Change Detection in Satellite Images Using Reconstruction Errors of Joint Autoencoders

With the growing number of open source satellite image time series, such as SPOT or Sentinel-2, the number of potential change detection applications is increasing every year. However, due to the image quality and resolution, the change detection process is a challenge nowadays. In this work, we propose an approach that uses the reconstruction losses of joint autoencoders to detect non-trivial changes (permanent changes and seasonal changes that do not follow common tendency) between two co-registered images in a satellite image time series. The autoencoder aims to learn a transformation model that reconstructs one co-registered image from another. Since trivial changes such as changes in luminosity or seasonal changes between two dates have a tendency to repeat in different areas of the image, their transformation model can be easily learned. However, non-trivial changes tend to be unique and can not be correctly translated from one date to another, hence an elevated reconstruction error where there is change. In this work, we compare two models in order to find the most performing one. The proposed approach is completely unsupervised and gives promising results for an open source time series when compared with other concurrent methods.