Charles M. Ciany

Computer aided detection/computer aided classification and data fusion algorithms for automated detection and classification of underwater mines

Raytheon has successfully developed a computer-aided detection/computer aided classification (CAD/CAC) algorithm to process the sidescan sonar outputs of both the AN/AQS20 helicopter-towed minehunting system and Woods Hole Oceanographic Institutes (WHOI) Remote Environmental Monitoring UnitS (REMUS) unmanned underwater vehicle. These systems employ high frequency acoustic imaging sonars to detect, classify, and localize minelike objects on the ocean bottom. The algorithm was initially demonstrated at the Coastal System Station (CSS) underwater range in Panama City, Florida, and then applied to REMUS sonar imagery taken in the Very Shallow Water (VSW) environment off the coast of San Diego, California. A data fusion technique for combining the outputs of three different CAD/CAC algorithms was subsequently developed and applied to a set of REMUS data. The fusion demonstrated a 4:1 reduction in false alarms relative to any single CAD/CAC algorithm. This paper gives overviews of the AN/AQS30 and the REMUS systems, describes the Raytheon CAD/CAC and Data Fusion algorithms, and gives sample results from processing of the sea test data.

Data fusion of computer-aided detection/computer-aided classification algorithms for classification of mines in very shallow water environments

A method for combining the outputs of three different computer aided detection/computer aided classification (CAD/CAC) algorithms is presented and applied to a set of sidescan sonar data taken in the very shallow water environment, where the CAD/CAC algorithms are each tuned to detect mine-like objects. The fusion center receives from each algorithm the planar image coordinates and a confidence factor associated with individual CAD/CAC contacts, and produces fused classification reports of the mine-like objects. Since the three CAD/CAC algorithms use very different approaches, we make the reasonable assumption that valid classifications are nearby each other and false alarms occur randomly in the image. The resultant geometric clustering eliminates most of the false alarms while maintaining a high level of correct classification performance. Our unique fusion algorithm takes a constrained optimization approach, which minimizes the total number of false alarms over the clustering distance and cluster confidence factor thresholds for a given probability of correct classification. Resultant receiver operating characteristics show a significant reduction in the number of false contacts: the false alarm rate from any individual CAD/CAC algorithm is reduced by a factor of four or greater through the optimized data fusion processing.

Real-time performance of fusion algorithms for computer aided detection and classification of bottom mines in the littoral environment

The fusion of multiple computer aided detection/computer aided classification (CAD/CAC) algorithms has been shown to be effective in reducing the false alarm rate associated with the automated classification of bottom mine-like objects when applied to side-scan sonar images taken in the littoral environment. Real-time operation of the CAD/CAC fusion algorithms from Raytheon, Lockheed Martin, and NSWC Coastal Systems Station (CSS) on board an unmanned underwater vehicle has recently been successfully demonstrated as part of a littoral test sponsored by the Office of Naval Research (ONR) in 2002. Test results proved that the fusion reliably classified bottom mine-like objects while significantly reducing the false alarm rate relative to that of a single CAD/CAC algorithm. This paper discusses the hardware and software architecture for the real-time implementation of the CAD/CAC algorithms, and presents the real-time performance results obtained during the experiment. Additional post processing performance results are also discussed for alternate fusion approaches, and the overall performance benefit through a significant reduction of false alarms at high correct classification probabilities is quantified.

Performance of fusion algorithms for computer-aided detection and classification of mines in very shallow water obtained from testing in navy Fleet Battle Exercise-Hotel 2000

The performance of Computer Aided Detection/Computer Aided Classification (CAD/CAC) Fusion algorithms on side-scan sonar images was evaluated using data taken at the Navyss Fleet Battle Exercise-Hotel held in Panama City, Florida, in August 2000. A 2-of-3 binary fusion algorithm is shown to provide robust performance. The algorithm accepts the classification decisions and associated contact locations form three different CAD/CAC algorithms, clusters the contacts based on Euclidian distance, and then declares a valid target when a clustered contact is declared by at least 2 of the 3 individual algorithms. This simple binary fusion provided a 96 percent probability of correct classification at a false alarm rate of 0.14 false alarms per image per side. The performance represented a 3.8:1 reduction in false alarms over the best performing single CAD/CAC algorithm, with no loss in probability of correct classification.

Performance of fusion algorithms for Computer Aided Detection and classification of bottom mines in the shallow water environment

The fusion of multiple Computer Aided Detection/Computer Aided Classification (CAD/CAC) algorithms has been shown to be effective in reducing the false alarm rate associated with the automated classification of bottom mine-like objects when applied to side-scan sonar images taken in Very Shallow Water (VSW) environments [C.M. Ciany et al., 2001],[C.M. Ciany et al., 2000]. The fusion of CAD/CAC algorithms from Raytheon and NSWC Coastal Systems Station (CSS) also has been demonstrated in the shallow water environment on a single sonar data set [C.M. Ciany et al., 2002]. This paper extends the shallow water CAD/CAC/Fusion performance analysis to an additional set of sonar data taken in the Gulf of Mexico during June 1998, and adds the outputs of a third CAD/CAC algorithm from Lockheed Martin to the fusion processing. The fusion algorithm accepts the classification confidence levels and associated contact locations from the three different CAD/CAC algorithms, clusters the contacts based on the distance between their locations, and then declares a valid target when a clustered contact passes a prescribed fusion criterion. Four different fusion criteria are evaluated: the first based on the Fisher Discriminant, the second and third based on simple and constrained binary combinations of the multiple CAD/CAC processor outputs, and the fourth based on a constrained optimization approach that minimizes the total number of false alarms over the clustering distance and cluster confidence factor thresholds for a given probability of correct classification. The resulting performance of the four fusion algorithms is compared, and the overall performance benefit of a significant reduction of false alarms at high correct classification probabilities is quantified.

Application of fusion algorithims for computer-aided detection and classification of bottom mines to shallow-water test data

The fusion of multiple Computer Aided Detection/Computer Aided Classification (CAD/CAC) algorithms has been shown to be effective in reducing the false alarm rate associated with the automated classification of bottom mine-like objects when applied to side-scan sonar images taken in Very Shallow Water (VSW) environments. This paper reports on the application of such CAD/CAC Fusion algorithms to the shallow water environment, using sidescan sonar data taken in the Gulf of Mexico during April 2000. The fusion algorithm accepts the classification confidence levels and associated contact locations from two different CAD/CAC algorithms, clusters the contacts based on the distance between their locations, and then declares a valid target when a clustered contact passes a prescribed fusion criterion. Two different fusion criteria are evaluated: the first based on the Fisher Discriminant, and the second based on a constrained optimization approach, which minimizes the total number of false alarms over the clustering distance and cluster confidence factor thresholds for a given probability of correct classification. The Fisher-based fusion provided an 82% probability of correct classification at a false alarm rate of 0.034 false alarms per image per side (port or starboard). This performance represented a 2:1 reduction in false alarms over a single CAD/CAC algorithm at this same probability of correct classification. The cluster confidence fusion algorithm performed nearly as well, yielding the 82% correct classification probability at a false alarm rate of 0.039 false alarms per image per side.

Application of Fusion Algorithms for Computer Aided Detection and Classification of Bottom Mines to Shallow Water Test Data From the Battle Space Preparation Autonomous Underwater Vehicle (BPAUV)

Over the past several years, Raytheon Company has adapted its Computer Aided Detection/Computer-Aided Classification (CAD/CAC)algorithm to process side-scan sonar imagery taken in both the Very Shallow Water (VSW) and Shallow Water (SW) operating environments. This paper describes the further adaptation of this CAD/CAC algorithm to process SW side-scan image data taken by the Battle Space Preparation Autonomous Underwater Vehicle (BPAUV), a vehicle made by Bluefin Robotics. The tuning of the CAD/CAC algorithm for the vehicles sonar is described, the resulting classifier performance is presented, and the fusion of the classifier outputs with those of three other CAD/CAC processors is evaluated. The fusion algorithm accepts the classification confidence levels and associated contact locations from the four different CAD/CAC algorithms, clusters the contacts based on the distance between their locations, and then declares a valid target when a clustered contact passes a prescribed fusion criterion. Four different fusion criteria are evaluated: the first based on thresholding the sum of the confidence factors for the clustered contacts, the second and third based on simple and constrained binary combinations of the multiple CAD/CAC processor outputs, and the fourth based on the Fisher Discriminant. The resulting performance of the four fusion algorithms is compared, and the overall performance benefit of a significant reduction of false alarms at high correct classification probabilities is quantified. The optimal Fisher fusion algorithm yields a 90% probability of correct classification at a false alarm probability of 0.0062 false alarms per image per side, a 34:1 reduction in false alarms relative to the best performing single CAD/CAC algorithm.

Propagation medium impact on sonar coherent processing for high frequency synthetic aperture imaging

The major benefit of using synthetic aperture sonar (SAS) in underwater imaging applications is the provision of near-optical quality imaging at practical area coverage rates without the need for large physical aperture sizes that carry with them impractical size, weight and power requirements for the host platform. Characterization of the underwater propagation mediums spatio-temporal coherence limitations is a pre-requisite to an effective synthetic aperture sonar (SAS) system design. Raytheon Company, in conjunction with Harbor Branch Oceanographic Institute, has designed and successfully conducted an acoustic medium stability experiment to provide such measurements. The experiment accurately characterizes the mediums coherence simultaneously in both space and time by employing a stationary acoustic projector and a large, stationary acoustic array that is long enough to encompass candidate SAS design lengths without requiring platform motion. The experiment is described, and the temporal coherence measurements are presented.