Multiple-Instance Hidden Markov Model for GPR-Based Landmine Detection

Abstract

Hidden Markov models (HMMs) have previously been successfully applied to subsurface threat detection using ground penetrating radar (GPR) data. However, parameter estimation in most HMM-based landmine detection approaches is difficult since object locations are typically well known for the 2-D coordinates on the Earth's surface but are not well known for object depths underneath the ground/time of arrival in a GPR A-scan. As a result, in a standard expectation maximization HMM (EM-HMM), all depths corresponding to a particular alarm location may be labeled as target sequences although the characteristics of data from different depths are substantially different. In this paper, an alternate HMM approach is developed using a multiple-instance learning (MIL) framework that considers an unordered set of HMM sequences at a particular alarm location, where the set of sequences is defined as positive if at least one of the sequences is a target sequence; otherwise, the set is defined as negative. Using the MIL framework, a collection of these sets (bags), along with their labels is used to train the target and nontarget HMMs simultaneously. The model parameters are inferred using variational Bayes, making the model tractable and computationally efficient. Experimental results on two synthetic and two landmine data sets show that the proposed approach performs better than a standard EM-HMM.

Automated Target Detection for Geophysical Applications

“…Many studies were performed on detecting different type of anomalies: mine detection ( [5][6][7][8]) sinkholes [9], subterranean voids and tunnel detection [10,11], buried utilities [12] cracks in ice sheets [13]) as well as for bridges and roads assessment [14]. The vast majority of detection methods involve some pre-processing stage (for example: Qin et Al.…”

Robust adaptive detection of buried pipes using GPR

“…An approach based on a correlation method has been developed in [7] to remove the clutter. A method based on hidden Markov model has been recently proposed in [8]. All these methods have good performances because even if the Signal to Noise Ratio (SNR) is weak due to the clutter, the response of the landmine is also strong.…”

Statistical moments calculated via integral images in application to landmine detection from Ground Penetrating Radar 3D scans

“…As regards the latter, quite many state-of-the-art methods have been tried out, e.g., Naive Bayes and LVQ in [6], neural networks in [10], least squares curve fitting in [9,26], HMMs in [14,17,26] or ensemble classifiers in [12,22]. Yet, it seems, in general, that the final success is less dependent on the choice of learning algorithm and more dependent on the quality of images and features extracted from them.…”