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Improvement Of Land Cover Classification With The Integration Of Topographical Data In Uneven Terrain

The aim of this study is to develop a framework for the integration of ancillary topographic information into supervised image classification to improve the accuracy of the classification product. Integration of topographic data into classification is basically through modification of training set in order to provide additional sensitivity to topographical characteristics associated with each land cover class in the study area. Multi-spectral Landsat 7 ETM 30x30 meter bands are the remotely sensed data used in the study. Ancillary topographic data are elevation, slope and aspect derived from 1/25000 scaled topographic map contours. A five-phase methodological framework was proposed for developing procedures for the integration of topographical data into a standard image classification task. Briefly / first phase is the selection of initial class spectral signatures, second phase is analyzing the information content of class spectral signatures and topographical data for a potential relationship, and quantification of the related topographical data. Third phase is the selection of class topographical signatures from the related topographical data. Fourth phase is redefinition of two training sets where one of which includes spectral information
only and the other includes both spectral and topographical information. The last phase is classification. Two products were derived where, first product used bands as input and was trained by spectral information only and the second was
the product for which bands and topographical data was used as input and it was trained with both spectral and topographical information. Method was applied to image and associated ancillary topographical data covering rural lands mainly composed of agricultural practices and rangelands
in Ankara. Method provided an improvement of 10% in overall accuracy for the classification with the integration of topographical data compared to that depended only on spectral data from remotely sensed images.

Identiferoai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12606599/index.pdf
Date01 November 2002
CreatorsGercek, Deniz
ContributorsToprak, Vedat
PublisherMETU
Source SetsMiddle East Technical Univ.
LanguageEnglish
Detected LanguageEnglish
TypeM.S. Thesis
Formattext/pdf
RightsTo liberate the content for public access

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