Advertisement

Multi-scale object-based fuzzy classification for LULC mapping from optical satellite images

  • Hang T. DoEmail author
  • Venkatesh Raghavan
  • Luan Xuan Truong
  • Go Yonezawa
Article
  • 5 Downloads

Abstract

In this paper, a multi-scale object-based fuzzy approach is demonstrated for land use/land cover (LULC) classification using high-resolution multi-spectral optical RapidEye and IKONOS images of Lao Cai and Can Tho areas in Vietnam respectively. Optimal threshold for segmentation procedure is selected from rate of change-local variance graph. Object-based fuzzy approach is implemented to identify LULC classes and LULC initial sets, and then the initial sets are classified to final LULC classes. In case of Lao Cai area, normalized difference vegetation index (NDVI), normalized difference water index (NDWI), water index (WI) in object-based are used to generated water, terrace field classes, and built-up and vegetation sets. NDVI, soil index (SI) and red band are used to distinguish built-up set to building, bare land and road classes. NDVI and RedEgde band are inputs to classify rice field and forest classes from vegetation set. In case of Can Tho area, NDWI and WI are generated to water, vegetation, paddy field classes and built-up set, and then built-up set is classified to building, bare land, road, and paddy field classes. The technique is able to create LULC maps of Lao Cai and Can Tho areas with (90.8%, 0.84), and (92.3%, 0.90) classification accuracy and kappa coefficient, correspondingly.

Keywords

Fuzzy LULC Local variance Multi-scale segment Object-based GRASS GIS 

Notes

Acknowledgements

We are deeply grateful to Dr. Ho Dinh Duan and Dr. Vinayaraj Poliyapram for their comments which are valuable in improving the manuscript. We also thank the anonymous reviewers for their critical and constructive suggestions. The first author would like to express gratitude to Nishimura International Scholarship Foundation (NISF) for award of fellowship to pursue her doctoral research.

Author Contributions

This research was mainly prepared and performed by HTD and VR. HTD and VR contributed with ideas and designing the data processing workflow. LXT and GY provided inputs about data processing methodology and field validation of results and revising of the manuscript.

Supplementary material

41324_2019_240_MOESM1_ESM.docx (1.9 mb)
Supplementary material 1 (DOCX 1986 kb)

References

  1. 1.
    Trincsi, K., Pham, T. T. T., & Turner, S. (2014). Mapping mountain diversity: Ethnic minorities and land use land cover change in Vietnam’s borderlands. Land Use Policy, 41, 484–497.Google Scholar
  2. 2.
    Rogan, J., & Chen, D. M. (2003). Remote sensing technology for mapping and monitoring land-cover and land-use change. Progress in Planning, 61, 301–325.Google Scholar
  3. 3.
    Shackelford, A. K., & Davis, C. H. (2003). A hierarchical fuzzy classification approach for high-resolution multispectral data over urban areas. IEEE Transactions on Geoscience and Remote Sensing, 41(9), 1920–1932.Google Scholar
  4. 4.
    Schowengerdt, R. A. (2007). Remote sensing: models and methods for image processing (3rd ed.). New York: Elsevier.Google Scholar
  5. 5.
    Blaschke, T., Burnett, C., & Pekkarinen, A. (2004). Image segmentation methods for object-based analysis and classification. In S. M. D. Jong & F. D. V. Meer (Eds.), Remote sensing image analysis: including the spatial domain. Remote sensing and digital image processing (Vol. 5). Dordrecht: Springer.Google Scholar
  6. 6.
    Benz, U. C., Hofmann, P., Willhauck, G., Lingenfelder, I., & Heynen, M. (2004). Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS Journal of Photogrammetry and Remote Sensing, 58(3–4), 239–258.Google Scholar
  7. 7.
    Van der Werff, H. M. A., & Van der Meer, F. D. (2008). Shape-based classification of spectrally identical objects. ISPRS Journal of Photogrammetry and Remote Sensing, 63(2), 251–258.Google Scholar
  8. 8.
    Wuest, B., & Zhang, Y. (2009). Region based segmentation of QuickBird multispectral imagery through band ratios and fuzzy comparison. ISPRS Journal of Photogrammetry and Remote Sensing, 64(1), 55–64.Google Scholar
  9. 9.
    Gamanya, R., De Mmaeyer, P., & De Dapper, M. (2009). Object-oriented change detection for the city of Harare, Zimbabwe. Expert Systems with Applications, 36(1), 571–778.Google Scholar
  10. 10.
    Dragut, L., Tiede, D., & Levick, S. R. (2010). ESP: A tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data. International Journal of Geographical Information Science, 24(6), 859–871.Google Scholar
  11. 11.
    Myint, S. W., Gober, P., Brazel, A., Grossman-Clarke, S., & Weng, Q. (2011). Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sensing of Environment, 115(5), 1145–1161.Google Scholar
  12. 12.
    Lizarazo, I., & Barros, J. (2010). Fuzzy image segmentation for urban land-cover classification. Photogrammetric Engineering & Remote Sensing, 76(2), 151–162.Google Scholar
  13. 13.
    Wood, T. F., & Foody, G. M. (1993). Using cover-type likelihoods and typicalities in a geographic information system data structure to map gradually changing environments. In R. Haines-Young, D. R. Green & S. H.Cousins (Eds.), Landscape ecology and GIS (pp. 141–146). London: Taylor and Francis.Google Scholar
  14. 14.
    Foody, G. M. (1995). Cross-entropy for the evaluation of the accuracy of a fuzzy land cover classification with fuzzy ground data. ISPRS Journal of Photogrametry and Remote Sensing, 50, 2–12.Google Scholar
  15. 15.
    Wang, F. (1990). Improve remote sensing imagery analysis through fuzzy information representation. Photogrammetric Engineering and Remote Sensing, 56, 1163–1169.Google Scholar
  16. 16.
    Satellite Imaging Corporation. RapidEye satellite sensors. https://www.satimagingcorp.com/satellite-sensors/other-satellite-sensors/rapideye/. Accessed 16 December 2018.
  17. 17.
    Tigges, J., Lakes, T., & Hostert, P. (2013). Urban vegetation classification: Benefit of multitemporal RapidEye satellite data. Remote Sensing of Environment, 136, 66–75.Google Scholar
  18. 18.
    Kross, A., McNairn, H., Lapen, D., Sunohara, M., & Champagne, C. (2015). Assessment of RapidEye vegetation indices for estimation of leaf area index and biomass in corn and soybean crops. International Journal of Applied Earth Observation and Geoinformation, 34, 235–248.Google Scholar
  19. 19.
    Satellite Imaging Corporation. IKONOS satellite sensor. https://www.satimagingcorp.com/satellite-sensors/ikonos/. Accessed 16 December 2018.
  20. 20.
    Kushida, K., Yongwon, K., Tsuyuzaki, S., & Fukuda, M. (2009). Spectral vegetation indices for estimating shrub cover, green phytomass and leaf turnover in a sedge-shrub tundra. International Journal of Remote Sensing, 30(6), 1651–1658.Google Scholar
  21. 21.
    Gao, B. C. (1996). NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58, 257–266.Google Scholar
  22. 22.
    Yamagata, Y., Sugita, M., & Yasuoka, Y. (1997). Development of Vegetation-Soil-Water Index algorithms and applications. Journal of the Remote Sensing Society of Japan, 17(1), 54–64.Google Scholar
  23. 23.
    Shao, P., Yang, G., Niu, X., Zhang, X., Zhan, F., & Tang, T. (2014). Information extraction of high-resolution remotely sensed image based on multiresolution segmentation. Sustainability, 6, 5300–5310.Google Scholar
  24. 24.
    Hay, G. J., Marceau, D. J., Dube, P., & Bouchard, A. (2004). A multi-scale framework for landscape analysis: Object-specific analysis and up scaling. Landscape Ecology, 16(6), 471–490.Google Scholar
  25. 25.
    Haralick, R. M., & Shapiro, L. G. (1985). Image segmentation techniques. Computer Vision, Graphics, and Image Processing, 29(1), 100–132.Google Scholar
  26. 26.
    Fu, K. S., & Mui, J. K. (1981). A survey on image segmentation. Pattern Recognition, 13(1), 3–16.Google Scholar
  27. 27.
    Woodcock, C. E., & Strahler, A. H. (1987). The factor of scale in remote sensing. Remote Sensing of Environment, 21(3), 311–332.Google Scholar
  28. 28.
    Kim, M., Madden, M., & Warner, T. (2008). Estimation of optimal image object size for the segmentation of forest stands with multispectral IKONOS imagery. In T. Blaschke, S. Lang, & G. J. Hay (Eds.), Object-based image analysis—Spatial concepts for knowledge driven remote sensing applications (pp. 291–307). Berlin: Springer.Google Scholar
  29. 29.
    Dragut, L., Csillik, O., Eisank, C., & Tiede, D. (2014). Automated parameterization for multi-scale image segmentation on multiple layers. ISPRS Journal of Photogrammetry and Remote Sensing, 88, 119–127.Google Scholar
  30. 30.
    Bauer, R. J., & Dahlquist, J. R. (1998). Technical market indicators: Analysis and performance. New York: Wiley.Google Scholar
  31. 31.
    Osgeo.org. PyGRASS documentation. https://grass.osgeo.org/grass70/manuals/libpython/pygrass_index.html. Accessed 25 December 2018.
  32. 32.
    Zhang, J., & Foody, G. M. (1998). A fuzzy classification of sub-urban land cover from remote sensed imagery. International Journal of Remote Sensing, 19(14), 2721–2738.Google Scholar
  33. 33.
    Scherer, R. (2012). Multiple fuzzy classification systems. Heidelberg: Springer.Google Scholar
  34. 34.
    Jasiewicz, J. (2010). A new GRASS GIS fuzzy inference system for massive data analysis. Computers & Sciences.  https://doi.org/10.1016/j.cageo.2010.09.008.Google Scholar
  35. 35.
    Neubert, M., Herold, H., & Meinel, G. (2008). Assessing image segmentation quality—Concepts, methods and application. In T. Blaschke, S. Lang, & G. J. Hay (Eds.), Object-based image analysis. Lecture notes in geoinformation and cartography. Berlin: Springer.Google Scholar

Copyright information

© Korean Spatial Information Society 2019

Authors and Affiliations

  1. 1.Osaka City UniversityOsakaJapan
  2. 2.Hanoi University of Mining and GeologyHanoiVietnam

Personalised recommendations