Graph Laplacian for Image Anomaly Detection

Reed-Xiaoli detector (RXD) is recognized as the benchmark algorithm for image anomaly detection; however, it presents known limitations, namely the dependence over the image following a multivariate Gaussian model, the estimation and inversion of a high-dimensional covariance matrix, and the inability to effectively include spatial awareness in its evaluation. In this work, a novel graph-based solution to the image anomaly detection problem is proposed; leveraging the graph Fourier transform, we are able to overcome some of RXD's limitations while reducing computational cost at the same time. Tests over both hyperspectral and medical images, using both synthetic and real anomalies, prove the proposed technique is able to obtain significant gains over performance by other algorithms in the state of the art.Comment: Published in Machine Vision and Applications (Springer

Exploitation of Intra-Spectral Band Correlation for Rapid Feacture Selection and Target Identification in Hyperspectral Imagery

This research extends the work produced by Capt. Robert Johnson for detecting target pixels within hyperspectral imagery (HSI). The methodology replaces Principle Components Analysis for dimensionality reduction with a clustering algorithm which seeks to associate spectral rather than spatial dimensions. By seeking similar spectral dimensions, the assumption of no a priori knowledge of the relationship between clustered members can be eliminated and clusters are formed by seeking high correlated adjacent spectral bands. Following dimensionality reduction Independent Components Analysis (ICA) is used to perform feature extraction. Kurtosis and Potential Target Fraction are added to Maximum Component Score and Potential Target Signal to Noise Ratio as mechanisms for discriminating between target and non-target maps. A new methodology exploiting Johnson’s Maximum Distance Secant Line method replaces the first zero bin method for identifying the breakpoint between signal and noise. A parameter known as Left Partial Kurtosis is defined and applied to determine when target pixels are likely to be found in the left tail of each signal histogram. A variable control over the number of iterations of Adaptive Iterative Noise filtering is introduced. Results of this modified algorithm are compared to those of Johnson’s AutoGAD [2007]

Reconstruction Error and Principal Component Based Anomaly Detection in Hyperspectral imagery

The rapid expansion of remote sensing and information collection capabilities demands methods to highlight interesting or anomalous patterns within an overabundance of data. This research addresses this issue for hyperspectral imagery (HSI). Two new reconstruction based HSI anomaly detectors are outlined: one using principal component analysis (PCA), and the other a form of non-linear PCA called logistic principal component analysis. Two very effective, yet relatively simple, modifications to the autonomous global anomaly detector are also presented, improving algorithm performance and enabling receiver operating characteristic analysis. A novel technique for HSI anomaly detection dubbed multiple PCA is introduced and found to perform as well or better than existing detectors on HYDICE data while using only linear deterministic methods. Finally, a response surface based optimization is performed on algorithm parameters such as to affect consistent desired algorithm performance

Towards the Mitigation of Correlation Effects in the Analysis of Hyperspectral Imagery with Extension to Robust Parameter Design

Standard anomaly detectors and classifiers assume data to be uncorrelated and homogeneous, which is not inherent in Hyperspectral Imagery (HSI). To address the detection difficulty, a new method termed Iterative Linear RX (ILRX) uses a line of pixels which shows an advantage over RX, in that it mitigates some of the effects of correlation due to spatial proximity; while the iterative adaptation from Iterative Linear RX (IRX) simultaneously eliminates outliers. In this research, the application of classification algorithms using anomaly detectors to remove potential anomalies from mean vector and covariance matrix estimates and addressing non-homogeneity through cluster analysis, both of which are often ignored when detecting or classifying anomalies, are shown to improve algorithm performance. Global anomaly detectors require the user to provide various parameters to analyze an image. These user-defined settings can be thought of as control variables and certain properties of the imagery can be employed as noise variables. The presence of these separate factors suggests the use of Robust Parameter Design (RPD) to locate optimal settings for an algorithm. This research extends the standard RPD model to include three factor interactions. These new models are then applied to the Autonomous Global Anomaly Detector (AutoGAD) to demonstrate improved setting combinations

Anomalous change detection in multi-temporal hyperspectral images

In latest years, the possibility to exploit the high amount of spectral information has made hyperspectral remote sensing a very promising approach to detect changes occurred in multi-temporal images. Detection of changes in images of the same area collected at different times is of crucial interest in military and civilian applications, spanning from wide area surveillance and damage assessment to geology and land cover. In military operations, the interest is in rapid location and tracking of objects of interest, people, vehicles or equipment that pose a potential threat. In civilian contexts, changes of interest may include different types of natural or manmade threats, such as the path of an impending storm or the source of a hazardous material spill. In this PhD thesis, the focus is on Anomalous Change Detection (ACD) in airborne hyperspectral images. The goal is the detection of small changes occurred in two images of the same scene, i.e. changes having size comparable with the sensor ground resolution. The objects of interest typically occupy few pixels of the image and change detection must be accomplished in a pixel-wise fashion. Moreover, since the images are in general not radiometrically comparable, because illumination, atmospheric and environmental conditions change from one acquisition to the other, pervasive and uninteresting changes must be accounted for in developing ACD strategies. ACD process can be distinguished into two main phases: a pre-processing step, which includes radiometric correction, image co-registration and noise filtering, and a detection step, where the pre-processed images are compared according to a defined criterion in order to derive a statistical ACD map highlighting the anomalous changes occurred in the scene. In the literature, ACD has been widely investigated providing valuable methods in order to cope with these problems. In this work, a general overview of ACD methods is given reviewing the most known pre-processing and detection methods proposed in the literature. The analysis has been conducted unifying different techniques in a common framework based on binary decision theory, where one has to test the two competing hypotheses H0 (change absent) and H1 (change present) on the basis of an observation vector derived from the radiance measured on each pixel of the two images. Particular emphasis has been posed on statistical approaches, where ACD is derived in the framework of Neymann Pearson theory and the decision rule is carried out on the basis of the statistical properties assumed for the two hypotheses distribution, the observation vector space and the secondary data exploited for the estimation of the unknown parameters. Typically, ACD techniques assume that the observation represents the realization of jointly Gaussian spatially stationary random process. Though such assumption is adopted because of its mathematical tractability, it may be quite simplistic to model the multimodality usually met in real data. A more appropriate model is that adopted to derive the well known RX anomaly detector which assumes the local Gaussianity of the hyperspectral data. In this framework, a new statistical ACD method has been proposed considering the local Gaussianity of the hyperspectral data. The assumption of local stationarity for the observations in the two hypotheses is taken into account by considering two different models, leading to two different detectors. In addition, when data are collected by airborne platforms, perfect co-registration between images is very difficult to achieve. As a consequence, a residual misregistration (RMR) error should be taken into account in developing ACD techniques. Different techniques have been proposed to cope with the performance degradation problem due to the RMR, embedding the a priori knowledge on the statistical properties of the RMR in the change detection scheme. In this context, a new method has been proposed for the estimation of the first and second order statistics of the RMR. The technique is based on a sequential strategy that exploits the Scale Invariant Feature Transform (SIFT) algorithm cascaded with the Minimum Covariance Determinant algorithm. The proposed method adapts the SIFT procedure to hyperspectral images and improves the robustness of the outliers filtering by means of a highly robust estimator of multivariate location. Then, the attention has been focused on noise filtering techniques aimed at enforcing the consistency of the ACD process. To this purpose, a new method has been proposed to mitigate the negative effects due to random noise. In particular, this is achieved by means of a band selection technique aimed at discarding spectral channels whose useful signal content is low compared with the noise contribution. Band selection is performed on a per-pixel basis by exploiting the estimates of the noise variance accounting also for the presence of the signal dependent noise component. Finally, the effectiveness of the proposed techniques has been extensively evaluated by employing different real hyperspectral datasets containing anomalous changes collected in different acquisition conditions and on different scenarios, highlighting advantages and drawbacks of each method. In summary, the main issues related to ACD in multi-temporal hyperspectral images have been examined in this PhD thesis. With reference to the pre-processing step, two original contributions have been offered: i) an unsupervised technique for the estimation of the RMR noise affecting hyperspectral images, and ii) an adaptive approach for ACD which mitigates the negative effects due to random noise. As to the detection step, a survey of the existing techniques has been carried out, highlighting the major drawbacks and disadvantages, and a novel contribution has been offered by presenting a new statistical ACD method which considers the local Gaussianity of the hyperspectral data

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

Ballistic Flash Characterization: Penetration and Back-face Flash

The Air Force is extremely concerned with the safety of its people, especially those who are flying aircraft. Aircrew members flying combat missions are concerned with the chance that a fragment from an exploding threat device may penetrate into the airframe to possibly ignite a fire onboard the aircraft. One concern for vulnerability revolves around a flash that may occur when a projectile strikes and penetrates an aircraft\u27s fuselage. When certain fired rounds strike the airframe, they break into fragments called spall. Spall and other fragmentation from an impact often gain enough thermal energy to oxidize the materials involved. This oxidation causes a flash. To help negate these incidents, analysts must be able to predict the flash that can occur when a projectile strikes an aircraft. This research directly continues AFIT work for the 46th Test Group, Survivability Analysis Flight, by examining models to predict the likelihood of penetration of a fragment fired at a target. Empirical live-fire fragment test data are used to create an empirical model of a flash event. The resulting model provides an initial back-face flash modeling capability that can be implemented in joint survivability analysis models

Automatic Target Recognition for Hyperspectral Imagery

Automatic target detection and recognition in hyperspectral imagery offer passive means to detect and identify anomalies based on their material composition. In many combat identification approaches through pattern recognition, a minimum level of confidence is expected with costs associated with labeling anomalies as targets, non-targets or out-of-library. This research approaches the problem by developing a baseline, autonomous four step automatic target recognition (ATR) process: 1) anomaly detection, 2) spectral matching, 3) out-of-library decision, and 4) non-declaration decision. Atmospheric compensation techniques are employed in the initial steps to compare truth library signatures and sensor processed signatures. ATR performance is assessed and additionally contrasted to two modified ATRs to study the effects of including steps three and four. Also explored is the impact on the ATR with two different anomaly detection methods