@article{oai:tsukuba.repo.nii.ac.jp:00035821,
 author = {村山, 祐司 and Estoque, Ronald C. and Murayama, Yuji and Mizutani Akiyama, Chiaki},
 issue = {10},
 journal = {Geocarto International},
 month = {Nov},
 note = {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.},
 pages = {1113--1129},
 title = {Pixel-based and object-based classifications using high- and medium-spatial-resolution imageries in the urban and suburban landscapes},
 volume = {30},
 year = {2015}
}