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SETSCI - Volume (2018)
ISAS 2018 - Ist International Symposium on Innovative Approaches in Scientific Studies, Kemer-Antalya, Turkey, Apr 11, 2018

A Brief Review on Object Oriented Image Classification Methods for Information Extraction from Satellite and Aerial Images on Land Use/Cover Applications (ISAS 2018_92)
Mustafa Hayri Kesikoğlu1, Sevim Yasemin Çiçekli2*, Tolga Kaynak3
1Harita Mühendisliği Bölümü, Erciyes Üniversitesi , Kayseri, Turkey
2Harita Mühendisliği Bölümü, Çukurova Üniversitesi , Adana, Turkey
3Harita Mühendisliği Bölümü, Erciyes Üniversitesi, Kayseri, Turkey
* Corresponding author: yoturanc@cu.edu.tr
Published Date: 2018-06-23   |   Page (s): 97-97   |    127     7

ABSTRACT Over the years, developments in the world of science and technology have emerged. Developments in image classification methods can also be evaluated in this context. Nowadays, the use of image classification methods for extracting information from data obtained by remote sensing and photogrammetric methods and involving largescale areas, is often preferred. Image classification is used in various studies such as determination of land use / cover, determination of temporal changes in coastal change areas, determination of product variety in agricultural areas. The image classification is basically divided into two to be pixel based and object based. In the pixel based classification, classification is made by each pixel, whereas in the object based classification, the classification is made on the objects formed by combining the pixels. Especially since the beginning of the 2000's, the importance of object based classification has been increased thanks to the development of remote sensing and photogrammetry. In addition, while looking at made comparison studies, it has been seen that the object based approach gives better results depending on the feature of data used in classification. In the context of this study, studies done up to now in the extraction of the land use/cover with object based classification methods have been observed.  
KEYWORDS Image classification, object based classification, land use/cover, photogrammetry, remote sensing

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