A Signal-Level Fusion Model for Image-Based Change Detection in DARPA's Dynamic Database System

Improving change detection performance (probability of detection/false alarm rate) is an important goal of DARPA's Dynamic Database (DDB) program. We describe a novel approach based on fusing the outputs from two complementary image-based change detection algorithms. Both use historical imagery over the region of interest to construct normalcy models for detecting change. Image level change detection (ILCD) segments the set of images into temporally co-varying pixel sets that are spatially distributed throughout the image, and uses spatial normalcy models constructed over these pixel sets to detect change in a new image. Object level change detection (OLCD) segments each image into a set of spatially compact objects, and uses temporal normalcy models constructed over objects associated over time to detect change in the new image. Because of the orthogonal manner in which ILCD and OLCD operate in space-time, false alarms tend to decorrelate. We develop signal-level statistical models to predict the performance gain (output/input signal to noise ratio) of each algorithm individually, and combined using 'and' fusion. Experimental results using synthetic aperture radar (SAR) images are presented. Fusion gains ranging from slightly greater than unity in low clutter backgrounds (e.g., open areas) to more than 20db in complex backgrounds containing man-made objects such as vehicles and buildings have been achieved and are discussed.