Deep few-shot learning for bi-temporal building change detection

Abstract

In real-world applications (e.g., change detection), annotating images is very expensive. To build effective deep learning models in these applications, deep few-shot learning methods have been developed and prove to be a robust approach in small training data. The study of building change detection from high spatial resolution satellite observations is important to research in remote sensing, photogrammetry, and computer vision nowadays, which can be widely used in a variety of real-world applications, such as map generation and updating. As manual high-resolution image interpretation is expensive and time-consuming, building change detection methods are of high interest. The interest in developing building change detection approaches from optical remote sensing images is rapidly increasing due to larger coverages, and lower costs of optical images. In this study, we focus on building change detection analysis on a small set of building changes from different regions that sit in several cities. In this paper, a new deep few-shot learning method is proposed for building change detection using Monte Carlo dropout and remote sensing observations. The setup is based on a small dataset, including bitemporal optical images labelled for building change detection.