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Synthetic Landmine Scene Development and Validation in DIRSIG

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Master's thesis

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Detection and neutralization of surface-laid and buried land mines has been a slow and dangerous endeavor for military forces and humanitarian organizations throughout the world. In an effort to make the process faster and safer, scientists have begun to exploit the ever-evolving passive electro-optical realm of detectors, both from a broadband perspective and a multi or hyperspectral perspective. Carried with this exploitation is the development of mine detection algorithms that take advantage of spectral features exhibited by mine targets, only available in a multi or hyperspectral data set. Difficulty in algorithm development arises from a lack of robust data, which is needed to appropriately test the validity of an algorithms results. This paper discusses the development of synthetic data using the Digital Imaging and Remote Sensing Image Generation DIRIG model. A synthetic land mine scene has been modeled representing data collected at an arid US Army test site by the University of Hawaiis Airborne Hyperspectral Imager AHI. The synthetic data has been created and validated to represent the surrogate minefield thermally, spatially, spectrally, and temporally over the 7.9 to 11.5 micron region using 70 bands of data. Validation of the scene has been accomplished by direct comparison to the AHI truth data using qualitative band to band visual analysis, radiance curve comparison, Rank Order Correlation comparison, Principle Components dimensionality analysis, Gray Level Co- occurrence Matrix and Spectral Co-occurrence Matrix analysis, and an evaluation of the Rx algorithms performance. This paper discusses land mine detection phenomenology, describes the steps taken to build the scene, modeling methods utilized to overcome input parameter limitations, and compares the synthetic scene to truth data.

Subject Categories:

  • Land Mine Warfare

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