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4 August 2000 Adaptive multi-image decision fusion
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Abstract
Adaptive decision fusion represents a unique addition to the ATR community interest in Wide Area Surveillance. Isolating targets form non-targets before they reach an ATR processing algorithm can significantly reduce subsequent ATR processing burdens. As the volume of imagery increases from diverse new sensor systems, adaptive methods will be required to reduce early-stage false alarms to levels that can be handled by more computationally intensive down-stream processing. Change detection algorithms solve part of the problem by reducing false alarms, but the mapping transformation form image space to change space also induces a new set of false reports. The Adaptive Multi-Image Decision Fusion process will provide a basis for fusing and interpreting these change events and 'bundling' them together in a feature set so that they can be dealt with by a feature-based classifier. The decision level fusion will use only feature provided by the component change detection modules . This acts as the first stage of screening to determine which sensor's and which algorithm's output should be fused and adaptively determines the corresponding optimal fusion rule. A complete set of fusion rules are examined for the two- detector case for collected SAR imagery, and theoretical considerations are discussed for the three-detector case. Each rule compares the relative performance from each change detection algorithm. The system determines the quality of each report with respect to the level of clutter, and determines the representative fusion rule. Examples are provided.
© (2000) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Martin E. Liggins II and Mark A. Nebrich "Adaptive multi-image decision fusion", Proc. SPIE 4052, Signal Processing, Sensor Fusion, and Target Recognition IX, (4 August 2000); https://doi.org/10.1117/12.395072
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