Pixel-based and object-based classifications using high- and medium-spatial-resolution imageries in the urban and suburban landscapes
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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.
収録刊行物
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- Geocarto International
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Geocarto International 30 (10), 1113-1129, 2015-11
Taylor & Francis
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詳細情報 詳細情報について
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- CRID
- 1050001202548214912
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- NII論文ID
- 120007136170
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- NII書誌ID
- AA10729367
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- ISSN
- 10106049
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- HANDLE
- 2241/00129249
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- 本文言語コード
- en
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- 資料種別
- journal article
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- データソース種別
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- IRDB
- CiNii Articles