Abstract
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.
Highlights
Current urban and rural space growth rates and obvious effects of building construction on different applications, such as building damage assessments, building change detection, population estimation, and urban planning continuous monitoring of building footprints are becoming ever more significant (Hardin et al, 2007; Hegazy and Kaloop, 2015; Khoshboresh-Masouleh et al, 2020; Masouleh and ShahHosseini, 2018)
Despite the previous approaches that try to extract building change without uncertainty map from small regions with deep learning models and large-scale datasets, in this paper, we focus on building change detection analysis on a small set of building change from different areas that sit in several cities
We focus on Monte Carlo dropout (Gal and Ghahramani, 2016) for uncertainty modelling in building change detection
Summary
Current urban and rural space growth rates and obvious effects of building construction on different applications, such as building damage assessments, building change detection, population estimation, and urban planning continuous monitoring of building footprints are becoming ever more significant (Hardin et al, 2007; Hegazy and Kaloop, 2015; Khoshboresh-Masouleh et al, 2020; Masouleh and ShahHosseini, 2018). The interest in developing building change detection methods from bi-temporal optical images with a different platform (e.g., UAV, Airborne, and Spaceborne) is rapidly increasing due to larger land-covers, and lower costs of remote sensing data (Bayanlou and Khoshboresh-Masouleh, 2020; Khoshboresh Masouleh and Saradjian, 2019; Khoshboresh-Masouleh and Hasanlou, 2020). Despite rapid advances in computer vision and remote sensing, change detection is a major challenge in mapping. Building change detection from an optical remote sensing image with high accuracy and precision needs considerable effort in developing robust methods.
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