<title>Target location and sensor fusion through calculated and measured image differencing</title>

Abstract

The ability to rapidly detect and identify potential targets both fixed and mobile from multiple sensor feeds is a critical function in network centric warfare. In this paper we describe the use of Image Differencing and 3D terrain database editing in order to fuse oblique aerial photos, IR sensor imagery, and other non-traditional data sources to produce battlefield metrics that support network centric operations. Such metrics include target detection, recognition, and location, and improved knowledge of the target environment. Key to our approach is the rapid generation of target and background signatures from highresolution 1-meter object descriptor terrain databases. This technique utilizes the difference between measured and calculated sensor images to 1) update and correct knowledge of the terrain background, 2) register multi sensor imagery 3) identify potential/candidate targets based on residual image differencing and 3) measure and report target locations based on scene matching. The technique is especially suited for utilizing imagery from reconnaissance and remotely piloted vehicle sensors. It also holds promise for automation and real-time data reduction of battlefield sensor feeds and for improving now-time situational awareness.We will present the algorithms and approach utilized in the Image Differencing technique. We will also describe the software developed to implement the approach. Lastly we will present the results of experiments and benchmarks conducted to identify and measure target locations in test locations at Ft. Hood, TX and Ft. Hunter Liggett, CA.

<title>Rapid terrain database generation using image differencing and 3D terrain editing tools</title>

“…This means we favor software and algorithm exploration and development, which provide an operator with functional controls, which automate repetitive and tedious procedures. This approach is discussed in more detail in our parallel paper [6]. What this means is that the database update algorithms shown in figure 2 above are first built as operator interactive loops.…”

“…As mentioned above this operation is discussed in a parallel paper [6] and we assume now that camera parameters for a registered calculated image, see figure 5a, have been achieved. The major difference between the input measurement and the calculated image is therefore assumed to be errors in the database.…”

“…Clearly if a reasonably good terrain database is available, a potential target in a measured image data stream can be background registered against a calculated image and hence the target's position can be determined to the accuracy of the database. The use of the PVNT and image differencing for target location is discussed in a parallel paper [6]. We assume therefore that this function is completed and both the measured and calculated images are looking at the same piece of ground from the same point of view.…”

Indications for quantum computation requirements from comparative brain analysis

“…A more general approach specifically tailored for UAV operation is being pursued by the one of us [25] [26]. This approach uses a database of known three dimensional terrain features to generate a reference template in the perspective, lighting, and occulted conditions encountered during flight.…”