ABSTRACT
Multispectral and hyperspectral imagers are powerful tools in remote sensing and provide great promise for rapid detection and characterization of agricultural food crop contaminants.  Hyperspectral imagers can be useful in detecting when a contaminant has been introduced to an agricultural crop before the crop stresses are visible to the human eye, providing a valuable lead time in first response.  In some cases there is no visible indicator that the contaminant has been introduced to the vegetation; i.e. the optical reflectance is altered only in the non-visible regions of the optical spectrum.  A hyperspectral image can provide densely sampled reflectance measurements across the visible and near infrared regions of the spectrum, resulting in hyperspectral signatures with 100’s of spectral bands.  These signatures can then be analyzed with advanced mathematical algorithms, to determine if a particular target is present.  In this application, the “target” would be “a contaminated agricultural crop” and the “nontarget” would be “an agricultural crop under normal conditions”.

In this work we investigate the use of discrete wavelet transforms, multi-classifiers, and decision fusion in an automated target recognition (ATR) system to address the challenges of hyperspectral data as it relates to food crop contamination detection.  The experimental results are very promising, resulting in accuracies as high as 90+% for some cases, and demonstrate the efficacy of the proposed system for rapid detection of agricultural food crop contaminations.