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The determination of coastline changes using artificial neural networks in Yamula Dam Lake, Turkey

机译:土耳其亚马拉坝湖中人工神经网络的海岸线变化的确定

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Yamula Dam Lake is an important area constructed for the purpose of producing hydroelectric energy and irrigation. In this study, the coastline boundary changes occurred in a part of Yamula Dam Lake in Kayseri province were examined using three multispectral Landsat 8 LDCM satellite images of March, August and November 2016. Firstly, image-to-image registration process was performed to conform the image coordinate systems of images to each other. The radiometric calibration process wasn't done, since there was not any process related to the reflectance and radiance values when determining the coastline boundary change. Then, each satellite image was classified into two information classes, namely water and other fields by using artificial neural network method. The change images were created for MarchAugust and August-November pairs by using the obtained classification images. The changes in coastline boundary were determined by the post classification comparison method. Consequently, bi-directional changes from water to land and from land to water were detected in Yamula Dam Lake.
机译:Yamula Dam Lake是一个重要的领域,用于生产水力电能和灌溉。在这项研究中,使用三个MultiSpectral Landsat 8 LDCM卫星图像在2016年8月和11月的三个LDCM卫星图像进行了亚马拉坝湖中发生了海岸线边界变化。首先,对图像到图像登记过程进行了符合彼此的图像的图像坐标系。辐射校准过程未完成,因为在确定海岸线边界变化时没有任何与反射率和辐射值相关的过程。然后,通过使用人工神经网络方法将每个卫星图像分为两个信息类别,即水和其他领域。通过使用所获得的分类图像,为Marchaugust和8月至11月对创建了更改图像。海岸线边界的变化由后分类比较方法确定。因此,在Yamula Dam湖中检测到从水到陆地和陆地到水的双向变化。

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