., Cao, Z., Wang, J., and Jordan, M.I. (2018, January 3\u20138). Conditional Adversarial Domain Adaptation. Proceedings of the 32nd International Conference on Neural Information Processing Systems, Montr\u00e9al, QC, Canada."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Shen, J., Qu, Y., Zhang, W., and Yu, Y. (2018, January 2\u20137). Wasserstein Distance Guided Representation Learning for Domain Adaptation. Proceedings of the 32nd AAAI Conference on Artificial Intelligence, AAAI 2018, New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.11784"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1109\/TNNLS.2016.2618765","article-title":"Incomplete Multisource Transfer Learning","volume":"29","author":"Ding","year":"2018","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Tzeng, E., Hoffman, J., Saenko, K., and Darrell, T. (2016, January 21\u201326). Adversarial Discriminative Domain Adaptation. Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.316"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Xu, R., Chen, Z., Zuo, W., Yan, J., and Lin, L. (2018, January 18\u201323). Deep Cocktail Network: Multi-Source Unsupervised Domain Adaptation with Category Shift. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00417"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wang, R., Collins, L.M., Bradbury, K., and Malof, J.M. (2018, January 22\u201327). Semisupervised Adversarial Discriminative Domain Adaptation, with Applica-tionto Remote Sensing Data. Proceedings of the IGARSS 2018\u20142018 IEEE International Geoscience and Remote Sensing Sym-Posium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518096"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Rahhal, M.M.A., Bazi, Y., Abdullah, T., Mekhalfi, M.L., AlHichri, H., and Zuair, M. (2018). Learning a Multi-Branch Neural Network from Multiple Sources for Knowledge Adaptation in Remote Sensing Imagery. Remote Sens., 10.","DOI":"10.3390\/rs10121890"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2504","DOI":"10.1109\/TGRS.2019.2951779","article-title":"Multisource Compensation Network for Remote Sensing Cross-Domain Scene Classification","volume":"58","author":"Lu","year":"2020","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4441","DOI":"10.1109\/TGRS.2017.2692281","article-title":"Domain Adaptation Network for Cross-Scene Classification","volume":"55","author":"Othman","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"789","DOI":"10.1109\/LGRS.2019.2931305","article-title":"Classifier-Constrained Deep Adversarial Domain Adaptation for Cross-Domain Semisupervised Classification in Remote Sensing Images","volume":"17","author":"Teng","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1109\/LGRS.2018.2800642","article-title":"Asymmetric Adaptation of Deep Features for Cross-Domain Classification in Remote Sensing Imagery","volume":"15","author":"Ammour","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Adayel, R., Bazi, Y., Alhichri, H., and Alajlan, N. (2020). Deep Open-Set Domain Adaptation for Cross-Scene Classification Based on Adversarial Learning and Pareto Ranking. Remote Sens., 12.","DOI":"10.3390\/rs12111716"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"7920","DOI":"10.1109\/TGRS.2020.2985072","article-title":"Domain Adaptation Based on Correlation Subspace Dynamic Distribution Alignment for Remote Sensing Image Scene Classification","volume":"58","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Bashmal, L., Bazi, Y., AlHichri, H., AlRahhal, M.M., Ammour, N., and Alajlan, N. (2018). Siamese-GAN: Learning Invariant Representations for Aerial Vehicle Image Categorization. Remote Sens., 10.","DOI":"10.3390\/rs10020351"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Gong, T., Zheng, X., and Lu, X. (2020). Cross-Domain Scene Classification by Integrating Multiple Incomplete Sources. IEEE Trans. Geosci. Remote Sens., 1\u201312.","DOI":"10.1109\/TGRS.2020.3034344"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"14078","DOI":"10.1109\/ACCESS.2021.3051085","article-title":"Classification of Remote Sensing Images Using EfficientNet-B3 CNN Model with Attention","volume":"9","author":"Alhichri","year":"2021","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Tan, M., Chen, B., Pang, R., Vasudevan, V., Sandler, M., Howard, A., and Le, Q.V. (2019). MnasNet: Platform-Aware Neural Architecture Search for Mobile, IEEE Computer Society. Available online: https:\/\/arxiv.org\/abs\/1807.11626.","DOI":"10.1109\/CVPR.2019.00293"},{"key":"ref_26","unstructured":"K\u0131zrak, A. (2021, September 26). Comparison of Activation Functions for Deep Neural Networks|by Ayy\u00fcce K\u0131zrak|Towards Data Science; 2019. Available online: https:\/\/towardsdatascience.com\/comparison-of-activation-functions-for-deep-neural-networks-706ac4284c8a."},{"key":"ref_27","unstructured":"Hu, J., Shen, L., and Sun, G. Squeeze-and-Excitation Networks. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"ImageNet Large Scale Visual Recognition Challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C. (2018, January 18\u201322). MobileNetV2: Inverted Residuals and Linear Bottlenecks. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_30","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 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_31","unstructured":"Hinton, G., Vinyals, O., and Dean, J. (2015). Distilling the Knowledge in a Neural Network. arXiv."},{"key":"ref_32","unstructured":"Chen, W.-Y., Liu, Y.-C., Kira, Z., Wang, Y.-C.F., and Huang, J.-B. (2019). A Closer Look at Few-Shot Classification. arXiv."},{"key":"ref_33","first-page":"721","article-title":"TADAM: Task Dependent Adaptive Metric for Improved Few-Shot Learning","volume":"2018","author":"Oreshkin","year":"2018","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Alajaji, D., Alhichri, H.S., Ammour, N., and Alajlan, N. (2020, January 9\u201311). Few-Shot Learning For Remote Sensing Scene Classification. Proceedings of the 2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS), Tunis, Tunisia.","DOI":"10.1109\/M2GARSS47143.2020.9105154"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Yang, Y., and Newsam, S. (2010, January 2\u20135). Bag-of-Visual-Words and Spatial Extensions for Land-Use Classification. Proceedings of the Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, San Jose, CA, USA.","DOI":"10.1145\/1869790.1869829"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3965","DOI":"10.1109\/TGRS.2017.2685945","article-title":"AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification","volume":"55","author":"Xia","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.isprsjprs.2018.01.004","article-title":"PatternNet: A Benchmark Dataset for Performance Evaluation of Remote Sensing Image Retrieval","volume":"145","author":"Zhou","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_38","unstructured":"Ranjan, R., Castillo, C.D., and Chellappa, R. (2017). L2-Constrained Softmax Loss for Discriminative Face Verification. arXiv, Available online:https:\/\/arxiv.org\/abs\/1703.09507."},{"key":"ref_39","first-page":"2579","article-title":"Visualizing Data Using T-SNE","volume":"9","author":"Hinton","year":"2008","journal-title":"J. Mach. Learn. Res."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/19\/3861\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:05:41Z","timestamp":1760166341000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/19\/3861"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,27]]},"references-count":39,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2021,10]]}},"alternative-id":["rs13193861"],"URL":"https:\/\/doi.org\/10.3390\/rs13193861","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,27]]}}}