Spatial Information Research

, Volume 27, Issue 2, pp 247–257 | Cite as

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

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


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.


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



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)


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Copyright information

© Korean Spatial Information Society 2019

Authors and Affiliations

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

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