{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T17:56:22Z","timestamp":1767117382489,"version":"build-2065373602"},"reference-count":80,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2019,9,1]],"date-time":"2019-09-01T00:00:00Z","timestamp":1567296000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Remote sensing image retrieval (RSIR), a superior content organization technique, plays an important role in the remote sensing (RS) community. With the number of RS images increases explosively, not only the retrieval precision but also the retrieval efficiency is emphasized in the large-scale RSIR scenario. Therefore, the approximate nearest neighborhood (ANN) search attracts the researchers\u2019 attention increasingly. In this paper, we propose a new hash learning method, named semi-supervised deep adversarial hashing (SDAH), to accomplish the ANN for the large-scale RSIR task. The assumption of our model is that the RS images have been represented by the proper visual features. First, a residual auto-encoder (RAE) is developed to generate the class variable and hash code. Second, two multi-layer networks are constructed to regularize the obtained latent vectors using the prior distribution. These two modules mentioned are integrated under the generator adversarial framework. Through the minimax learning, the class variable would be a one-hot-like vector while the hash code would be the binary-like vector. Finally, a specific hashing function is formulated to enhance the quality of the generated hash code. The effectiveness of the hash codes learned by our SDAH model was proved by the positive experimental results counted on three public RS image archives. Compared with the existing hash learning methods, the proposed method reaches improved performance.<\/jats:p>","DOI":"10.3390\/rs11172055","type":"journal-article","created":{"date-parts":[[2019,9,2]],"date-time":"2019-09-02T03:16:12Z","timestamp":1567394172000},"page":"2055","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Large-Scale Remote Sensing Image Retrieval Based on Semi-Supervised Adversarial Hashing"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1375-0778","authenticated-orcid":false,"given":"Xu","family":"Tang","sequence":"first","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Chao","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Jingjing","family":"Ma","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0379-2042","authenticated-orcid":false,"given":"Xiangrong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Fang","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}]},{"given":"Licheng","family":"Jiao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,1]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.isprsjprs.2012.09.010","article-title":"A review of EO image information mining","volume":"75","author":"Quartulli","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1109\/TGRS.2006.890579","article-title":"GeoIRIS: Geospatial information retrieval and indexing system\u2014Content mining, semantics modeling, and complex queries","volume":"45","author":"Shyu","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3023","DOI":"10.1109\/TGRS.2013.2268736","article-title":"Remote sensing image retrieval with global morphological texture descriptors","volume":"52","author":"Aptoula","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"892","DOI":"10.1109\/TGRS.2015.2469138","article-title":"Hashing-based scalable remote sensing image search and retrieval in large archives","volume":"54","author":"Demir","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Gu, Y., Wang, Y., and Li, Y. (2019). A Survey on Deep Learning-Driven Remote Sensing Image Scene Understanding: Scene Classification, Scene Retrieval and Scene-Guided Object Detection. Appl. Sci., 9.","DOI":"10.3390\/app9102110"},{"key":"ref_7","unstructured":"Wang, Q., Chen, M.L., Nie, F.P., and Li, X.L. (2018). Detecting coherent groups in crowd scenes by multiview clustering. IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1265","DOI":"10.1109\/TNNLS.2018.2861209","article-title":"Spectral embedded adaptive neighbors clustering","volume":"30","author":"Wang","year":"2018","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1581","DOI":"10.1109\/TNNLS.2018.2868836","article-title":"Hierarchical feature selection for random projection","volume":"30","author":"Wang","year":"2018","journal-title":"IEEE Trans. Neural Networks and Learning Systems"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4148","DOI":"10.1109\/TVT.2018.2883046","article-title":"Robust hierarchical deep learning for vehicular management","volume":"68","author":"Wang","year":"2018","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_11","unstructured":"Wang, J., Shen, H.T., Song, J., and Ji, J. (2014). Hashing for similarity search: A survey. arXiv."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1109\/TPAMI.2017.2699960","article-title":"A survey on learning to hash","volume":"40","author":"Wang","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_13","first-page":"2","article-title":"Fast approximate nearest neighbors with automatic algorithm configuration","volume":"2","author":"Muja","year":"2009","journal-title":"VISAPP"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2227","DOI":"10.1109\/TPAMI.2014.2321376","article-title":"Scalable nearest neighbor algorithms for high dimensional data","volume":"36","author":"Muja","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Indyk, P., and Motwani, R. (1998, January 24\u201326). Approximate nearest neighbors: Towards removing the curse of dimensionality. Proceedings of the Thirtieth Annual ACM Symposium on Theory of Computing, Dallas, TX, USA.","DOI":"10.1145\/276698.276876"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Charikar, M.S. (2002, January 19\u201321). Similarity estimation techniques from rounding algorithms. Proceedings of the Thiry-Fourth Annual ACM Symposium on Theory of Computing, Montreal, QC, Canada.","DOI":"10.1145\/509907.509965"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1145\/1327452.1327494","article-title":"Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions","volume":"51","author":"Andoni","year":"2008","journal-title":"Commun. ACM"},{"key":"ref_18","first-page":"11","article-title":"Hashing techniques: A survey and taxonomy","volume":"50","author":"Chi","year":"2017","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_19","unstructured":"Gionis, A., Indyk, P., and Motwani, R. (1999, January 7\u201310). Similarity search in high dimensions via hashing. Proceedings of the 25rd International Conference on Very Large Data, Edinburgh, Scotland, UK."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Datar, M., Immorlica, N., Indyk, P., and Mirrokni, V.S. (2004, January 8\u201311). Locality-sensitive hashing scheme based on p-stable distributions. Proceedings of the Twentieth Annual Symposium on Computational Geometry, Brooklyn, NY, USA.","DOI":"10.1145\/997817.997857"},{"key":"ref_21","unstructured":"Lv, Q., Josephson, W., Wang, Z., Charikar, M., and Li, K. (2007, January 23\u201327). Multi-probe LSH: Efficient indexing for high-dimensional similarity search. Proceedings of the 33rd International Conference on Very Large Data Bases, Vienna, Austria."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Li, P., and K\u00f6nig, C. (2010, January 26\u201330). b-Bit minwise hashing. Proceedings of the 19th International Conference on World Wide Web, Raleigh, NC, USA.","DOI":"10.1145\/1772690.1772759"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Li, P., Konig, A., and Gui, W. (2010, January 6\u201311). b-Bit minwise hashing for estimating three-way similarities. Proceedings of the Advances in Neural Information Processing Systems 2010, Vancouver, BC, Canada.","DOI":"10.1145\/1772690.1772759"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Gan, J., Feng, J., Fang, Q., and Ng, W. (2012, January 20\u201324). Locality-sensitive hashing scheme based on dynamic collision counting. Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, Scottsdale, AZ, USA.","DOI":"10.1145\/2213836.2213898"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2039","DOI":"10.1109\/ACCESS.2017.2781360","article-title":"Binary hashing for approximate nearest neighbor search on big data: A survey","volume":"6","author":"Cao","year":"2017","journal-title":"IEEE Access"},{"key":"ref_26","unstructured":"Weiss, Y., Torralba, A., and Fergus, R. (2009, January 7\u201310). Spectral hashing. Proceedings of the Advances in Neural Information Processing Systems 2009, Vancouver, BC, Canada."},{"key":"ref_27","unstructured":"Liu, W., Mu, C., Kumar, S., and Chang, S.F. (2014, January 8\u201313). Discrete graph hashing. Proceedings of the Advances in Neural Information Processing Systems 2014, Montreal, QC, Canada."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Shi, X., Xing, F., Cai, J., Zhang, Z., Xie, Y., and Yang, L. (2016, January 11\u201314). Kernel-based supervised discrete hashing for image retrieval. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46478-7_26"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1109\/TPAMI.2017.2678475","article-title":"Fast supervised discrete hashing","volume":"40","author":"Gui","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_31","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 2012, Lake Tahoe, NV, USA."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Erin Liong, V., Lu, J., Wang, G., Moulin, P., and Zhou, J. (2015, January 7\u201312). Deep hashing for compact binary codes learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298862"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Lai, H., Pan, Y., Liu, Y., and Yan, S. (2015, January 7\u201312). Simultaneous feature learning and hash coding with deep neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298947"},{"key":"ref_34","unstructured":"Zhao, F., Huang, Y., Wang, L., and Tan, T. (2015, January 7\u201312). Deep semantic ranking based hashing for multi-label image retrieval. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Liu, H., Wang, R., Shan, S., and Chen, X. (2016, January 27\u201330). Deep supervised hashing for fast image retrieval. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.227"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Xia, R., Pan, Y., Lai, H., Liu, C., and Yan, S. (2014, January 27\u201331). Supervised hashing for image retrieval via image representation learning. Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, Qu\u00e9bec City, QC, Canada.","DOI":"10.1609\/aaai.v28i1.8952"},{"key":"ref_37","unstructured":"Wang, D., Cui, P., Ou, M., and Zhu, W. (2015, January 25\u201331). Deep multimodal hashing with orthogonal regularization. Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, Buenos Aires, Argentina."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhu, H., Long, M., Wang, J., and Cao, Y. (2016, January 12\u201317). Deep hashing network for efficient similarity retrieval. Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, Phoenix, AZ, USA.","DOI":"10.1609\/aaai.v30i1.10235"},{"key":"ref_39","unstructured":"Li, Q., Sun, Z., He, R., and Tan, T. (2017, January 4\u20139). Deep supervised discrete hashing. Proceedings of the Advances in Neural Information Processing Systems 2017, Long Beach, CA, USA."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"950","DOI":"10.1109\/TGRS.2017.2756911","article-title":"Large-scale remote sensing image retrieval by deep hashing neural networks","volume":"56","author":"Li","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","unstructured":"Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., and Frey, B. (2015). Adversarial autoencoders. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2923","DOI":"10.1109\/TGRS.2003.817197","article-title":"Information mining in remote sensing image archives: System concepts","volume":"41","author":"Datcu","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1109\/TGRS.2012.2205158","article-title":"Geographic image retrieval using local invariant features","volume":"51","author":"Yang","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","first-page":"366","article-title":"Object classification of aerial images with bag-of-visual words","volume":"7","author":"Xu","year":"2009","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_45","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_46","doi-asserted-by":"crossref","unstructured":"Tang, X., Zhang, X., Liu, F., and Jiao, L. (2018). Unsupervised deep feature learning for remote sensing image retrieval. Remote Sens., 10.","DOI":"10.3390\/rs10081243"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"3876","DOI":"10.1109\/JSTARS.2015.2429137","article-title":"SAR images retrieval based on semantic classification and region-based similarity measure for earth observation","volume":"8","author":"Jiao","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1824","DOI":"10.1109\/JSTARS.2017.2664119","article-title":"SAR image content retrieval based on fuzzy similarity and relevance feedback","volume":"10","author":"Tang","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhang, Y., Tao, C., and Zhu, H. (2016). Content-based high-resolution remote sensing image retrieval via unsupervised feature learning and collaborative affinity metric fusion. Remote Sens., 8.","DOI":"10.3390\/rs8090709"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1109\/TGRS.2007.892007","article-title":"Interactive remote-sensing image retrieval using active relevance feedback","volume":"45","author":"Ferecatu","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2323","DOI":"10.1109\/TGRS.2014.2358804","article-title":"A novel active learning method in relevance feedback for content-based remote sensing image retrieval","volume":"53","author":"Demir","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1109\/LGRS.2016.2636819","article-title":"Fusion similarity-based reranking for SAR image retrieval","volume":"14","author":"Tang","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"5798","DOI":"10.1109\/TGRS.2017.2714676","article-title":"Two-stage reranking for remote sensing image retrieval","volume":"55","author":"Tang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"He, J., Liu, W., and Chang, S.F. (2010, January 25\u201328). Scalable similarity search with optimized kernel hashing. Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA.","DOI":"10.1145\/1835804.1835946"},{"key":"ref_55","unstructured":"Heo, J.P., Lee, Y., He, J., Chang, S.F., and Yoon, S.E. (2012, January 16\u201321). Spherical hashing. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2304","DOI":"10.1109\/TPAMI.2015.2408363","article-title":"Spherical hashing: Binary code embedding with hyperspheres","volume":"37","author":"Heo","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Shen, F., Shen, C., Liu, W., and Tao Shen, H. (2015, January 7\u201312). Supervised discrete hashing. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298598"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Do, T.T., Doan, A.D., Nguyen, D.T., and Cheung, N.M. (2016, January 8\u201316). Binary hashing with semidefinite relaxation and augmented lagrangian. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46475-6_49"},{"key":"ref_59","unstructured":"Liu, W., Wang, J., Ji, R., Jiang, Y.G., and Chang, S.F. (2012, January 16\u201321). Supervised hashing with kernels. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"969","DOI":"10.1016\/j.ijar.2008.11.006","article-title":"Semantic hashing","volume":"50","author":"Salakhutdinov","year":"2009","journal-title":"Int. J. Approx. Reason."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Do, T.T., Doan, A.D., and Cheung, N.M. (2016, January 8\u201316). Learning to hash with binary deep neural network. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46454-1_14"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Jiang, Q.Y., and Li, W.J. (2018, January 2\u20137). Asymmetric deep supervised hashing. Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans, LO, USA.","DOI":"10.1609\/aaai.v32i1.11814"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"6521","DOI":"10.1109\/TGRS.2018.2839705","article-title":"Learning source-invariant deep hashing convolutional neural networks for cross-source remote sensing image retrieval","volume":"56","author":"Li","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_64","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8\u201313). Generative adversarial nets. Proceedings of the Advances in Neural Information Processing Systems 2014, Montreal, QC, Canada."},{"key":"ref_65","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_66","unstructured":"Murphy, K.P. (2012). Machine Learning: A Probabilistic Perspective, MIT Press."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/s10479-005-5724-z","article-title":"A tutorial on the cross-entropy method","volume":"134","author":"Kroese","year":"2005","journal-title":"Ann. Oper. Res."},{"key":"ref_68","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Ghasedi Dizaji, K., Zheng, F., Sadoughi, N., Yang, Y., Deng, C., and Huang, H. (2018, January 18\u201322). Unsupervised deep generative adversarial hashing network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00386"},{"key":"ref_70","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 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, San Jose, CA, USA.","DOI":"10.1145\/1869790.1869829"},{"key":"ref_71","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_72","doi-asserted-by":"crossref","first-page":"1865","DOI":"10.1109\/JPROC.2017.2675998","article-title":"Remote sensing image scene classification: Benchmark and state of the art","volume":"105","author":"Cheng","year":"2017","journal-title":"Proc. IEEE"},{"key":"ref_73","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_74","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","article-title":"Deep learning in remote sensing: A comprehensive review and list of resources","volume":"5","author":"Zhu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Zhou, W., Newsam, S., Li, C., and Shao, Z. (2017). Learning low dimensional convolutional neural networks for high-resolution remote sensing image retrieval. Remote Sens., 9.","DOI":"10.3390\/rs9050489"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"1380","DOI":"10.1109\/TKDE.2012.76","article-title":"Semi-supervised nonlinear hashing using bootstrap sequential projection learning","volume":"25","author":"Wu","year":"2012","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1109\/TCSVT.2017.2771332","article-title":"SSDH: Semi-supervised deep hashing for large scale image retrieval","volume":"29","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Cao, Y., Long, M., Wang, J., Zhu, H., and Wen, Q. (2016, January 12\u201317). Deep quantization network for efficient image retrieval. Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, Phoenix, AZ, USA.","DOI":"10.1609\/aaai.v30i1.10455"},{"key":"ref_80","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/17\/2055\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:15:48Z","timestamp":1760188548000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/17\/2055"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,1]]},"references-count":80,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2019,9]]}},"alternative-id":["rs11172055"],"URL":"https:\/\/doi.org\/10.3390\/rs11172055","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2019,9,1]]}}}