Pixel-based and object-based classifications using high- and medium-spatial-resolution imageries in the urban and suburban landscapes

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Abstract

With the increasing availability of high-spatial-resolution remote sensing imageries and with the observed limitations of pixel-based techniques, the development and testing of geographic object-based image analysis (GEOBIA) techniques for image classification have become one of the main research areas in geospatial science. This paper examines and compares the classification performance of a pixel-based method and an object-based method as applied to high- (QuickBird satellite image) and medium- (Landsat TM image) spatial-resolution imageries in the context of urban and suburban landscapes. For the pixel-based classification, the maximum-likelihood supervised classification approach was employed. And for the object-based classification, the pixel-based classified maps were integrated with a set of image segments produced using various calibrations. The results show evidence that the object-based method can produce classifications that are more accurate for both high- and medium-spatial- resolution imageries in the context of urban and suburban landscapes.

Journal

  • Geocarto International

    Geocarto International 30(10), 1113-1129, 2015-11

    Taylor & Francis

Codes

  • NII Article ID (NAID)
    120005668316
  • NII NACSIS-CAT ID (NCID)
    AA10729367
  • Text Lang
    ENG
  • Article Type
    journal article
  • ISSN
    1010-6049
  • Data Source
    IR 
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