Removing Parallax-Induced False Changes in Change Detection

Accurate change detection (CD) results in urban environments is of interest to a diverse set of applications including military surveillance, environmental monitoring, and urban development. This work presents a hyperspectral CD (HSCD) framework. The framework uncovers the need for HSCD methods that resolve false change caused by image parallax. A Generalized Likelihood Ratio Test (GLRT) statistic for HSCD is developed that accommodates unknown mis-registration between imagery described by a prior probability density function for the spatial mis-registration. The potential of the derived method to incorporate more complex signal proccessing functions is demonstrated by the incorporation of a parallax error mitigation component. Results demonstrate that parallax mitigation reduces false alarms

Removing Parallax-Induced Changes in Hyperspectral Change Detection

Hyperspectral-based change detection is often inadvertently affected by image artifacts, reducing the accuracy of the change detector. We present a Hyperspectral Change Detection (HSCD) process to distinguish parallax-induced change from legitimate change. Image parallax decreases the accuracy of change detection results. The approach introduced in this paper utilizes a combination of a spectral change detector and stereo geometry to reduce parallax-induced false alarms. Image parallax is determined by considering the error in the epipolar constraint, meaning the corresponding points between two images must lie on epipolar lines. Experimental analysis shows a false alarm reduction by nearly one order of magnitude on synthetic hyperspectral imagery and nearly two orders of magnitude on real hyperspectral imagery. Abstract © 2012 IEEE