Christopher R. Ratto

Exploiting Ground-Penetrating Radar Phenomenology in a Context-Dependent Framework for Landmine Detection and Discrimination

A technique for making landmine detection with a ground-penetrating radar (GPR) sensor more robust to fluctuations in environmental conditions is presented. Context-dependent feature selection (CDFS) counteracts environmental uncertainties that degrade detection and discrimination performances by modifying decision rules based on inference of the environmental context. This paper utilized both physics-based and statistical methods for extracting features from GPR data to characterize surface texture and subsurface electrical properties, and a nonparametric hypothesis test was used to identify the environmental context from which the data were collected. The results of probabilistic context identification were then used to fuse an ensemble of classifiers for discriminating landmines from clutter under diverse environmental conditions. CDFS was evaluated on a large set of GPR data collected over several years in different weather and terrain conditions. Results indicate that our context-dependent technique improved landmine discrimination performance over conventional fusion of several currently fielded algorithms from the recent literature.

Multiple instance and context dependent learning in hyperspectral data

Hyperspectral imaging (HSI) is a powerful tool for various remote sensing tasks including agricultural modeling and landmine/ unexploded ordnance clearance. Although the application of standard supervised learning techniques to HSI data has previously been explored, several aspects of hyperspectral data collection and ground truth labeling make some of the assumptions underlying standard machine learning techniques invalid. For example, HSI is highly dependent upon local environmental conditions, and pixel-by-pixel labels for HSI data are often not available. As a result, data from hyperspectral sensing under various scenarios is not typically i.i.d., and correct data labels must be inferred from training data while learning decision boundaries. In this work we explore two possible solutions to these problems: context-dependent learning for overcoming variations between collections, and multiple instance learning for simultaneously inferring local target labels and global target decision boundaries. Results are compared to standard logistic discriminant classification approaches.

Context-dependent landmine detection with ground-penetrating radar using a Hidden Markov Context Model

Context-dependent approaches to landmine detection have been developed in recent years to exploit the sensitivity of ground-penetrating radar (GPR) to changes in environmental conditions. Previous approaches to context-dependent fusion have only considered the special case of statistically independent observations. This work proposes the use of Hidden Markov Models, trained on the GPR background, for modeling the context of observation sequences. The performances of context-dependent fusion using two statistical context models were compared in an experiment with field data. One approach utilized a Hidden Markov Context Model (HMCM), and the other utilized a Gaussian mixture. Experimental results illustrated that the HMCM improved performance of context-dependent fusion. These results suggest that spatial dependencies are an important source of contextual information for landmine detection that warrants further investigation.

Bayesian Context-Dependent Learning for Anomaly Classification in Hyperspectral Imagery

Many remote sensing applications involve the classification of anomalous responses as either objects of interest or clutter. This paper addresses the problem of anomaly classification in hyperspectral imagery (HSI) and focuses on robustly detecting disturbed earth in the long-wave infrared (LWIR) spectrum. Although disturbed earth yields a distinct LWIR signature that distinguishes it from the background, its distribution relative to clutter may vary over different environmental contexts. In this paper, a generic Bayesian framework is proposed for training context-dependent classification rules from wide-area airborne LWIR imagery. The proposed framework combines sparse classification models with either supervised or discriminative context identification to pool information across contexts and improve classification overall. Experiments are performed with data from a LWIR landmine detection system. Contexts are learned from endmember abundances extracted from the background near each detected anomaly. Classification performance is compared with single-classifier approaches using the same information as well as other baseline anomaly detectors from the literature. Results indicate that utilizing context for classifying anomalies in HSI could lead to more robust performance over varying terrain.

A hidden Markov context model for GPR-based landmine detection incorporating stick-breaking priors

In recent years, context-dependent algorithm fusion has been proposed for improving landmine detection with ground-penetrating radar (GPR) across changing environmental and operating conditions. While context-dependent fusion techniques generally assume independent observations, previous work showed that spatial information may be exploited by modeling context with a hidden Markov model (HMM). However, the degree of performance improvement was found to depend the number of states included in the HMM. In this work, stick-breaking priors were employed to automate learning of the number of HMM states, and therefore the number of contexts to consider. The improved spatially-dependent fusion technique was evaluated on GPR data collected over various targets at multiple test sites, and performance was compared to another context-dependent technique which assumed independent observations. Results illustrate the potential for nonparametric, spatially-dependent context modeling to exploit contextual information in sequentially-collected GPR data and improve overall classification performance.

Context-dependent feature selection using unsupervised contexts applied to GPR-based landmine detection

Context-dependent classification techniques applied to landmine detection with ground-penetrating radar (GPR) have demonstrated substantial performance improvements over conventional classification algorithms. Context-dependent algorithms compute a decision statistic by integrating over uncertainty in the unknown, but probabilistically inferable, context of the observation. When applied to GPR, contexts may be defined by differences in electromagnetic properties of the subsurface environment, which are due to discrepancies in soil composition, moisture levels, and surface texture. Context-dependent Feature Selection (CDFS) is a technique developed for selecting a unique subset of features for classifying landmines from clutter in different environmental contexts. In past work, context definitions were assumed to be soil moisture conditions which were known during training. However, knowledge of environmental conditions could be difficult to obtain in the field. In this paper, we utilize an unsupervised learning algorithm for defining contexts which are unknown a priori. Our method performs unsupervised context identification based on similarities in physics-based and statistical features that characterize the subsurface environment of the raw GPR data. Results indicate that utilizing this contextual information improves classification performance, and provides performance improvements over non-context-dependent approaches. Implications for on-line context identification will be suggested as a possible avenue for future work.

Estimation of soil permittivity through autoregressive modeling of time-domain ground-penetrating radar data

Recent advances in context-dependent processing for vehicle-based route clearance suggest that information regarding the environmental context associated with ground-penetrating radar (GPR) data can potentially be exploited to improve target detection performance. In this work, a statistical approach to estimating soil permittivity (dielectric constant) from raw time-domain data is presented as an alternative to electromagnetic model inversion. First, a large set of GPR data was simulated using finite-difference time-domain (FDTD) modeling over a heterogeneous subsurface with a rough air/ground interface. Physics-based features were then extracted from the simulated data through autoregressive (AR) modeling of B-scan time slices. A linear least-squares regression model was applied to the features, and experimental results indicate that the dielectric constant of the base soil can be accurately predicted by the regression model. This approach has several advantages over model inversion techniques for estimating soil permittivity, since it is causal, computationally efficient, and does not require an analytical electromagnetic model a priori.

Context-dependent feature selection for landmine detection with ground-penetrating radar

We present a novel method for improving landmine detection with ground-penetrating radar (GPR) by utilizing a priori knowledge of environmental conditions to facilitate algorithm training. The goal of Context-Dependent Feature Selection (CDFS) is to mitigate performance degradation caused by environmental factors. CDFS operates on GPR data by first identifying its environmental context, and then fuses the decisions of several classifiers trained on context-dependent subsets of features. CDFS was evaluated on GPR data collected at several distinct sites under a variety of weather conditions. Results show that using prior environmental knowledge in this fashion has the potential to improve landmine detection.

Integration of lidar with the NIITEK GPR for improved performance on rough terrain

Vehicle-mounted ground-penetrating radar (GPR) has proved to be a valuable technology for buried threat detection, especially in the area of military route clearance. However, detection performance may be degraded in very rough terrain or o-road conditions. This is because the signal processing approaches for target detection in GPR rst identify the ground re ection in the data, and then align the data in order to remove the ground re ection. Under extremely rough terrain, antenna bounce and multipath eects render nding the ground re ection a dicult task, and errors in ground localization can lead to data alignment that distorts potential target signatures and/or creates false alarms. In this work, commercial-o-the-shelf light detection and ranging (LIDAR), global positioning system (GPS), and inertial measurement unit (IMU) were integrated with a GPR into a prototype route clearance system. The LIDAR provided high-resolution measurements of the ground surface prole, and the GPS/IMU recorded the vehicles position and orientation. Experiments investigated the applicability of the integrated system for nding the ground re ection in GPR data and decoupling vehicle motion from the rough surface response. Assessment of ground-tracking performance was based on an experiment involving three prepared test lanes, each with dierent congurations of buried targets and terrain obstacles. Several algorithms for target detection in GPR were applied to the data, both with traditional preprocessing and incorporating the LIDAR and IMU. Experimental results suggest that the LIDAR and IMU may be valuable components for ground tracking in next-generation GPR systems.

Contextual learning in ground-penetrating radar data using Dirichlet process priors

In landmine detection applications, fluctuation of environmental and operating conditions can limit the performance of sensors based on ground-penetrating radar (GPR) technology. As these conditions vary, the classification and fusion rules necessary for achieving high detection and low false alarm rates may change. Therefore, context-dependent learning algorithms that exploit contextual variations of GPR data to alter decision rules have been considered for improving the performance of landmine detection systems. Past approaches to contextual learning have used both generative and discriminative methods to learn a probabilistic mixture of contexts, such as a Gaussian mixture, fuzzy c-means clustering, or a mixture of random sets. However, in these approaches the number of mixture components is pre-defined, which could be problematic if the number of contexts in a data collection is unknown a priori. In this work, a generative context model is proposed which requires no a priori knowledge in the number of mixture components. This was achieved through modeling the contextual distribution in a physics-based feature space with a Gaussian mixture, while also incorporating a Dirichlet process prior to model uncertainty in the number of mixture components. This Dirichlet process Gaussian mixture model (DPGMM) was then incorporated in the previously-developed Context-Dependent Feature Selection (CDFS) framework for fusion of multiple landmine detection algorithms. Experimental results suggest that when the DPGMM was incorporated into CDFS, the degree of performance improvement over conventional fusion was greater than when a conventional fixed-order context model was used.