{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T10:12:09Z","timestamp":1760609529564,"version":"build-2065373602"},"reference-count":42,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2023,6,21]],"date-time":"2023-06-21T00:00:00Z","timestamp":1687305600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41961063"],"award-info":[{"award-number":["41961063"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With the increasing demand for remote sensing image applications, extracting the required images from a huge set of remote sensing images has become a hot topic. The previous retrieval methods cannot guarantee the efficiency, accuracy, and interpretability in the retrieval process. Therefore, we propose a bag-of-words association mapping method that can explain the semantic derivation process of remote sensing images. The method constructs associations between low-level features and high-level semantics through visual feature word packets. An improved FP-Growth method is proposed to achieve the construction of strong association rules to semantics. A feedback mechanism is established to improve the accuracy of subsequent retrievals by reducing the semantic probability of incorrect retrieval results. The public datasets AID and NWPU-RESISC45 were used to validate these experiments. The experimental results show that the average accuracies of the two datasets reach 87.5% and 90.8%, which are 22.5% and 20.3% higher than VGG16, and 17.6% and 15.6% higher than ResNet18, respectively. The experimental results were able to validate the effectiveness of our proposed method.<\/jats:p>","DOI":"10.3390\/s23135807","type":"journal-article","created":{"date-parts":[[2023,6,22]],"date-time":"2023-06-22T02:09:17Z","timestamp":1687399757000},"page":"5807","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Semantic Retrieval of Remote Sensing Images Based on the Bag-of-Words Association Mapping Method"],"prefix":"10.3390","volume":"23","author":[{"given":"Jingwen","family":"Li","sequence":"first","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China"},{"name":"Ecological Spatiotemporal Big Data Perception Service Laboratory, Guilin University of Technology, Guilin 541004, China"}]},{"given":"Yanting","family":"Cai","sequence":"additional","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China"}]},{"given":"Xu","family":"Gong","sequence":"additional","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7146-0141","authenticated-orcid":false,"given":"Jianwu","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China"},{"name":"Ecological Spatiotemporal Big Data Perception Service Laboratory, Guilin University of Technology, Guilin 541004, China"}]},{"given":"Yanling","family":"Lu","sequence":"additional","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China"}]},{"given":"Xiaode","family":"Meng","sequence":"additional","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China"}]},{"given":"Li","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1349","DOI":"10.1109\/34.895972","article-title":"Content-based image retrieval at the end of the early years","volume":"22","author":"Smeulders","year":"2000","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_2","first-page":"23","article-title":"Integration of Color and Texture Features in CBIR System","volume":"164","author":"Atlam","year":"2017","journal-title":"Int. J. Comput. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"650","DOI":"10.1016\/j.asoc.2015.01.058","article-title":"Content-based image retrieval techniques for the analysis of dermatological lesions using particle swarm optimization technique","volume":"30","author":"Jiji","year":"2015","journal-title":"Appl. Soft Comput."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1049\/iet-ipr.2018.5277","article-title":"Detected text-based image retrieval approach for textual images","volume":"13","author":"Unar","year":"2019","journal-title":"IET Image Process."},{"key":"ref_5","first-page":"611","article-title":"Implementation and comparison of text-based image retrieval schemes","volume":"10","author":"Zaidi","year":"2019","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.neucom.2013.08.007","article-title":"An image retrieval scheme with relevance feedback using feature reconstruction and SVM reclassification","volume":"127","author":"Wang","year":"2014","journal-title":"Neurocomputing"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1927469","DOI":"10.1080\/23311916.2021.1927469","article-title":"Content-based image retrieval: A review of recent trends","volume":"8","author":"Hameed","year":"2021","journal-title":"Cogent Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"9658350","DOI":"10.1155\/2019\/9658350","article-title":"Content-Based Image Retrieval and Feature Extraction: A Comprehensive Review","volume":"2019","author":"Latif","year":"2019","journal-title":"Math. Probl. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1016\/j.neucom.2020.07.139","article-title":"Recent developments of content-based image retrieval (CBIR)","volume":"452","author":"Li","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2687","DOI":"10.1109\/TCSVT.2021.3080920","article-title":"A Decade Survey of Content Based Image Retrieval Using Deep Learning","volume":"32","author":"Dubey","year":"2022","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1109\/MC.2007.239","article-title":"From Pixels to Semantic Spaces: Advances in Content-Based Image Retrieval","volume":"40","author":"Vasconcelos","year":"2007","journal-title":"Computer"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1016\/j.patcog.2006.04.045","article-title":"A survey of content-based image retrieval with high-level semantics","volume":"40","author":"Liu","year":"2007","journal-title":"Pattern Recognit."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1109\/TSMCB.2010.2086060","article-title":"Combined Mining: Discovering Informative Knowledge in Complex Data","volume":"41","author":"Cao","year":"2011","journal-title":"IEEE Trans. Syst. Man Cybern. Part B"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Tan, S.C. (2018, January 6\u20137). Improving Association Rule Mining Using Clustering-based Discretization of Numerical Data. Proceedings of the 2018 International Conference on Intelligent and Innovative Computing Applications (ICONIC), Mon Tresor, Mauritius.","DOI":"10.1109\/ICONIC.2018.8601291"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2187","DOI":"10.1007\/s13042-018-0806-9","article-title":"Dynamic optimisation based fuzzy association rule mining method","volume":"10","author":"Zheng","year":"2019","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_16","first-page":"201","article-title":"Efficient attribute selection strategies for association rule mining in high dimensional data","volume":"15","author":"Harikumar","year":"2017","journal-title":"Int. J. Comput. Sci. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.knosys.2018.04.038","article-title":"ARM\u2013AMO: An efficient association rule mining algorithm based on animal migration optimization","volume":"154","author":"Son","year":"2018","journal-title":"Knowl. Based Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.patrec.2020.05.006","article-title":"A PSO-based algorithm for mining association rules using a guided exploration strategy","volume":"138","author":"Rosas","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"9661","DOI":"10.1109\/TGRS.2020.3035676","article-title":"Deep Hashing Learning for Visual and Semantic Retrieval of Remote Sensing Images","volume":"59","author":"Song","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1109\/JSTARS.2019.2961634","article-title":"Multilabel Remote Sensing Image Retrieval Based on Fully Convolutional Network","volume":"13","author":"Shao","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.inffus.2010.02.001","article-title":"Using Multi-Modal Semantic Association Rules to fuse keywords and visual features automatically for Web image retrieval","volume":"12","author":"He","year":"2011","journal-title":"Inf. Fusion"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Alghamdi, R.A., Taileb, M., and Ameen, M. (2014, January 13\u201316). A new multimodal fusion method based on association rules mining for image retrieval. Proceedings of the MELECON 2014\u20142014 17th IEEE Mediterranean Electrotechnical Conference, Beirut, Lebanon.","DOI":"10.1109\/MELCON.2014.6820584"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Liu, J., and Liu, S. (2015, January 26\u201331). Semantic retrieval for remote sensing images using association rules mining. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7325812"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1109\/TBDATA.2019.2948924","article-title":"Exploiting Deep Features for Remote Sensing Image Retrieval: A Systematic Investigation","volume":"6","author":"Tong","year":"2020","journal-title":"IEEE Trans. Big Data"},{"key":"ref_25","unstructured":"Xu, H., Wang, J.-Y., and Mao, L. (2017, January 2\u20134). Relevance feedback for Content-based Image Retrieval using deep learning. Proceedings of the 2017 2nd International Conference on Image, Vision and Computing (ICIVC), Chengdu, China."},{"key":"ref_26","unstructured":"Buckley, C., and Salton, G. (1992, January 1). Optimization of relevance feedback weights. Proceedings of the 18th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Seattle, WA, USA."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Cox, I.J., Miller, M.L., Omohundro, S.M., and Yianilos, P.N. (1996, January 25\u201329). Pichunter: Bayesian relevance feedback for image retrieval. Proceedings of the 13th International Conference on Pattern Recognition, Vienna, Austria.","DOI":"10.1109\/ICPR.1996.546971"},{"key":"ref_28","unstructured":"Tong, S., and Chang, E. (October, January 30). Support vector machine active learning for image retrieval. Proceedings of the Ninth ACM International Conference on Multimedia, Ottawa, ON, Canada."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1016\/j.knosys.2014.10.009","article-title":"A graph-based relevance feedback mechanism in content-based image retrieval","volume":"73","author":"Kundu","year":"2015","journal-title":"Knowl.Based Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2288","DOI":"10.1109\/36.868886","article-title":"Interactive learning and probabilistic retrieval in remote sensing image archives","volume":"38","author":"Schroder","year":"2000","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Qazanfari, H., Hassanpour, H., and Qazanfari, K. (2017, January 20\u201321). A short-term learning framework based on relevance feedback for content-based image retrieval. Proceedings of the 2017 3rd Iranian Conference on Intelligent Systems and Signal Processing (ICSPIS), Shahrood, Iran.","DOI":"10.1109\/ICSPIS.2017.8311604"},{"key":"ref_32","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_33","doi-asserted-by":"crossref","first-page":"2395","DOI":"10.1080\/01431161.2011.608740","article-title":"High-resolution satellite scene classification using a sparse coding based multiple feature combination","volume":"33","author":"Sheng","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_34","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_35","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_36","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_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","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_39","doi-asserted-by":"crossref","unstructured":"Stricker, M.A., and Orengo, M. (1995, January 23). Similarity of color images. Proceedings of the Storage and Retrieval for Image and Video Databases III, San Jose, CA, USA.","DOI":"10.1117\/12.205308"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Meng, J., Yan, J., and Zhao, J. (2022). Bubble Plume Target Detection Method of Multibeam Water Column Images Based on Bags of Visual Word Features. Remote Sens., 14.","DOI":"10.3390\/rs14143296"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Kadhim, A.I. (2019, January 2\u20134). Term weighting for feature extraction on Twitter: A comparison between BM25 and TF-IDF. Proceedings of the 2019 International Conference on Advanced Science and Engineering (ICOASE), Zakho-Duhok, Iraq.","DOI":"10.1109\/ICOASE.2019.8723825"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.ins.2018.10.006","article-title":"Multi-co-training for document classification using various document representations: TF\u2013IDF, LDA, and Doc2Vec","volume":"477","author":"Kim","year":"2019","journal-title":"Inf. Sci."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/13\/5807\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:58:24Z","timestamp":1760126304000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/13\/5807"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,21]]},"references-count":42,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["s23135807"],"URL":"https:\/\/doi.org\/10.3390\/s23135807","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2023,6,21]]}}}