Heterogeneous Change Detection on Remote Sensing Data with Self-Supervised Deep Canonically Correlated Autoencoders

Change detection is a well-known topic of remote sensing. The goal is to track and monitor the evolution of changes affecting the Earth surface over time. The recently increased availability in remote sensing data for Earth observation and in computational power has raised the interest in this field of research. In particular, the keywords “multitemporal” and “heterogeneous” play prominent roles. The former refers to the availability and the comparison of two or more satellite images of the same place on the ground, in order to find changes and track the evolution of the observed surface, maybe with different time sensitivities. The latter refers to the capability of performing change detection with images coming from different sources, corresponding to different sensors, wavelengths, polarizations, acquisition geometries, etc. This thesis addresses the challenging topic of multitemporal change detection with heterogeneous remote sensing images. It proposes a novel approach, taking inspiration from recent developments in the literature. The proposed method is based on deep learning - involving autoencoders of convolutional neural networks - and represents an exapmple of unsupervised change detection. A major novelty of the work consists in including a prior information model, used to make the method unsupervised, within a well-established algorithm such as the canonical correlation analysis, and in combining these with a deep learning framework to give rise to an image translation method able to compare heterogeneous images regardless of their highly different domains. The theoretical analysis is supported by experimental results, comparing the proposed methodology to the state of the art of this discipline. Two different datasets were used for the experiments, and the results obtained on both of them show the effectiveness of the proposed method.