Skip to Main Content
Hyperspectral change detection (HSCD) provides an avenue for detecting subtle targets in complex backgrounds. Complicating the problem of change detection is the presence of shadow, illumination, and atmospheric differences, as well as misregistration and parallax error, which often produce the appearance of change. Recent development of a model-based (MB) approach to HSCD has demonstrated potential improvement for mitigating false alarms due specifically to shadow differences using calibrated data. Further development and application of the MB approach is provided here. The method is extended for use on both uncalibrated and relatively calibrated hyperspectral data and is applied to airborne hyperspectral imagery collected using the Hyperspectral Digital Imagery Collection Experiment visible to short-wave infrared sensor and uncalibrated tower imagery collected by the Air Force Research Laboratory.