Deep learning approaches to Earth Observation change detection
This work addresses the computationally challenging task of change detection in remote sensing, which is useful for applications like urban growth analysis, but it appears incremental as it applies existing deep learning methods to this domain.
The paper tackles the problem of change detection in satellite images, presenting two deep learning approaches (semantic segmentation and classification) using convolutional neural networks to achieve good results that can be refined for various applications.
The interest for change detection in the field of remote sensing has increased in the last few years. Searching for changes in satellite images has many useful applications, ranging from land cover and land use analysis to anomaly detection. In particular, urban change detection provides an efficient tool to study urban spread and growth through several years of observation. At the same time, change detection is often a computationally challenging and time-consuming task, which requires innovative methods to guarantee optimal results with unquestionable value and within reasonable time. In this paper we present two different approaches to change detection (semantic segmentation and classification) that both exploit convolutional neural networks to achieve good results, which can be further refined and used in a post-processing workflow for a large variety of applications.