A Locally Adaptable Iterative RX Detector

We present an unsupervised anomaly detection method for hyperspectral imagery (HSI) based on data characteristics inherit in HSI. A locally adaptive technique of iteratively refining the well-known RX detector (LAIRX) is developed. The technique is motivated by the need for better first- and second-order statistic estimation via avoidance of anomaly presence. Overall, experiments show favorable Receiver Operating Characteristic (ROC) curves when compared to a global anomaly detector based upon the Support Vector Data Description (SVDD) algorithm, the conventional RX detector, and decomposed versions of the LAIRX detector. Furthermore, the utilization of parallel and distributed processing allows fast processing time making LAIRX applicable in an operational setting

Using QR Factorization for Real-Time Anomaly Detection of Hyperspectral Images

Anomaly detection has been used successfully on hyperspectral images for over a decade. However, there is an ever increasing need for real-time anomaly detectors. Historically, anomaly detection methods have focused on analysis after the entire image has been collected. As useful as post-collection anomaly detection is, there is a great advantage to detecting an anomaly as it is being collected. This research is focused on speeding up the process of detection for a pre-existing method, Linear RX, which is a variation on the traditional Reed-Xiaoli detector. By speeding up the process of detection, it is possible to create a real-time anomaly detector. The window covariance matrix is our main area focus for speed improvement. Several methods were investigated, including QR factorization and tracking the change in the window covariance matrix as it moves through the image. Finally, performance comparisons are made to the original Linear RX detector

Modeling and performance estimation for airborne minefield detection system

Many programs aimed at airborne mine and minefield detection are being pursued and different algorithms are being developed and evaluated to achieve performance specifications. Thus far, no single algorithm or detection architecture has been able to fulfill the performance specifications for different mine and minefield detection scenarios...a need exists for a simulation based approach. One such simulation system is developed and evaluated in this thesis. The factors affecting the performance of an airborne detection system include physical parameters (type of background, time of day), data collection parameters (swath width, number of steps, in-step and in-flight overlap), and minefield scenarios. Data collection parameters are included in the simulation tool. False alarms and mine statistics are modeled based on the available data collected as a part of the developmental programs. Various mine and minefield detection algorithms are modeled and evaluated. Simulations are run, and Receiver Operating Characteristic (ROC) curves are used to evaluate the performance at both the mine and minefield levels. Analytical models for minefield detection performance are formulated and used to validate the simulated performance --Abstract, page iii

Optimized Hyperspectral Imagery Anomaly Detection Through Robust Parameter Design

Anomaly detection algorithms for hyperspectral imagery (HSI) are an important first step in the analysis chain which can reduce the overall amount of data to be processed. The actual amount of data reduced depends greatly on the accuracy of the anomaly detection algorithm implemented. Most, if not all, anomaly detection algorithms require a user to identify some initial parameters. These parameters (or controls) affect overall algorithm performance. Regardless of the anomaly detector being utilized, algorithm performance is often negatively impacted by uncontrollable noise factors which introduce additional variance into the process. In the case of HSI, the noise variables are embedded in the image under consideration. Robust parameter design (RPD) offers a method to model the controls as well as the noise variables and identify robust parameters. This research identifies image noise characteristics necessary to perform RPD on HSI. Additionally, a small sample training and test algorithm is presented. Finally, the standard RPD model is extended to consider higher order noise coefficients. Mean and variance RPD models are optimized in a dual response function suggested by Lin and Tu. Results are presented from simulations and two anomaly detection algorithms, the Reed-Xiaoli anomaly detector and the autonomous global anomaly detector