Dr. Benjamin T. Purman Profile

Dr. Benjamin T. Purman

Sr. Engineer at GDAIS

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Proceedings Article | 2 September 2004 Paper

Prediction-based registration: an automated multi-INT registration algorithm

Benjamin Purman, James Spencer, Jennifer Conk

Proceedings Volume 5427, (2004) https://doi.org/10.1117/12.548775

KEYWORDS: Sensors, Image registration, Data modeling, Synthetic aperture radar, Image sensors, Image fusion, Image processing, Reflectivity, Image quality, Sun

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This paper presents an algorithm for the automatic georegistration of electro-optical (EO) and synthetic aperture radar (SAR) imagery intelligence (IMINT). The algorithm uses a scene reference model in a global coordinate frame to register the incoming IMINT, or mission image. Auxiliary data from the mission image and this model predict a synthetic reference image of a scene at the same collection geometry as the mission image. This synthetic image provides a traceback structure relating the synthetic reference image to the scene model. A correlation matching technique is used to register the mission image to the synthetic reference image. Once the matching has been completed, mission image pixels can be transformed into the corresponding synthetic reference image. Using the traceback structure associated with the synthetic reference image, these pixels can then be transformed into the scene model space. Since the scene model space exists in a global coordinate frame, the mission image has been georegistered. This algorithm is called Prediction-Based Registration (PBR). There are a number of advantages to the PBR approach. First, the transformation from image space to scene model space is computed as a 3D to 2D transformation. This avoids solving the ill-posed problem of directly transforming a 2D image into 3D space. The generation of a synthetic reference simplifies the image matching process by creating the synthetic reference at the same geometry as the mission image. Further, dissimilar sensor phenomenologies are accounted for by using the appropriate sensor model. This allows sensor platform and image formation errors to be accounted for in their own domain when multiple sensors are being registered.

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