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

• URL: http://arxiv.org/abs/2006.00467v1
• Date: Sun, 31 May 2020 08:19:11 GMT
• Title: End-to-End Change Detection for High Resolution Drone Images with GAN Architecture
• Authors: Yura Zharkovsky, Ovadya Menadeva
• Abstract summary: We show 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. A deep learning, data-driven algorithm for identifying changes based on a change detection deep learning algorithm was proposed.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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