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fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
L. Zhai; J. Sun; H. Sang; G. Yang; Y. Jia (2012)
Publisher: Copernicus Publications
Journal: The International Archives of the Photogrammetry
Languages: English
Types: Article
Subjects: TA1-2040, T, TA1501-1820, Applied optics. Photonics, Engineering (General). Civil engineering (General), Technology

Classified by OpenAIRE into

ACM Ref: ComputingMethodologies_PATTERNRECOGNITION
Traditional land classification techniques for large areas that use LANDSAT TM imagery are typically limited to the fixed spatial resolution of the sensors. For modeling habitat characteristics is often difficult when a study area is large and diverse and complete sampling of environmental variables is unrealistic. We also did some researches on this field, in this paper we firstly introduced the decision tree classification based on C5.0, and then introduced the classification workflow. The study results were compared with the Maximum Likelihood Classification result. Victoria of Australia was as the study area, the LANDSAT ETM+ images were used to classify. Experiments show that the decision tree classification method based on C5.0 is better.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Liang ZHAI, Wenhan XIE, Huiyong SANG , Jinping SUN. Land cover mapping with Landsat data: The Tasmania case study. The 2011 International Symposium on Image and Data Fusion, 9-11 August 2011, Tengchong, Yunnan, China.
    • S. M. JOY, A non-parametric, supervised classification of vegetation types on the Kaibab National Forest using decision trees. International Journal of Remote Sensing, 2003, vol.24, NO.9, 1835-1852.
    • Perera, K. and Tsuchiya, K., 2009. Experiment for mapping land cover and it's change in southeastern Sri Lanka utilizing 250m resolution MODIS imageries.
    • Advances in Space Research, 43 (9). pp. 1349-1355.
    • Heinl, M., Walde, J., Tappeiner, G., and Tappeiner U., 2009. Classifiers vs. input variables-The drivers in image classification for land cover mapping.
    • International Journal of Applied Earth Observation and Geoinformation, 11(6). pp. 423-430.
    • Foody, G. M., 2002. Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80(1). pp. 185-201.
    • Lu D. and Weng Q., 2007. A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28(5). pp. 823-870.
    • Herold, M., Mayaux, P., Woodcock, C.E., Baccini, A., and Schmullius, C., 2008. Some challenges in global land cover mapping: An assessment of agreement and accuracy in existing 1 km datasets. Remote Sensing of Environment, 112(5). pp. 2538-2556.
    • O., Zhu, Z., Yang, L., and Merchant, J. W., (2000). Development of a global land cover Higher resolution Global Land Cover Mapping Project. http://www.globallandcover.com/, July, 1, 2011.
  • No related research data.
  • No similar publications.