Taylor C. Glenn

Multi-Modal Change Detection, Application to the Detection of Flooded Areas: Outcome of the 2009–2010 Data Fusion Contest

The 2009-2010 Data Fusion Contest organized by the Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society was focused on the detection of flooded areas using multi-temporal and multi-modal images. Both high spatial resolution optical and synthetic aperture radar data were provided. The goal was not only to identify the best algorithms (in terms of accuracy), but also to investigate the further improvement derived from decision fusion. This paper presents the four awarded algorithms and the conclusions of the contest, investigating both supervised and unsupervised methods and the use of multi-modal data for flood detection. Interestingly, a simple unsupervised change detection method provided similar accuracy as supervised approaches, and a digital elevation model-based predictive method yielded a comparable projected change detection map without using post-event data.

Bayesian Fuzzy Clustering

We present a Bayesian probabilistic model and inference algorithm for fuzzy clustering that provides expanded capabilities over the traditional Fuzzy C-Means approach. Additionally, we extend the Bayesian Fuzzy Clustering model to handle a variable number of clusters and present a particle filter inference technique to estimate the model parameters including the number of clusters. We show results on synthetic and real data and compare with other approaches.

Region Processing of Ground Penetrating Radar and Electromagnetic Induction for Handheld Landmine Detection

An analysis of the utility of region-based processing of Ground Penetrating Radar (GPR) and Electromagnetic Induction (EMI) is presented. Algorithms for re-sampling GPR data acquired over non-rectangular and non-regular grids are presented. Depth-dependent whitening is used to form GPR images as functions of depth bins. Shape, size, and contrast-based features are used to distinguish mines from non-mines. The processing is compared to point-wise processing of the same data. Comparisons are made to GPR data collected by machine and by humans. Evaluations are performed on calibration data, for which the ground truth is known to the algorithm developers, and blind data, for which the ground truth is not known to the algorithm developers.

Landmine detection using frequency domain features from GPR measurements and their fusion with time domain features

We present in this paper the use of frequency domain features deduced from the energy density spectrum to improve the detection of landmines. The energy density spectrum is obtained from the GPR measurements at an alarm location, and a method to estimate the energy density spectrum is proposed. The energy density spectrum is shown to reveal distinct characteristics between landmine and clutter objects and therefore can be explored for their discrimination. The robustness and consistency of the frequency domain features are demonstrated through two different GPRs. The fusion of frequency domain features and time-domain features is also examined. Experimental results at several test sites confirm the advantages of frequency domain features to better discriminate between mine targets and clutter objects, and the effectiveness of fusion in frequency and time domain features to improve detection performance.

Spectral unmixing using the beta compositional model

This paper introduces a beta compositional model as a mixing model for hyperspectral images. Endmembers are represented via beta distributions, hereafter referred to as betas, to constrain endmembers to a physically-meaningful range. Two associated spectral unmixing algorithms are described and applied to simulated and real hyperspectral imagery.

Comparison of algorithms for finding the air-ground interface in ground penetrating radar signals

In using GPR images for landmine detection it is often useful to identify the air-ground interface in the GPR signal for alignment purposes. A number of algorithms have been proposed to solve the air-ground interface detection problem, including some which use only A-scan data, and others which track the ground in B-scans or C-scans. Here we develop a framework for comparing these algorithms relative to one another and we examine the results. The evaluations are performed on data that have been categorized in terms of features that make the air-ground interface difficult to find or track. The data also have associated human selected ground locations, from multiple evaluators, that can be used for determining correctness. A distribution is placed over each of the human selected ground locations, with the sum of these distributions at the algorithm selected location used as a measure of its correctness. Algorithms are also evaluated in terms of how they affect the false alarm and true positive rates of mine detection algorithms that use ground aligned data.

Landmine Detection Using Two-Tapped Joint Orthogonal Matching Pursuits

Joint Orthogonal Matching Pursuits (JOMP) is used here in the context of landmine detection using data obtained from an electromagnetic induction (EMI) sensor. The response from an object containing metal can be decomposed into a discrete spectrum of relaxation frequencies (DSRF) from which we construct a dictionary. A greedy iterative algorithm is proposed for computing successive residuals of a signal by subtracting away the highest matching dictionary element at each step. The nal condence of a particular signal is a combination of the reciprocal of this residual and the mean of the complex component. A two-tap approach comparing signals on opposite sides of the geometric location of the sensor is examined and found to produce better classication. It is found that using only a single pursuit does a comparable job, reducing complexity and allowing for real-time implementation in automated target recognition systems. JOMP is particularly highlighted in comparison with a previous EMI detection algorithm known as String Match.

Optimal fusion of alarm sets from multiple detectors using dynamic programming

In a standard target detection approach, data is collected, points of interest called alarms are identified, and detection algorithms determine the confidence that a target is present at each point. Receiver operating characteristic (ROC) curves can be used to evaluate the performance of each detector and choose operating thresholds. The use of multiple sensors can improve the probability of detection of a diverse set of targets. It is difficult to properly assess the performance of a system of detectors and choose the best joint set of operating thresholds if confidence values from different detectors do not compare meaningfully. Fusion methods can be used to improve the joint performance of a set of detectors. However, in the case where different detectors do not operate on the same points of interest, typical fusion methods cannot be used to improve the binary decisions on individual alarms. In this paper, we propose a new fusion method that maps the confidence outputs from different detectors to a shared range where they compare meaningfully, and optimizes the joint performance of multiple detectors even when their alarm sets are disjoint. Our method uses dynamic programming to monotonically map the confidence output from each detector onto a shared range in such a way that we maximize the area-under-the-curve (AUC) of the ROC curve corresponding to the joint set of alarms. This joint ROC curve can be used to determine the operational thresholds for each individual detector to maximize their joint performance.

Subpixel target detection in hyperspectral imagery using piece-wise convex spatial-spectral unmixing, possibilistic and fuzzy clustering, and co-registered LiDAR

A new algorithm for subpixel target detection in hyperspectral imagery is proposed which uses the PFCM-FLICM-PCE algorithm to model and estimate the parameters of the image background. This method uses the piece-wise convex mixing model with spatial-spectral constraints, and uses possibilistic and fuzzy clustering techniques to find the piece-wise convex regions and robustly estimate the parameters. A method for integrating the elevation measurements of a co-registered LiDAR sensor is also proposed. The performance of the proposed methods is demonstrated on a real-world dataset with emplaced detection targets.

On the use of energy density spectra for discriminating between landmines and clutter objects

The paper studies the use of energy density spectra (EDS) derived from ground penetrating radar (GPR) measurements on a sub-surface target to discriminate between landmines and clutter objects. The GPR used to collect the data is frequency swept and has a bandwidth of 1.4 GHz. Our investigation indicates that the EDS reveals distinct characteristics between landmine targets and many clutter objects, and hence can be exploited for their discrimination. Experimental results based on data measured at a government test site corroborate the effectiveness of the proposed approach.