  Real-time satellite imaging has a central role in monitoring, detecting and
estimating the intensity of key natural phenomena such as floods, earthquakes,
etc. One important constraint of satellite imaging is the trade-off between
spatial/spectral resolution and their revisiting time, a consequence of design
and physical constraints imposed by satellite orbit among other technical
limitations. In this paper, we focus on fusing multi-temporal, multi-spectral
images where data acquired from different instruments with different spatial
resolutions is used. We leverage the spatial relationship between images at
multiple modalities to generate high-resolution image sequences at higher
revisiting rates. To achieve this goal, we formulate the fusion method as a
recursive state estimation problem and study its performance in filtering and
smoothing contexts. Furthermore, a calibration strategy is proposed to estimate
the time-varying temporal dynamics of the image sequence using only a small
amount of historical image data. Differently from the training process in
traditional machine learning algorithms, which usually require large datasets
and computation times, the parameters of the temporal dynamical model are
calibrated based on an analytical expression that uses only two of the images
in the historical dataset. A distributed version of the Bayesian filtering and
smoothing strategies is also proposed to reduce its computational complexity.
To evaluate the proposed methodology we consider a water mapping task where
real data acquired by the Landsat and MODIS instruments are fused generating
high spatial-temporal resolution image estimates. Our experiments show that the
proposed methodology outperforms the competing methods in both estimation
accuracy and water mapping tasks.
