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Symmetry 2018, 10(7), 294; https://doi.org/10.3390/sym10070294

Vegetation Greening for Winter Oblique Photography Using Cycle-Consistence Adversarial Networks

1
School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
2
School of Tourism and Geography Science, Jilin Normal University, Changchun 130024, China
3
School of Public and Environment Affairs, Indiana University, Bloomington, IN 47408, USA
*
Author to whom correspondence should be addressed.
Received: 27 May 2018 / Revised: 6 July 2018 / Accepted: 18 July 2018 / Published: 20 July 2018
(This article belongs to the Special Issue Novel Machine Learning Approaches for Intelligent Big Data)
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Abstract

A 3D city model is critical for the construction of a digital city. One of the methods of building a 3D city model is tilt photogrammetry. In this method, oblique photography is crucial for generating the model because the visual quality of photography directly impacts the model’s visual effect. Yet, sometimes, oblique photography does not have good visual quality due to a bad season or defective photographic equipment. For example, for oblique photography taken in winter, vegetation is brown. If this photography is employed to generate the 3D model, the result would be bad visually. Yet, common methods for vegetation greening in oblique photography rely on the assistance of the infrared band, which is not available sometimes. Thus, a method for vegetation greening in winter oblique photography without the infrared band is required, which is proposed in this paper. The method was inspired by the work on CycleGAN (Cycle-consistence Adversarial Networks). In brief, the problem of turning vegetation green in winter oblique photography is considered as a style transfer problem. Summer oblique photography generally has green vegetation. By applying CycleGAN, winter oblique photography can be transferred to summer oblique photography, and the vegetation can turn green. Yet, due to the existence of “checkerboard artifacts”, the original result cannot be applied for real production. To reduce artifacts, the generator of CycleGAN is modified. As the final results suggest, the proposed method unlocks the bottleneck of vegetation greening when the infrared band is not available and artifacts are reduced. View Full-Text
Keywords: intelligence big data; adversarial network; oblique photography; vegetation greening intelligence big data; adversarial network; oblique photography; vegetation greening
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Xue, X.; Wu, C.; Sun, Z.; Wu, Y.; Xiong, N.N. Vegetation Greening for Winter Oblique Photography Using Cycle-Consistence Adversarial Networks. Symmetry 2018, 10, 294.

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