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Use of Shape Representation and Similarity in Classification of UXO in Magnetometry Data

机译：形状表示和相似性在磁力测定数据中UXO分类中的应用

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Over the past few years sophisticated passive and active metal detectors have been coupled with state-of-the-art (SOA) GPS systems to provide increasingly accurate real-time localization capability when conducting Unexploded Ordnance (UXO) surveys. However, clearing UXO ranges with these automated techniques invariably still requires digging 5-100 items for every recovered intact ordnance item. In an attempt to improve discrimination and reduce false alarms, target analysis techniques have been developed to process UXO survey data sets, emphasizing statistical analysis approaches applied to the output parameters of physics-based target fitting algorithms. The objective of this project is to go beyond the use of physics-based parameters when making decisions about ordnance classification. We have incorporated shape representation and similarity based on anomalies in the mapped data files to extract and exploit image features related to the target signatures. This provides an additional input for a discrimination/classification decision. Shape information is an important component of the semantic content of the UXO target image and is a primary component of the visual decision making process used by the human analyst in the current interactive data analysis approach. Because this information is so important to the human in-the-loop, if it can be quantified and incorporated into the machine analysis of the data, it will provide an important classification tool. Indeed, if the target analysis process is ever to be fully and effectively automated, implementation of this step is imperative. This project employed existing data sets taken by the Vehicular Multi-sensor Towed Array Detection System (MTADS) at the Badlands Bombing Range (BBR) during 2001.

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