Enhancing vector shoreline data using a data fusion approach

Mark Carlotto; Mark Nebrich; David DeMichele

doi:10.1117/12.2265422

2 May 2017 Enhancing vector shoreline data using a data fusion approach

Mark Carlotto, Mark Nebrich, David DeMichele

Proceedings Volume 10200, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI; 1020010 (2017) https://doi.org/10.1117/12.2265422
Event: SPIE Defense + Security, 2017, Anaheim, CA, United States

Abstract

Vector shoreline (VSL) data is potentially useful in ATR systems that distinguish between objects on land or water. Unfortunately available data such as the NOAA 1:250,000 World Vector Shoreline and NGA Prototype Global Shoreline data cannot be used by themselves to make a land/water determination because of the manner in which the data are compiled. We describe a data fusion approach for creating labeled VSL data using test points from Global 30 Arc-Second Elevation (GTOPO30) data to determine the direction of vector segments; i.e., whether they are in clockwise or counterclockwise order. We show consistently labeled VSL data be used to easily determine whether a point is on land or water using a vector cross product test.

Citation Download Citation

Mark Carlotto, Mark Nebrich, and David DeMichele "Enhancing vector shoreline data using a data fusion approach", Proc. SPIE 10200, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI, 1020010 (2 May 2017); https://doi.org/10.1117/12.2265422

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