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Persistent sensing by Unmanned Airborne Vehicles (UAVs) has brought up challenging issues including multi-scale
analysis, multi-modal sensor fusion, and scene localization. As for the first issue, the multi-scale and
multi-resolution issues occur when a mobile sensor changes altitudes or two different sensors with the same
camera provide any redundant images from different altitudes. To overcome these issues, we first focus on
collecting invariant feature data from the multi-resolution representation of a high resolution image. Recently, an
information-theoretic matching criterion has been developed for robust data registration without any knowledge
of feature correspondence. This criterion is used as an intelligent computing algorithm of choosing a good scale-representation
that helps to find an unknown scaling factor between two different and redundant measurements.
As for the second issue of multi-modal sensor fusion, we observe that Electro-optical (EO) and Infrared (IR)
images in the DARPA VIVID database have an inherent scaling-difference, even though the different modalities
come from the two fixed EO and IR sensors attached on the same mobile sensor. Here we provide a new
experimental result of multi-modal data fusion that successfully combines complementary information via the
process of data refinement. The recovered transformation reveals one of the fundamental characteristics of the
two different modalities. The last issue of scene localization is required for identifying the scene visited before.
In this paper, we demonstrate the trajectory of the mobile sensor based only on the extracted transformations
(not relying on any telemetric data of the mobile sensor which is not available persistently) by projecting the
center locations of image measurements onto the two dimensional reference coordinate.
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