Resolution Enhancement of Thermal Images via Multiemporal Fusion of Etherogeneous Data

Remotely sensed thermal data are of major interest for a variety of environmental and agricultural applications, allowing for the continuous estimation of geophysical features over wide areas. However, the effective use of these techniques often requires images with both high spatial resolution (HSR) and high temporal rate (htr), which constitute conflicting requirements for a single sensor. A solution can be achieved through data fusion techniques that provide improved synthetic images by taking advantage of data collected by multiple sensors. In this perspective we investigate in this study a method for combining thermal image sequences with complementary features, evaluating several different approaches for completing this task. The paper reports the analysis of several approaches based on deterministic interpolation and fusion schemes, focusing on the integration of data collected by the recent VIIRS sensor with data provided by the SEVIRI and MODIS instruments.