Abstract
Recently, convolutional neural networks have grown in popularity in a variety of fields, such as computer vision and audio and text processing. This importance is due to the performance of this type of neural network in the state of the art, and in a wide variety of disciplines. However, the use of convolutional neural networks has not been widely used for remote sensing applications until recently. In this paper, we propose a CNN-based system capable of efficiently extracting buildings from very high-resolution satellite images, by combining the performances of the two architectures; U-Net and VGG19, which is obtained by putting two blocks in parallel based mainly on U-Net: The first block is a standard U-Net, and the second is designed by replacing the contraction path of standard U-Net with the pre-trained weights of VGG19.
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