We present a fully automatic method for the alignment SAR images, which is capable of precise and robust alignment.A multiresolution SAR image matching metric is first used to automatically determine tie-points, which are then used to perform coarse-to-fine resolution image alignment. A formalism is developed for the automatic determination of tie-point regions that contain sufficiently distinctive structure to provide strong constraints on alignment. The coarse-to-fine procedure for the refinement of the alignment estimate both improves computational efficiency and yields robust and consistent image alignment.
This paper describes the Sensor to Shooter Information Fusion for Rapid Targeting (SSIFRT) program. The objective ofthis program is to design, develop, test, and demonstrate the fusion of intelligence, surveillance, and reconnaissance (TSR) data with on-board sensor data. This decentralized information fusion system will take advantage ofboth on-board tactical platform and off-board sensor data to generate a high performance identification capability. The algorithm development will address Automatic Target Recognition (ATR), ground target tracking, target cueing, and registration of imagery residing on both ground station (off-board) and tactical aircraft (on-board) systems. Analysis of data link and processing requirements/capabilities will be performed to determine an on-board and off-board fusion architecture.The off-board component would be targeted for ground station applications where multiple sources of information will come together. The off-board fusion algorithm employs a Bayesian approach to integrate information from multiple image sources such as SAR, EO, and FLIR as well as non-image based intelligence sources such as Ground Moving Target Indicators (GMTI). Model-based ATR technology will be an important module of this off-board fusion system for the extraction of target information from image data sources. Compression techniques and innovative representations of information will be investigated so that information can be transferred to the shooter platform using existing or planned communication channels. Finally, the tactical platform's on-board system will incorporate a fused feature methodology that utilizes off-board cues and on-board SAP. and FLIIR imagery for final on-board target identification. INTRODUCTIONThe SSIFRT project has very ambitious goals in terms ofboth image ATR and data fusion. The goals will be achieved by leveraging technology from a wide range of existing programs. The baseline SSIFRT program utilizes the off-board fusion system from the Information Fusion OnlOff-board Avionics (INFO) program, an AFRL Phase II SBIR, which has developed an all-source information fusion architecture for integrating information from multiple intelligence sources, including image data from SAR, FLIR, and potentially EO sources. Within this architecture, image data is preprocessed to
Accurate image registration is critical for applications such as precision targeting, geo-location, change-detection, surveillance, and remote sensing. However, the increasing volume of image data is exceeding the current capacity of human analysts to perform manual registration. This image data glut necessitates the development of automated approaches to image registration, including algorithm parameter value selection. Proper parameter value selection is crucial to the success of registration techniques. The appropriate algorithm parameters can be highly scene and sensor dependent. Therefore, robust algorithm parameter value selection approaches are a critical component of an end-to-end image registration algorithm. In previous work, we developed a general framework for multisensor image registration which includes feature-based registration approaches. In this work we examine the problem of automated parameter selection. We apply the automated parameter selection approach of Yitzhaky and Peli to select parameters for featurebased registration of multisensor image data. The approach consists of generating multiple feature-detected images by sweeping over parameter combinations and using these images to generate estimated ground truth. The feature-detected images are compared to the estimated ground truth images to generate ROC points associated with each parameter combination. We develop a strategy for selecting the optimal parameter set by choosing the parameter combination corresponding to the optimal ROC point. We present numerical results showing the effectiveness of the approach using registration of collected SAR data to reference EO data.
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