An Automated Method for Change Detection in Areas of High Clutter Density Using Sonar Imagery
نویسندگان
چکیده
Change detection is the process by which objects are detected by comparing current acoustic measurements with historical ones. This method is often the only viable option of detecting targets when the size and shape of the object is unknown, or when the clutter density is very high, causing a prohibitive number of false alarms. This paper presents a method and results for automatically performing change detection in environments with a high number of false targets, such as a port or harbour. Using an unmanned system, data was collected with a high-frequency sidescan sonar on two separate days in the winter of 2008 in a port. Target signatures are simulated at specific locations using ray-tracing and fused with the real data in order to generate controlled test cases, taking into account sonar orientation and navigation error. Then, geo-located images are automatically coregistered to each other in order to compensate for errors in positioning between surveys. Four methods are then compared for performing the change detection: the Kolmogorov-Smirnoff test statistic, the Bray-Curtis distance, the relative entropy and a traditional approach using detection and association between contacts. Results are shown for a variety of situations, and detection and false alarm rates are discussed.
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