A ratio-based change detection method known as multiratio fusion (MRF) is proposed and tested. The MRF framework builds on other change detection components proposed in this work: dual ratio (DR) and multiratio (MR). The DR method involves two ratios coupled with adaptive thresholds to maximize detected changes and minimize false alarms. The use of two ratios is shown to outperform the single ratio case when the means of the image pairs are not equal. MR change detection builds on the DR method by including negative imagery to produce four total ratios with adaptive thresholds. Inclusion of negative imagery is shown to improve detection sensitivity and to boost detection performance in certain target and background cases. MRF further expands this concept by fusing together the ratio outputs using a routine in which detections must be verified by two or more ratios to be classified as a true changed pixel. The proposed method is tested with synthetically generated test imagery and real datasets with results compared to other methods found in the literature. DR is shown to significantly outperform the standard single ratio method. MRF produces excellent change detection results that exhibit up to a 22% performance improvement over other methods from the literature at low false-alarm rates.
Interest in the use of active electro-optical(EO) sensors for non-cooperative target identification has steadily increased as the quality and availability of EO sources and detectors have improved. A unique and recent innovation has been the development of an airborne synthetic aperture imaging capability at optical wavelengths. To effectively exploit this new data source for target identification, one must develop an understanding of target-sensor phenomenology at those wavelengths. Current high-frequency, asymptotic EM predictors are computationally intractable for such conditions, as their ray density is inversely proportional to wavelength. As a more efficient alternative, we have developed a geometric optics based simulation for synthetic aperture ladar that seeks to model the second order statistics of the diffuse scattering commonly found at those wavelengths but with much lesser ray density. Code has been developed, ported to high-performance computing environments, and tested on a variety of target models.
Image change detection has long been used to detect significant events in aerial imagery, such as the arrival or departure
of vehicles. Usually only the underlying structural changes are of interest, particularly for movable objects, and the
challenge is to differentiate the changes of intelligence value (change detections) from incidental appearance changes (false
detections). However, existing methods for automated change detection continue to be challenged by nuisance variations in
operating conditions such as sensor (camera exposure, camera viewpoints), targets (occlusions, type), and the environment
(illumination, shadows, weather, seasons). To overcome these problems, we propose a novel vehicle change detection
method based on the detection response maps (DRM). The detector serves as an advanced filter that normalizes the images
being compared specifically for object level change detection (OLCD). In contrast to current methods that compare pixel
intensities, the proposed DRM-OLCD method is more robust to nuisance changes and variations in image appearance. We
demonstrate object-level change detection for vehicle appearing and disappearing in electro-optical (EO) visual imagery.