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

Estoque, Ronald C.  ,  Murayama, Yuji  ,  Mizutani Akiyama, Chiaki

30 ( 10 )  , pp.1113 - 1129 , 2015-11 , Taylor & Francis
ISSN:1010-6049
NCID:AA10729367
Description
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.
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