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Kim, M., Madden, M. and Warner, M. (2008) Estimation of Optimal Image Object Size for the Segmentation of Forest Stands with Multispectral IKONOS Imagery. In: Blaschke, T., Lang, S. and Hay, G.J., Eds., Eds., Object-Based Image Analysis, Springer, Berlin, 292-307. https://doi.org/10.1007/978-3-540-77058-9_16

has been cited by the following article:

• TITLE: The Building Extraction Based on Object Oriented Classification Method in High Vegetation Coverage Area

  AUTHORS: Baoying Ye, Nisha Bao

  KEYWORDS: Oriented Classification, High Vegetation Coverage Area, Building

  JOURNAL NAME: Journal of Computer and Communications, Vol.7 No.7, July 10, 2019

  ABSTRACT: Quickly extraction of building information technology is an important application in urban development planning, electronic information, national defense and others. This paper takes Landsat-8 multispectral and panchromatic data as data source, using the local variance method to select the optimal segmentation scale, normalized difference vegetation index (NDVI) and the normalized building index (NDBI) and panchromatic brightness value of an object oriented classification rule extraction. The high vegetation coverage area of buildings, and through the spatial relationships and distinguishing feature of collections of buildings independent buildings and villages. The results showed that Google earth high resolution image analysis and accuracy evaluation. the results of the extraction based on the overall accuracy of village extraction was 83%, the accuracy of extraction of independent buildings was 70%, according to the L8 remote sensing data, object oriented classification method can quickly and accurately extract the high vegetation coverage area of the building.