Evaluation of CFAR effects on adaptive Boolean decision fusion performance for SAR/EO change detection

The classical Bayesian method of decision fusion applies a single detection strategy that maximizes some objective function (minimizes the cost of declaring an error). The distributed detection process follows a similar approach, except that herein, a set of Boolean fusion rules are used to account for each relevant association condition. Each fusion rule applies a coupled decision statistic that adjusts detection performance thresholds for each change detector based on the combined performance of the other detectors according to overall system performance expectations (CFAR setting), localized conditions, and which combination of change detection algorithms best contributes to the fusion process (the most appropriate fusion rule). However, applying these rules to a fixed CFAR shows that based on environmental conditions, certain rules result in a much higher CFAR error than others (based on the Probability of False Alarm), even though the corresponding Probability of Detection gives high performance. Adapting the CFAR level to minimize this error for each fusion rule gives a more realistic performance criteria. The conditions under evaluation involve the use of three image change detection algorithms (two using SAR images, and one using Electro-Optical Imagery). Each change detection algorithm provides a unique observation of the environment. The Adaptive Boolean Decision Fusion process provides a basis for fusing and interpreting these change events.