Towards adaptive thresholding for sub-pixel co-registration and anomaly detection
Jeannine A. Abiva, Tesfaye G-Michael, Rodney G. Roberts · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2017
Automated change detection (ACD) is a technique that automatically discerns any area of change when comparing two images of the same geographic location over different moments in time. Within the ACD processing stream, co-registration ensures the areas depicted in two images coincide. The difficulty in co-registering sonar images of the sea floor can arise from a difference in vehicle trajectories, low resolution, and the presence of noise. Moreover, the changing features of the sea floor can further add to the difficulty. The successful co-registration of sonar images is important when comparing images, and is thus required in areas such as change detection and mosaicing. In this effort, a three-step co-registration process is used: co-registration by navigational alignment, fine-scale co-registration using SIFT, and local co-registration that corrects navigational differences. In this paper, we focus on the final step where phase alignment occurs. To eliminate unreliable unwrapped phase data, we introduce a novel histogram based adaptive thresholding technique which rejects errors in phase alignment occurring in the across-track direction of the vehicle. Further, an adaptive thresholding technique is applied to the change-map generated following the co-registration stage. To isolate pixels of interest related to anomalies or targets, a thresholding method is applied in conjunction with principal and independent component analysis (PCA and ICA). We will demonstrate the effectiveness of these adaptive thresholding techniques in sub-pixel co-registration and target detection.