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Abstract-We develop a vegetation mapping method using long-wave hyperspectral imagery and apply it to landmine detection. The novel aspect of the method is that it makes use of emissivity skewness. The main purpose of vegetation detection for mine detection is to minimize false alarms. Vegetation, such as round bushes, may be mistaken as mines by mine detection algorithms, particularly in synthetic aperture radar (SAR) imagery. We employ an unsupervised vegetation detection algorithm that exploits statistics of emissivity spectra of vegetation in the long-wave infrared spectrum for identification. This information is incorporated into a Choquet integral-based fusion structure, which fuses detector outputs from hyperspectral imagery and SAR imagery. Vegetation mapping is shown to improve mine detection results over a variety of images and fusion models.

A novel multiple-instance hidden Markov model (MI-HMM) is introduced for classification of time-series data, and its training is developed using stochastic expectation maximization. The MI-HMM provides a single statistical form to learn the parameters of an HMM in a multiple-instance learning framework without introducing any additional parameters. The efficacy of the model is shown both on synthetic data and on a real landmine data set. Experiments on both the synthetic data and the landmine data set show that an MI-HMM can 1) achieve statistically significant performance gains when compared with the best existing HMM for the landmine detection problem, 2) eliminate the ad hoc approaches in training set selection, and 3) introduce a principled way to work with ambiguous time-series data. She has worked on several target detection problems with data from ground penetrating radar, hyperspectral, electromagnetic induction, and LiDAR sensors, with a special focus on landmine detection. Her research interests include machine learning, pattern recognition, hyperspectral image analysis, statistical data analysis, computer vision, and medical imaging. Dr. Yuksel was a recipient of the University of Florida College of Engineering Outstanding International Student Award in 2010 and the Phyllis M. Meek Spirit of Susan B. Anthony Award at the University of Florida in 2008. Jeremy Bolton (S'07-M'09) received the B.S. degree in computer engineering and the M.Eng. and Ph.D. degrees from the University of Florida, Gainesville, FL, USA, in 2003 and 2008, respectively.He is currently a Consultant in the area of academic course design for a variety of e-learning platforms. Previously, he was a Research Scientist with

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