{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T18:09:51Z","timestamp":1775326191714,"version":"3.50.1"},"reference-count":52,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2018,2,24]],"date-time":"2018-02-24T00:00:00Z","timestamp":1519430400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Deanship of Scientific Research at King Saud University through the Local Research Group Program Under Project","award":["RG-1435-055"],"award-info":[{"award-number":["RG-1435-055"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In this paper, we present a new algorithm for cross-domain classification in aerial vehicle images based on generative adversarial networks (GANs). The proposed method, called Siamese-GAN, learns invariant feature representations for both labeled and unlabeled images coming from two different domains. To this end, we train in an adversarial manner a Siamese encoder\u2013decoder architecture coupled with a discriminator network. The encoder\u2013decoder network has the task of matching the distributions of both domains in a shared space regularized by the reconstruction ability, while the discriminator seeks to distinguish between them. After this phase, we feed the resulting encoded labeled and unlabeled features to another network composed of two fully-connected layers for training and classification, respectively. Experiments on several cross-domain datasets composed of extremely high resolution (EHR) images acquired by manned\/unmanned aerial vehicles (MAV\/UAV) over the cities of Vaihingen, Toronto, Potsdam, and Trento are reported and discussed.<\/jats:p>","DOI":"10.3390\/rs10020351","type":"journal-article","created":{"date-parts":[[2018,2,27]],"date-time":"2018-02-27T04:20:47Z","timestamp":1519705247000},"page":"351","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":57,"title":["Siamese-GAN: Learning Invariant Representations for Aerial Vehicle Image Categorization"],"prefix":"10.3390","volume":"10","author":[{"given":"Laila","family":"Bashmal","sequence":"first","affiliation":[{"name":"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":"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":"Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8105-9746","authenticated-orcid":false,"given":"Mohamad","family":"AlRahhal","sequence":"additional","affiliation":[{"name":"Information Science Department, College of Applied Computer Science, King Saud University, Riyadh 11543, Saudi Arabia"}]},{"given":"Nassim","family":"Ammour","sequence":"additional","affiliation":[{"name":"Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia"}]},{"given":"Naif","family":"Alajlan","sequence":"additional","affiliation":[{"name":"Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2018,2,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive image features from scale-invariant keypoints","volume":"60","author":"Lowe","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/0031-3203(95)00067-4","article-title":"A comparative study of texture measures with classification based on featured distributions","volume":"29","author":"Ojala","year":"1996","journal-title":"Pattern Recognit."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1947","DOI":"10.1109\/TGRS.2014.2351395","article-title":"Pyramid of spatial relatons for scene-level land use classification","volume":"53","author":"Chen","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1109\/LGRS.2015.2513443","article-title":"Bag-of-visual-words scene classifier with local and global features for high spatial resolution remote sensing imagery","volume":"13","author":"Zhu","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.ins.2016.02.021","article-title":"Scene classification using local and global features with collaborative representation fusion","volume":"348","author":"Zou","year":"2016","journal-title":"Inf. Sci. (Ny)"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4620","DOI":"10.1109\/JSTARS.2014.2339842","article-title":"Land-use scene classification using a concentric circle-structured multiscale bag-of-visual-words model","volume":"7","author":"Zhao","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1109\/TGRS.2013.2241444","article-title":"Unsupervised feature learning for aerial scene classification","volume":"52","author":"Cheriyadat","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2155","DOI":"10.1109\/LGRS.2015.2453130","article-title":"Land-use classification with compressive sensing multifeature fusion","volume":"12","author":"Mekhalfi","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"6207","DOI":"10.1109\/TGRS.2015.2435801","article-title":"Scene classification based on the multifeature fusion probabilistic topic model for high spatial resolution remote sensing imagery","volume":"53","author":"Zhong","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4238","DOI":"10.1109\/TGRS.2015.2393857","article-title":"Effective and efficient midlevel visual elements-oriented land-use classification using vhr remote sensing images","volume":"53","author":"Cheng","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1109\/LGRS.2015.2503142","article-title":"Unsupervised multilayer feature learning for satellite image scene classification","volume":"13","author":"Li","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2015","DOI":"10.1109\/JSTARS.2015.2444405","article-title":"Unsupervised feature learning via spectral clustering of multidimensional patches for remotely sensed scene classification","volume":"8","author":"Hu","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2108","DOI":"10.1109\/TGRS.2015.2496185","article-title":"Dirichlet-derived multiple topic scene classification model for high spatial resolution remote sensing imagery","volume":"54","author":"Zhao","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1109\/TASL.2011.2109382","article-title":"Acoustic modeling using deep belief networks","volume":"20","author":"Mohamed","year":"2012","journal-title":"IEEE Trans. Audio Speech Lang. Process."},{"key":"ref_15","unstructured":"Vega, P.J.S., Feitosa, R.Q., Quirita, V.H.A., and Happ, P.N. (2016, January 4\u20137). Single sample face recognition from video via stacked supervised auto-encoder. Proceedings of the 29th Graphics, Patterns and Images (SIBGRAPI) Conference, Sao Paulo, Brazil."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1162\/NECO_a_00682","article-title":"Efficient training of convolutional deep belief networks in the frequency domain for application to high-resolution 2D and 3D Images","volume":"27","author":"Brosch","year":"2015","journal-title":"Neural Comput."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1109\/TPAMI.2014.2353635","article-title":"Deep reconstruction models for image set classification","volume":"37","author":"Hayat","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the dimensionality of data with neural networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A fast learning algorithm for deep belief nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Vincent, P., Larochelle, H., Bengio, Y., and Manzagol, P.A. (2008, January 5\u20139). Extracting and composing robust features with denoising autoencoders. Proceedings of the 25th International Conference on Machine Learning, New York, NY, USA.","DOI":"10.1145\/1390156.1390294"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1915","DOI":"10.1109\/TPAMI.2012.231","article-title":"Learning hierarchical features for scene labeling","volume":"35","author":"Farabet","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2448","DOI":"10.1109\/LGRS.2015.2483680","article-title":"Multiview deep learning for land-use classification","volume":"12","author":"Luus","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2321","DOI":"10.1109\/LGRS.2015.2475299","article-title":"Deep Learning based feature selection for remote sensing scene classification","volume":"12","author":"Zou","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1895","DOI":"10.1109\/LGRS.2016.2616440","article-title":"Deep filter banks for land-use scene classification","volume":"13","author":"Wu","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1793","DOI":"10.1109\/TGRS.2015.2488681","article-title":"Scene classification via a gradient boosting random convolutional network framework","volume":"54","author":"Zhang","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"zegedy, 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, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_27","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). Imagenet classification with deep convolutional neural networks. Proceedings of the 25th International Conference on Neural Information Processing Systems, Lake Tahoe, Nevada."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., and Darrell, T. (2014, January 3\u20137). Caffe: Convolutional architecture for fast feature embedding. Proceedings of the 22nd ACM international conference on Multimedia, Orlando, FL, USA.","DOI":"10.1145\/2647868.2654889"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1109\/LGRS.2017.2657778","article-title":"Training deep convolutional neural networks for land-cover classification of high-resolution imagery","volume":"14","author":"Scott","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1016\/j.patcog.2016.07.001","article-title":"Towards better exploiting convolutional neural networks for remote sensing scene classification","volume":"61","author":"Nogueira","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1109\/LGRS.2015.2499239","article-title":"Deep learning earth observation classification using imagenet pretrained networks","volume":"13","author":"Marmanis","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"14680","DOI":"10.3390\/rs71114680","article-title":"Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery","volume":"7","author":"Hu","year":"2015","journal-title":"Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1977","DOI":"10.1080\/01431161.2016.1171928","article-title":"Using convolutional features and a sparse autoencoder for land-use scene classification","volume":"37","author":"Othman","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"4104","DOI":"10.1109\/JSTARS.2017.2705419","article-title":"Aggregating rich hierarchical features for scene classification in remote sensing imagery","volume":"10","author":"Wang","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. EARTH Obs. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"704","DOI":"10.1109\/LGRS.2017.2672643","article-title":"Land-use classification via extreme learning classifier based on deep convolutional features","volume":"14","author":"Weng","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"4775","DOI":"10.1109\/TGRS.2017.2700322","article-title":"Deep feature fusion for VHR remote sensing scene classification","volume":"55","author":"Chaib","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","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_38","unstructured":"Radford, A., Metz, L., and Chintala, S. (2018, February 23). Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Available online: https:\/\/arxiv.org\/abs\/1511.06434."},{"key":"ref_39","unstructured":"Mirza, M., and Osindero, S. (2018, February 23). Conditional Generative Adversarial Nets. Available online: https:\/\/arxiv.org\/abs\/1411.1784."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Tan, W.R., Chan, C.S., Aguirre, H., and Tanaka, K. (2018, February 23). ArtGAN: Artwork Synthesis with Conditional Categorial Gans. Available online: https:\/\/arxiv.org\/abs\/1702.03410.","DOI":"10.1109\/ICIP.2017.8296985"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhang, H., Xu, T., Li, H., Zhang, S., Huang, X., Wang, X., and Metaxas, D. (2018, February 23). Stackgan: Text to Photo-Realistic Image Synthesis with Stacked Generative Adversarial Networks. Available online: https:\/\/arxiv.org\/abs\/1612.03242.","DOI":"10.1109\/ICCV.2017.629"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Ledig, C., Theis, L., Husz\u00e1r, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., and Wang, Z. (2018, February 23). Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. Available online: https:\/\/arxiv.org\/abs\/1609.04802.","DOI":"10.1109\/CVPR.2017.19"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2092","DOI":"10.1109\/LGRS.2017.2752750","article-title":"MARTA GANs: Unsupervised representation learning for remote sensing image classification","volume":"14","author":"Lin","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"He, Z., Liu, H., Wang, Y., and Hu, J. (2017). Generative Adversarial networks-based semi-supervised learning for hyperspectral image classification. Remote Sens., 9.","DOI":"10.3390\/rs9101042"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Suarez, P.L., Sappa, A.D., and Vintimilla, B.X. (2017, January 21\u201326). Infrared image colorization based on a triplet DCGAN architecture. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.32"},{"key":"ref_46","first-page":"387","article-title":"WaterGAN: Unsupervised generative network to enable real-time color correction of monocular underwater images","volume":"3","author":"Li","year":"2018","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_47","first-page":"1","article-title":"Domain-adversarial training of neural networks","volume":"17","author":"Ganin","year":"2016","journal-title":"J. Mach. Learn. Res."},{"key":"ref_48","unstructured":"Liu, M.Y., and Tuzel, O. (2018, February 23). Coupled Generative Adversarial Networks. Available online: https:\/\/arxiv.org\/abs\/1606.07536."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Tzeng, E., Hoffman, J., Saenko, K., and Darrell, T. (2017, February 17). Adversarial Discriminative Domain Adaptation. Available online: https:\/\/arxiv.org\/abs\/1702.05464.","DOI":"10.1109\/CVPR.2017.316"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Bousmalis, K., Silberman, N., Dohan, D., Erhan, D., and Krishnan, D. (arXiv, 2016). Unsupervised pixel-level domain adaptation with generative adversarial networks, arXiv.","DOI":"10.1109\/CVPR.2017.18"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1109\/TCYB.2016.2633306","article-title":"Learning domain-invariant subspace using domain features and independence maximization","volume":"48","author":"Yan","year":"2018","journal-title":"IEEE Trans. Cybern."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Sun, B., Feng, J., and Saenko, K. (2016, January 12\u201317). Return of frustratingly easy domain adaptation. Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, Phoenix, Arizona.","DOI":"10.1609\/aaai.v30i1.10306"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/2\/351\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T14:56:13Z","timestamp":1760194573000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/2\/351"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,2,24]]},"references-count":52,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2018,2]]}},"alternative-id":["rs10020351"],"URL":"https:\/\/doi.org\/10.3390\/rs10020351","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,2,24]]}}}