A Modular System Based on U-Net for Automatic Building Extraction from very high-resolution satellite images

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...

Full description

Saved in:
Bibliographic Details
Published in:E3S Web of Conferences 2022-01, Vol.351, p.1071
Main Authors: El Asri, Smail Ait, El Adib, Samir, Negabi, Ismail, Raissouni, Naoufal
Format: Article
Language:English
Subjects:
Artificial neural networks
Computer vision
High resolution
Image resolution
Modular systems
Neural networks
Remote sensing
Satellite imagery
Satellites
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2267-1242
2555-0403
2267-1242
DOI:10.1051/e3sconf/202235101071