Detection and neutralization of surface-laid and buried landmines 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 algorithm's results. This paper discusses the development of synthetic data using the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model. A synthetic landmine scene has been modeled representing data collected at an arid US Army test site by the University of Hawaii's 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 R(x) algorithm's performance. This paper discusses landmine 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.

Library of Congress Subject Headings

Image processing--Digital techniques--Evaluation; Remote sensing--Computer simulation--Evaluation; Land mines--Remote sensing; Land mines--Computer simulation; Computer software--Verification

Publication Date


Document Type


Student Type


Degree Name

Imaging Science (MS)

Department, Program, or Center

Chester F. Carlson Center for Imaging Science (COS)


John Schott

Advisor/Committee Member

Carl Salvaggio

Advisor/Committee Member

David Messinger


RIT – Main Campus

Plan Codes