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

TL;DR

This paper introduces a novel deep few-shot learning approach utilizing Monte Carlo dropout for building change detection in high-resolution remote sensing images, enabling effective analysis with limited labeled data.

Contribution

It proposes a new deep few-shot learning method specifically designed for building change detection using remote sensing data, addressing the challenge of limited labeled samples.

Findings

Effective detection of building changes with small datasets

Utilization of Monte Carlo dropout improves model robustness

Applicable to diverse urban regions

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 analysis of building change detection from high spatial resolution remote sensing observations is important research in photogrammetry, computer vision, and remote sensing nowadays, which can be widely used in a variety of real-world applications, such as map 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 change 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 labeled for building change detection.