{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T06:20:25Z","timestamp":1762323625560,"version":"build-2065373602"},"reference-count":54,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2013,6,7]],"date-time":"2013-06-07T00:00:00Z","timestamp":1370563200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Associative methods for content-based image ranking by semantics are attractive due to the similarity of generated models to human models of understanding. Although they tend to return results that are better understood by image analysts, the induction of these models is difficult to build due to factors that affect training complexity, such as coexistence of visual patterns in same images, over-fitting or under-fitting and semantic representation differences among image analysts. This article proposes a methodology to reduce the complexity of ranking satellite images for associative methods. Our approach employs genetic operations to provide faster and more accurate models for ranking by semantic using low level features. The added accuracy is provided by a reduction in the likelihood to reach local minima or to overfit. The experiments show that, using genetic optimization, associative methods perform better or at similar levels as  state-of-the-art ensemble methods for ranking. The mean average precision (MAP) of ranking by semantic was improved by 14% over similar associative methods that use other optimization techniques while maintaining smaller size for each semantic model.<\/jats:p>","DOI":"10.3390\/ijgi2020531","type":"journal-article","created":{"date-parts":[[2013,6,10]],"date-time":"2013-06-10T03:32:11Z","timestamp":1370835131000},"page":"531-552","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Genetic Optimization for Associative Semantic Ranking Models of Satellite Images by Land Cover"],"prefix":"10.3390","volume":"2","author":[{"given":"Adrian","family":"Barb","sequence":"first","affiliation":[{"name":"Great Valley School of Professional Studies, Penn State University, Malvern, PA 19355, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nil","family":"Kilicay-Ergin","sequence":"additional","affiliation":[{"name":"Great Valley School of Professional Studies, Penn State University, Malvern, PA 19355, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2013,6,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1109\/TGRS.2005.843253","article-title":"Human-centered concepts for exploration and understanding of earth observation images","volume":"43","author":"Datcu","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.isprsjprs.2007.09.001","article-title":"A genetic algorithm rule-based approach for land-cover classification","volume":"63","author":"Tseng","year":"2008","journal-title":"ISPRS J. Photogramm."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/j.compenvurbsys.2009.11.001","article-title":"Spatial data mining and geographic knowledge discovery\u2014An introduction","volume":"33","author":"Mennis","year":"2009","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_4","unstructured":"Datcu, M., and Seidel, K. (2000, January 18\u201325). Image Information Mining: Exploration of Image Content in Large Archives. Proceedings of 2000 IEEE Aerospace Conference, Big Sky, MT, USA."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1023\/A:1015508302797","article-title":"Image mining: Trends and developments","volume":"19","author":"Hsu","year":"2002","journal-title":"J. Intell. Inf. Syst."},{"key":"ref_6","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 Recogn."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1109\/LGRS.2009.2014083","article-title":"Image mining using directional spatial constraints","volume":"7","author":"Aksoy","year":"2010","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1109\/LGRS.2009.2017214","article-title":"Visual-semantic modeling in content-based geospatial information retrieval using associative mining techniques","volume":"7","author":"Barb","year":"2010","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"698","DOI":"10.1109\/JSTARS.2010.2058794","article-title":"A semi-supervised algorithm for auto-annotation and unknown structures discovery in satellite image databases","volume":"3","author":"Blanchart","year":"2010","journal-title":"IEEE J. Select. Topics Appl. Earth Observ. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1109\/JSTARS.2010.2081349","article-title":"Bridging the semantic gap for satellite image annotation and automatic mapping applications","volume":"4","author":"Bratasanu","year":"2011","journal-title":"IEEE J. Select. Topics Appl. Earth Observ. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2563","DOI":"10.1109\/TGRS.2005.847908","article-title":"Semantics-enabled framework for knowledge discovery from earth observation data archives","volume":"43","author":"Durbha","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4036","DOI":"10.1109\/TGRS.2012.2187353","article-title":"Multi-index multi-object content-based retrieval","volume":"50","author":"Klaric","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1109\/LGRS.2009.2023536","article-title":"Semantic annotation of satellite images using latent dirichlet allocation","volume":"7","author":"Lienou","year":"2010","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1109\/TGRS.2006.890580","article-title":"Detecting man-made structures and changes in satellite imagery with a content-based information retrieval system built on self-organizing maps","volume":"45","author":"Molinier","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1603","DOI":"10.1109\/TGRS.2010.2088404","article-title":"Entropy-balanced bitmap tree for shape-based object retrieval from large-scale satellite imagery databases","volume":"49","author":"Scott","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","first-page":"4687","DOI":"10.1109\/TGRS.2011.2152847","article-title":"Clustering of detected changes in high-resolution satellite imagery using a stabilized competitive agglomeration algorithm","volume":"49","author":"Sjahputera","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1415","DOI":"10.1080\/13658816.2011.635595","article-title":"Semantic similarity measurement based on knowledge mining: An artificial neural net approach","volume":"26","author":"Li","year":"2012","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Agrawal, R., Imielinski, T., and Swami, A. (1993, January 26\u201328). Mining Association Rules between Sets of Items in Large Databases. Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data (SIGMOD \u201993), Washington, DC, USA.","DOI":"10.1145\/170035.170072"},{"key":"ref_20","unstructured":"Agrawal, R., and Srikant, R. (1994, January 12\u201315). Fast Algorithms for Mining Association Rules in Large Databases. Proceedings of the 20th International Conference on Very Large Data Bases (VLDB \u201994), San Francisco, CA, USA."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1111\/1467-9671.00133","article-title":"Fuzzy set approaches to spatial data mining of association rules","volume":"7","author":"Ladner","year":"2003","journal-title":"Trans. GIS"},{"key":"ref_22","first-page":"56","article-title":"Using fuzzy SOM strategy for satellite image retrieval and information mining","volume":"6","author":"Huang","year":"2008","journal-title":"J. Syst. Cybern. Inf."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1017\/S0269888907001026","article-title":"A review of associative classification mining","volume":"22","author":"Thabtah","year":"2007","journal-title":"Knowl. Eng. Rev."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning, Ser, Springer.","DOI":"10.1007\/978-0-387-84858-7"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/S0004-3702(97)00063-5","article-title":"Selection of relevant features and examples in machine learning","volume":"97","author":"Blum","year":"1997","journal-title":"Artif. Intell."},{"key":"ref_26","unstructured":"Li, W., Han, J., and Pei, J. (December, January 29). CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules. Proceeding of the IEEE International Conference on Data Mining (ICDM 2001), San Jose, CA, USA."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1613\/jair.279","article-title":"Improved use of continuous attributes in C4.5","volume":"4","author":"Quinlan","year":"1996","journal-title":"J. Artif. Intell. Res."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1119","DOI":"10.1016\/0167-8655(94)90127-9","article-title":"Floating search methods in feature selection","volume":"15","author":"Pudil","year":"1994","journal-title":"Pattern Recogn. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1370","DOI":"10.1016\/j.datak.2009.07.002","article-title":"Supporting content-based image retrieval and computer-aided diagnosis systems with association rule-based techniques","volume":"68","author":"Ribeiro","year":"2009","journal-title":"Data Knowl. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Maimon, O., and Rokach, L. (2010). Data Mining and Knowledge Discovery Handbook, Springer.","DOI":"10.1007\/978-0-387-09823-4"},{"key":"ref_31","unstructured":"Goldberg, D.E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley Longman Publishing Co.,Inc.. [1st]."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Holland, J.H. (1992). Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, The MIT Press.","DOI":"10.7551\/mitpress\/1090.001.0001"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1016\/j.compenvurbsys.2009.07.007","article-title":"Evaluation of the use of spectral and textural information by an evolutionary algorithm for multi-spectral imagery classification","volume":"33","author":"Momm","year":"2009","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1016\/j.compenvurbsys.2009.10.004","article-title":"Predicting air pollution using fuzzy genetic linear membership kriging in GIS","volume":"33","author":"Shad","year":"2009","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhang, X., Wang, J., Wu, F., Fan, Z., and Li, X. (2006, January 16\u201318). A Novel Spatial Clustering with Obstacles Constraints Based on Genetic Algorithms and k-Medoids. Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications (ISDA \u201906), Washington, DC, USA.","DOI":"10.1109\/ISDA.2006.75"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Gao, L., Dai, S., Zheng, S., and Yan, G. (2007, January 24\u201327). Using Genetic Algorithm for Data Mining Optimization in an Image Database. Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007), Haikou, Hainan, China.","DOI":"10.1109\/FSKD.2007.603"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1506","DOI":"10.1109\/TGRS.2007.892604","article-title":"Multiobjective genetic clustering for pixel classification in remote sensing imagery","volume":"45","author":"Bandyopadhyay","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"De Stefano, C., Fontanella, F., and Marrocco, C. (2008, January 26). A GA-Based Feature Selection Algorithm for Remote Sensing Images. Proceedings of the 2008 Conference on Applications of Evolutionary Computing (Evo\u201908), Naples, Italy.","DOI":"10.1007\/978-3-540-78761-7_29"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"810","DOI":"10.1016\/j.dss.2011.01.015","article-title":"Improving the ranking quality of medical image retrieval using a genetic feature selection method","volume":"51","author":"Ribeiro","year":"2011","journal-title":"Decis. Support Syst."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"963","DOI":"10.1109\/LGRS.2012.2187513","article-title":"On the use of the genetic algorithm filter-based feature selection technique for satellite precipitation estimation","volume":"9","author":"Mahrooghy","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_41","unstructured":"Barb, A.S., and Barb, C.S. (2012, January 24\u201326). Genetic Methods for Associative Semantic Ranking of Landsat Image Regions by Land Cover. Proceedings of Image Information Mining Workshop, Oberpfaffenhofen, Germany."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Syswerda, G. (1990, January 15\u201318). A Study of Reproduction in Generational and Steady-State Genetic Algorithms. Proceeding of the First Workshop on Foundations of Genetic Algorithms, Bloomington Campus, IN, USA.","DOI":"10.1016\/B978-0-08-050684-5.50009-4"},{"key":"ref_43","unstructured":"Vavak, F., and Fogarty, T. (1996, January 20\u201322). Comparison of Steady State and Generational Genetic Algorithms for Use in Nonstationary Environments. Proceedings of IEEE International Conference on Evolutionary Computation, Nagoya, Japan."},{"key":"ref_44","unstructured":"Davis, L. (1991). Handbook of Genetic Algorithms, Van Nostrand Reinhold."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1007\/s00180-006-0250-2","article-title":"A fast algorithm for balanced sampling","volume":"21","author":"Chauvet","year":"2006","journal-title":"Comput. Stat."},{"key":"ref_46","unstructured":"The Wisconsin Regional Orthophotography Consortium (WROC). Available online:http:\/\/www.ncwrpc.org\/WROC\/."},{"key":"ref_47","unstructured":"Asuncion, A., and Newman, D.J. UCI Machine Learning Repository. Available online:http:\/\/www.ics.uci.edu\/\u223cmlearn\/MLRepository.html."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Zellner, A., Keuzenkamp, H., and McAleer, M. (2002). What is the Problem of Simplicity?, Cambridge University Press.","DOI":"10.1017\/CBO9780511493164.001"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Bishop, C. (1996). Neural Networks for Pattern Recognition, Oxford University Press. [1st].","DOI":"10.1201\/9781420050646.ptb6"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1006\/jcss.1997.1504","article-title":"A decision-theoretic generalization of on-line learning and an application to boosting","volume":"55","author":"Freund","year":"1997","journal-title":"J. Comput. Syst. Sci."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1007\/s10994-005-0466-3","article-title":"Logistic model trees","volume":"59","author":"Landwehr","year":"2005","journal-title":"Mach. Learn."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"4316","DOI":"10.1109\/TIT.2009.2025558","article-title":"Tree-based ranking methods","volume":"55","author":"Clemencon","year":"2009","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_53","unstructured":"Available online:http:\/\/www.R-project.org."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v036.i11","article-title":"Feature selection with the Boruta package","volume":"36","author":"Kursa","year":"2010","journal-title":"J. Stat. Softw."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/2\/2\/531\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:47:14Z","timestamp":1760219234000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/2\/2\/531"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2013,6,7]]},"references-count":54,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2013,6]]}},"alternative-id":["ijgi2020531"],"URL":"https:\/\/doi.org\/10.3390\/ijgi2020531","relation":{},"ISSN":["2220-9964"],"issn-type":[{"type":"electronic","value":"2220-9964"}],"subject":[],"published":{"date-parts":[[2013,6,7]]}}}