Jeremy Bolton

Vegetation Mapping for Landmine Detection Using Long-Wave Hyperspectral Imagery

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.

Random set framework for multiple instance learning

Multiple instance learning (MIL) is a technique used for learning a target concept in the presence of noise or in a condition of uncertainty. While standard learning techniques present the learner with individual samples, MIL alternatively presents the learner with sets of samples. Although sets are the primary elements used for analysis in MIL, research in this area has focused on using standard analysis techniques. In the following, a random set framework for multiple instance learning (RSF-MIL) is proposed that can directly perform analysis on sets. The proposed method uses random sets and fuzzy measures to model the MIL problem, thus providing a more natural mathematical framework, a more general MIL solution, and a more versatile learning tool. Comparative experimental results using RSF-MIL are presented for benchmark data sets. RSF-MIL is further compared to the state-of-the-art in landmine detection using ground penetrating radar data.

Discrete Choquet Integral as a Distance Metric

The discrete Choquet integral is a nonlinear transformation that integrates a real function with respect to a fuzzy measure. We show that the discrete Choquet integral defines a metric if and only if the corresponding measure satisfies certain monotonicity constraints, thereby completely characterizing the class of measures that induce a metric with the Choquet integral.

Random Set Framework for Context-Based Classification With Hyperspectral Imagery

In remotely sensed hyperspectral imagery, many samples are collected on a given flight and many variable factors contribute to the distribution of samples. Various factors transform spectral responses causing them to appear differently in different contexts. We develop a method that infers context via spectra population distribution analysis. In this manner, feature space orientations of sets of spectral signatures are characterized using random set models. The models allow for the characterization of complex and irregular patterns in a feature space. The developed random set framework for context-based classification applies context-specific classifiers in an ensemblelike manner, and aggregates their decisions based on their contextual relevance to the spectra under test. Results indicate that the proposed method improves classification accuracy over similar classifiers, which make no use of contextual information, and performs well when compared to similar context-based approaches.

Multi-sensor and algorithm fusion with the Choquet integral: applications to landmine detection

We discuss the application of Choquet integrals to multi-algorithm and multi-sensor fusion in landmine detection. Choquet integrals are defined. Specific classes of measures, the full and Sugeno measures, are described. Full measures are optimized via quadratic programming. A steepest descent algorithm for optimizing Sugeno measures is derived by applying implicit differentiation. Multiple detection algorithms are applied to hyper-spectral and synthetic aperture radar imagery. In addition, a LWIR vegetation index is computed using statistics of apparent emissivity. The detection algorithms are combined using an OR operator and Choquet integrals with respect to full and Sugeno measures. The Choquet integral with respect to the full measure achieves lower false alarm rates

Application of Multiple-Instance Learning for Hyperspectral Image Analysis

Multiple-instance learning (MIL) is a learning paradigm used for learning a target concept in the presence of noise or with an uncertainty in target information including class labels. Due to the difficult situations in which hyperspectral images (HSIs) are collected, research in this area is extremely relevant and directly applicable. In the following, an MIL framework is proposed for target spectra learning for HSI analysis. MIL techniques are compared to their non-MIL counterparts (standard machine learning techniques). Experimental results indicate that MIL can learn target spectra with a lack of target information and, furthermore, result in improved classifiers.

Multiple instance learning for hyperspectral image analysis

Multiple instance learning is a recently researched learning paradigm that allows a machine learning algorithm to learn target concepts with uncertainty in the class labels of training data. In the following, this approach is assessed for use in hyperspectral image analysis. Two leading MIL algorithms are used in a classification experiment and results are compared to a state-of-the-art context-based classifier. Results indicate that using a MIL based approach may improve learned target models and subsequently classification results.

Multiple-Instance Hidden Markov Models With Applications to Landmine Detection

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 achieve statistically significant performance gains when compared with the best existing HMM for the landmine detection problem, eliminate the ad hoc approaches in training set selection, and introduce a principled way to work with ambiguous time-series data.

Landmine detection with Multiple Instance Hidden Markov Models

A novel Multiple Instance Hidden Markov Model (MI-HMM) is introduced for classification of ambiguous time-series data, and its training is accomplished via Metropolis-Hastings sampling. Without introducing any additional parameters, the MI-HMM provides an elegant and simple way to learn the parameters of an HMM in a Multiple Instance Learning (MIL) framework. The efficacy of the model is shown on a real landmine dataset. Experiments on the landmine dataset show that MI-HMM learning is very effective, and outperforms the state-of-the-art models that are currently being used in the field for landmine detection.

Sub-pixel target spectra estimation and detection using functions of multiple instances

The Functions of Multiple Instances (FUMI) method for learning target pattern and non-target patterns is introduced and extended. The FUMI method differs significantly from traditional supervised learning algorithms because only functions of target patterns are available. Moreover, these functions are likely to involve other non-target patterns. In this paper, data points which are convex combinations of a target prototype and several non-target prototypes are considered. The Convex-FUMI (C-FUMI) method learns the target and non-target patterns, the number of non-target patterns, and the weights (or proportions) of all the prototypes for each data point. For hyperspectral image analysis, the target and non-target prototypes estimated using C-FUMI are the end-members for the target material and non-target (background) materials. For this method, training data need only binary labels indicating whether a data point contains or does not contain some proportion of the target endmember; the specific target proportions for the training data are not needed. In this paper, the C-FUMI algorithm is extended to incorporate weights for training data such that target and non-target training data sets are balanced (resulting in the Weighted C-FUMI algorithm). After learning the target prototype using the binary-labeled training data, target detection is performed on test data. Results showing sub-pixel explosives detection and sub-pixel target detection on simulated data are presented.