Removing parallax-induced changes in Hyperspectral Change Detection

Abstract

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.

Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art

“…A cluster kernel RX (CKRX) algorithm was developed in [289] that clustered the background pixels, then used the cluster centers in the anomaly detection. Other investigations have also focused on various specific changes, for example, work focusing on eliminating image parallax errors [290], vegetation and illumination variation [291], and diurnal and seasonal variations [292].…”