End-to-End Change Detection for High Resolution Drone Images with GAN Architecture

Yura Zharkovsky; Ovadya Menadeva

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arXiv · 2006.00467

End-to-End Change Detection for High Resolution Drone Images with GAN Architecture

Yura Zharkovsky·Ovadya Menadeva

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Abstract

Monitoring large areas is presently feasible with high resolution drone cameras, as opposed to time-consuming and expensive ground surveys. In this work we reveal for the first time, the potential of using a state-of-the-art change detection GAN based algorithm with high resolution drone images for infrastructure inspection. We demonstrate this concept on solar panel installation. A deep learning, data-driven algorithm for identifying changes based on a change detection deep learning algorithm was proposed. We use the Conditional Adversarial Network approach to present a framework for change detection in images. The proposed network architecture is based on pix2pix GAN framework. Extensive experimental results have shown that our proposed approach outperforms the other state-of-the-art change detection methods.

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Yura Zharkovsky, Ovadya Menadeva. 2020-05-31. End-to-End Change Detection for High Resolution Drone Images with GAN Architecture. https://arxiv.org/abs/2006.00467

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