Multisensor target detection using adaptive feature-based fusion

Target detection techniques play an important role in automatic target recognition (ATR) systems because overall ATR performance depends closely on detection results. A number of detection techniques based on infrared (IR) images have been developed using a variety of pattern recognition approaches. However, target detection based on a single IR sensor is often hampered by adverse weather conditions or countermeasures, resulting in unacceptably high false alarm rates. Multiple imaging sensors in different spectral ranges, such as visible and infrared bands, are used here to reduce such adverse effects. The imaging data from the different sensors are jointly processed to exploit the spatial characteristics of the objects. Four local features are used to exploit the local characteristics of the images generated from each sensor. A confidence image is created via feature-based fusion that combines the features to obtain potential target locations. Experimental results using two test sequences are provided to demonstrate the viability of the proposed technique.