SEARCH RESULT

By the development of technology, image classification algorithms frequently use to identify land use and land cover of any area in remote sensing studies. Due to the diversity and complexity of land cover on the wetland areas, it is quite difficult to obtain accurate results related to the earth's surface. The main purpose of this research is to compare the overall accuracies of object based and pixel based image classification methods. Arslantaş Dam Lake is structured on Ceyhan River for irrigation, flood control and electricity generation in Osmaniye province. In this study, Landsat-8 LDCM satellite image of Aslantaş Dam Lake with spatial resolution of 30m, acquired on December 29, 2017 was used. Firstly, image was classified by pixel based classification with support vector machines (SVM) method. After that, image was reclassified by object based classification with K-nearest neighbour (KNN) method. Five classes namely lake, agricultural area, soil, vegetation and building area were determined by using these algorithms. Ground truth data were gathered from aerial photographs, available maps and personal informations. Finally, overall accuracies of these methods were compared. It is observed from the classification results that object based KNN method provide higher accuracy than the other classification method.

International Conference on Advanced Technologies, Computer Engineering and Science
ICATCES

Mustafa Hayri Kesikoğlu Sevim YASEMİN ÇİÇEKLİ Tolga Kaynak

With the development of technology, remote sensing is commonly used for ecological studies and monitoring wetland and management. Artificial Neural Networks are extremely simplified model of the brain occurring by neurons and layers connecting to neurons so artificial neural networks method is frequently used to classify satellite images. In this study, Landsat5 satellite image with spatial resolution of 30m, acquired on October 29, 2007 and Landsat-8 acquired on November 27, 2017 were used to identified the coastline changes at Kozan Dam Lake. The lake is used as drinking and irrigation water. Therefore, it is very important to examine the coastline changes of the lake. In first step, image to image registration was made to conform image coordinate systems of images to each other. Second step, images were classified by artificial neural networks method. Four classes namely lake, agricultural area, soil, and vegetation area were determined. Third step, image classification accuracies were determined. Finally, the changes in coastline of Dam Lake were calculated by post classification comparison method. Coastline change of Dam Lake was calculated as 0.6 km2 increase and the change image map was created. At the end of the study Kozan Dam Lake coastline changes were monitored by using remote sensing methods.

International Conference on Advanced Technologies, Computer Engineering and Science
ICATCES

Tolga Kaynak Sevim YASEMİN ÇİÇEKLİ Mustafa Hayri Kesikoğlu