, Volume 20, Issue 4, pp 629–649 | Cite as

Developing a web-based system for supervised classification of remote sensing images

  • Ziheng Sun
  • Hui Fang
  • Liping Di
  • Peng Yue
  • Xicheng Tan
  • Yuqi Bai


Web-based image classification systems aim to provide users with an easy access to image classification function. The existing work mainly focuses on web-based unsupervised classification systems. This paper proposes a web-based supervised classification system framework which includes three modules: client, servlet and service. It comprehensively describes how to combine the procedures of supervised classification into the development of a web system. A series of methods are presented to realize the modules respectively. A prototype system of the framework is also implemented and a number of remote sensing (RS) images are tested on it. Experiment results show that the prototype is capable of accomplishing supervised classification of RS images on the Web. If appropriate algorithms and parameter values are used, the results of the web-based solution could be as accurate as the results of traditional desktop-based systems. This paper lays the foundation on both theoretical and practical aspects for the future development of operational web-based supervised classification systems.


Supervised classification Geoprocessing web service Web-based processing system Remote sensing image Cyberinsfrastructure 



This research was partially supported by grants from the U.S. Department of Energy (Grant # DE-NA0001123, PI: Dr. Liping Di), U.S. National Science Foundation (Grant # ICER-1440294, PI: Dr. Liping Di), National Natural Science Foundation of China (91438203, 41271397 and 51277167) and Hubei Science and Technology Support Program (2014BAA087). The authors appreciate Ms. Julia Di of Columbia University for proofreading and improving the manuscript.


  1. 1.
    Malamas EN et al (2003) A survey on industrial vision systems, applications and tools. Image Vis Comput 21(2):171–188CrossRefGoogle Scholar
  2. 2.
    Eastman JR (2001) idridi32/r2 Guide to GIS and image processing volume 1. Clark Labs. Accessed on 30 Sept 2014
  3. 3.
    Canty MJ (2014) Image analysis, classification and change detection in remote sensing: with algorithms for ENVI/IDL and python, Third Editionth edn. CRC Press, Boca RatonGoogle Scholar
  4. 4.
    Long W III, Sriharan S (2004) Land cover classification of SSC image: unsupervised and supervised classification using ERDAS Imagine. in Proceedings of IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2004.Vol. 4, 2707–2712Google Scholar
  5. 5.
    Flanders D, Hall-Beyer M, Pereverzoff J (2003) Preliminary evaluation of eCognition object-based software for cut block delineation and feature extraction. Can J Remote Sens 29(4):441–452CrossRefGoogle Scholar
  6. 6.
    Di L (2004). Distributed geospatial information services-architectures, standards, and research issues. The international archives of photogrammetry, remote sensing, and spatial information sciences, 35(Part 2)Google Scholar
  7. 7.
    Ferran A, Bernabe S, Rodriguez PG, Plaza A (2013) A web-based system for classification of remote sensing data. IEEE J Appl Earth Obs Remote Sens 6(4):1934–1948CrossRefGoogle Scholar
  8. 8.
    Ma W, Mao K (2009) Development and application of online image processing system based on applet and JAI. Proc Int Conf EnvironSci Inf Appl Technol ESIAT 2009:382–385Google Scholar
  9. 9.
    Zhang D, Yu L, Deng C, Di L (2008) OGC WPS-based remote sensing image processing in Web environment. J Zhejiang Univ (Eng Sci) 7:018Google Scholar
  10. 10.
    Banman C (2002) Supervised and unsupervised land use classification. Notes for the Advanced Image Processing Class held by James S. Aber at Emporia State University, Emporia, Kansas, USA. Accessed 25 Jan 2014
  11. 11.
    Blaschke T (2010) Object based image analysis for remote sensing. ISPRS J Photogramm Remote Sens 65(1):2–16CrossRefGoogle Scholar
  12. 12.
    Karen LS, Prescott AP (1986) Issues in the use of kappa to estimate reliability. Med Care 24(8):733–741CrossRefGoogle Scholar
  13. 13.
    Zhang H, Fritts JE, Goldman SA (2008) Image segmentation evaluation: a survey of unsupervised methods. Comput Vis Image Underst 110(2):260–280CrossRefGoogle Scholar
  14. 14.
  15. 15.
    Graham S, Davis D, Simeonov S, Daniels G, Brittenham P, Nakamura Y, Fremantle P, König D, Zentner C (2004) Building web services with Java: making sense of XML, SOAP, WSDL, and UDDI. Sams publishingGoogle Scholar
  16. 16.
    Schut P. (Ed.) (2007) OpenGIS web processing service. OGC standard document. Accessed 10 Aug 2013
  17. 17.
    Gudgin M, Hadley M, Mendelsohn N, Moreau JJ, Nielsen HF, Karmarkar A, Lafon Y (2007) SOAP Version 1.2. W3C recommendation specificationGoogle Scholar
  18. 18.
    Christensen E, Curbera F, Meredith G, Weerawarana S (2001) Web services description language (WSDL) 1.1. W3C working draftGoogle Scholar
  19. 19.
    Hey T, Trefethen AE (2005) Cyberinfrastructure for e-Science. Science 308(5723):817–821CrossRefGoogle Scholar
  20. 20.
    Foerster TA, Brühl, Schäffer B (2011) RESTful web processing service. in Proceedings 14th AGILE International Conference on Geographic Information Science. Utrecht, NetherlandsGoogle Scholar
  21. 21.
    Di L (2005) The implementation of geospatial web services at geobrain. in Proceedings of 2005 NASA Earth Science Technology ConferenceGoogle Scholar
  22. 22.
    Zhao P, Yu G, Di L (2006) Geospatial web services. In: Hilton B.(Ed.) Emerging spatial information systems and applications. Idea Group Publishing, 1–33Google Scholar
  23. 23.
    Apache (2015) Apache Axis2 version 1.6.3. Accessed 05 Sept 2015
  24. 24.
    Yue P, Gong J, Di L, Yuan J, Sun L, Sun Z, Wang Q (2010) GeoPW: laying blocks for the geospatial processing web. Trans GIS 14(6):755–772CrossRefGoogle Scholar
  25. 25.
    Neteler M, Bowman MH, Landa M, Metz M (2012) GRASS GIS: a multi-purpose open source GIS. Environ Model Softw 31:124–130CrossRefGoogle Scholar
  26. 26.
    Li X, Di L, Han W, Zhao P, Dadi U (2010) Sharing geoscience algorithms in a Web service-oriented environment (GRASS GIS example). Comput Geosci 36(8):1060–1068CrossRefGoogle Scholar
  27. 27.
    Chopra V, Li S, Genender J (2007) Professional Apache Tomcat 6. John Wiley & SonsGoogle Scholar
  28. 28.
    Munz F (2014) Oracle WebLogic server 12c: distincitve recipes (Architecture, Administration and Development). 2nd Edition, munz&more publishingGoogle Scholar
  29. 29.
    Comaniciu D, Meer P (2002) Mean shift: a robust approach toward feature space analysis. IEEE Trans Pattern Anal Mach Intell 24(5):603–619CrossRefGoogle Scholar
  30. 30.
    Congalton RG (1991) A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens Environ 37(1):35–46CrossRefGoogle Scholar
  31. 31.
    Di L, Zhao P, Yang W, Yue P (2006) Ontology-driven automatic geospatial-processing modeling based on web-service chaining. In Proceedings of the sixth annual NASA earth science technology conference (pp. 27–29)Google Scholar
  32. 32.
    Yue P, Di L, Yang W, Yu G, Zhao P (2007) Semantics-based automatic composition of geospatial Web service chains. Comput Geosci 33(5):649–665CrossRefGoogle Scholar
  33. 33.
    Yue P, Di L, Yang W, Yu G, Zhao P, Gong J (2009) Semantic Web Services‐based process planning for earth science applications. Int J Geogr Inf Sci 23(9):1139–1163CrossRefGoogle Scholar
  34. 34.
    Juric MB, Krizevnik M (2010) WS-BPEL 2.0 for SOA Composite applications with oracle SOA Suite 11g. Packt Publishing LtdGoogle Scholar
  35. 35.
    Sun Z, Yue P (2010) The use of Web 2.0 and geoprocessing services to support geoscientific workflows. In: Proceedings of 18th International Conference on Geoinformatics, 18–20 June 2010, Beijing, China. 1–5Google Scholar
  36. 36.
    Chen A, Di L, Wei Y, Bai Y, Liu Y (2009) Use of grid computing for modeling virtual geospatial products. Int J Geogr Inf Sci 23(5):581–604CrossRefGoogle Scholar
  37. 37.
    Yu GE, Zhao P, Di L, Chen A, Deng M, Bai Y (2012) BPELPower—A BPEL execution engine for geospatial web services. Comput Geosci 47:87–101CrossRefGoogle Scholar
  38. 38.
    Garrett JJ (2005) Ajax: a new approach to web applications. Adaptive Path, Accessed 17 Aug 2015
  39. 39.
    Trimble Inc. (2016). eCognition, Accessed on 16 Feb 2016

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Ziheng Sun
    • 1
  • Hui Fang
    • 2
  • Liping Di
    • 1
  • Peng Yue
    • 2
  • Xicheng Tan
    • 3
  • Yuqi Bai
    • 4
  1. 1.Center for Spatial Information Science and Systems (CSISS)George Mason UniversityFairfaxUSA
  2. 2.Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS)Wuhan UniversityWuhanChina
  3. 3.Spatial Information and Digital Technology Department, International School of SoftwareWuhan UniversityWuhanChina
  4. 4.Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System ScienceTsinghua UniversityBeijingChina

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