Use of Generative Adversarial Network for Cross-Domain Change Detection

TL;DR

This paper proposes a GAN-based method for cross-domain change detection by translating reference images into the query domain, enabling effective in-domain comparison and leveraging existing change detection techniques.

Contribution

It introduces a novel GAN-based image translation approach to address cross-domain change detection with limited training data.

Findings

Effective cross-domain change detection demonstrated

GAN-based translation reduces domain discrepancies

Improved accuracy over traditional methods

Abstract

This paper addresses the problem of cross-domain change detection from a novel perspective of image-to-image translation. In general, change detection aims to identify interesting changes between a given query image and a reference image of the same scene taken at a different time. This problem becomes a challenging one when query and reference images involve different domains (e.g., time of the day, weather, and season) due to variations in object appearance and a limited amount of training examples. In this study, we address the above issue by leveraging a generative adversarial network (GAN). Our key concept is to use a limited amount of training data to train a GAN-based image translator that maps a reference image to a virtual image that cannot be discriminated from query domain images. This enables us to treat the cross-domain change detection task as an in-domain image comparison. This allows us to leverage the large body of literature on in-domain generic change detectors. In addition, we also consider the use of visual place recognition as a method for mining more appropriate reference images over the space of virtual images. Experiments validate efficacy of the proposed approach.