Change Detection with Heterogeneous Remote Sensing Data: From Semi-Parametric Regression to Deep Learning

Change detection represents a major family of remote sensing image analysis techniques and plays a fundamental role in a variety of applications to environmental monitoring and disaster risk management. However, most change detection methods operate under the assumption that the multitemporal input data have been collected with the same (or very similar) acquisition modality - a possibly critical restriction in several applications. In this paper, the problem and the opportunities of change detection from multitemporal data acquired through heterogeneous modalities are addressed. Methodologically, this is a highly challenging data fusion problem, especially within an unsupervised framework. Here, these challenges and the methodological approaches proposed in the literature' which range from earlier semi-parametric regression to current deep learning architectures, are reviewed. Then, recent fully unsupervised techniques, based on spectral clustering, traditional image regression, and deep image-to-image translation, are briefly described.