Intersensor Remote Sensing Image Registration Using Multispectral Semantic Embeddings

This letter presents a novel intersensor registration framework specially designed to register Sentinel-3 (S3) operational data using the Sentinel-2 (S2) instrument as a reference. The substantially higher resolution of the Multispectral Instrument (MSI), on-board S2, with respect to the Ocean and Land Color Instrument (OLCI), carried by S3, makes the former sensor a suitable spatial reference to finely adjust OLCI products. Nonetheless, the important spectral-spatial differences between both instruments may constrain traditional registration mechanisms to effectively align data of such different nature. In this context, the proposed registration scheme advocates the use of a topic model-based embedding approach to conduct the intersensor registration task within a common multispectral semantic space, where the input imagery is represented according to their corresponding spectral feature patterns instead of the low-level attributes. Thus, the OLCI products can be effectively registered to the MSI reference data by aligning those hidden patterns that fundamentally express the same visual concepts across the sensors. The experiments, conducted over four different S2 and S3 operational data collections, reveal that the proposed approach provides performance advantages over six different intersensor registration counterparts.