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Segmentation of Satellite Images of the Earth's surface using Neural Network Technologies

机译:使用神经网络技术分割地球表面卫星图像

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The authors present an algorithm for segmentation of satellite images based on the U-net convolutional neural network. It includes the following stages: preparation of input data, modification of the structure of a convolutional neural network (CNN) taking into account the parameters of the studied images, training CNN on the formed training sample, and segmentation of test images. The authors suggest procedures for optimizing the CNN training time and increasing the accuracy of the selected classes of objects with a limited training sample of images and computing resources. The results of experimental studies that confirm the effectiveness of the method are presented.
机译:作者呈现了一种基于U-Net卷积神经网络的卫星图像分割算法。它包括以下阶段:编写输入数据,考虑到所研究的图像的参数,在形成的训练样本上训练CNN的参数,以及测试图像的分割,修改卷积神经网络的结构(CNN)。作者建议优化CNN培训时间并增加所选对象的准确性的程序,其中具有有限的图像和计算资源的训练样本。提出了确认该方法的有效性的实验研究结果。

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