Algorithm Development for Hyperspectral Anomaly Detection

This dissertation proposes and evaluates a novel anomaly detection algorithm suite for ground-to-ground, or air-to-ground, applications requiring automatic target detection using hyperspectral (HS) data. Targets are manmade objects in natural background clutter under unknown illumination and atmospheric conditions. The use of statistical models herein is purely for motivation of particular formulas for calculating anomaly output surfaces. In particular, formulas from semiparametrics are utilized to obtain novel forms for output surfaces, and alternative scoring algorithms are proposed to calculate output surfaces that are comparable to those of semiparametrics. Evaluation uses both simulated data and real HS data from a joint data collection effort between the Army Research Laboratory and the Army Armament Research Development & Engineering Center. A data transformation method is presented for use by the two-sample data structure univariate semiparametric and nonparametric scoring algorithms, such that, the two-sample data are mapped from their original multivariate space to an univariate domain, where the statistical power of the univariate scoring algorithms is shown to be improved relative to existing multivariate scoring algorithms testing the same two-sample data. An exhaustive simulation experimental study is conducted to assess the performance of different HS anomaly detection techniques, where the null and alternative hypotheses are completely specified, including all parameters, using multivariate normal and mixtures of multivariate normal distributions. Finally, for ground-to-ground anomaly detection applications, where the unknown scales of targets add to the problem complexity, a novel global anomaly detection algorithm suite is introduced, featuring autonomous partial random sampling (PRS) of the data cube. The PRS method is proposed to automatically sample the unknown background clutter in the test HS imagery, and by repeating multiple times this process, one can achieve a desirably low cumulative probability of taking target samples by chance and using them as background samples. This probability is modeled by the binomial distribution family, where the only target related parameter--the proportion of target pixels potentially covering the imagery--is shown to be robust. PRS requires a suitable scoring algorithm to compare samples, although applying PRS with the new two-step univariate detectors is shown to outperform existing multivariate detectors

Detección de objetivos en imágenes hiperespectrales sobre FPGAs

Trabajo de Fin de Grado en Ingeniería de Computadores, Facultad de Informática UCM, Departamento de Arquitectura de Computadores y Automática, Curso 2019/2020In recent years there has been a resurgence in the space race, motivated especially by commercial companies. Their aircrafts are equipped with a multitude of sensors, one of them being hyperspectral cameras. This type of camera takes images in hundreds of different spectral bands, with the aim of providing information of the ground. Because of the large size of hyperspectral images, they are sent to ground stations for processing, with the consequent cost of transmission and storage. Preferably these images should be processed or compressed on site and only a fraction of the data obtained should be sent. Given the spatial environment and the characteristics of these types of algorithms, FPGAs or ASICs are postulated as an optimal system for their implementation. This work presents an FPGA implementation of the Reed-Xiaoli algorithm of anomaly detection for hyperspectral images. For its implementation, an analysis of the operations of the algorithm has been made, centered in a oating point version and another one in integer arithmetic, and of the repercussions that certain decisions have with the precision that is reached. Thus, demonstrating how complex algorithms with oating point operations can be executed in FPGAs by transforming them to use integer arithmetic.En los últimos años ha ocurrido un resurgimiento de la carrera espacial motivado especialmente por empresas comerciales. Sus aeronaves son equipadas con una multitud de sensores, siendo uno de ellos las cámaras hiperespectrales. Este tipo de cámaras toma imágenes en cientos de bandas espectrales diferentes, con el objetivo de proporcionar información sobre el terreno. A causa del gran tamaño de las imágenes hiperespectrales, estas son enviadas a la Tierra para su procesado, con el consecuente coste de transmisión y almacenamiento. Preferentemente estas imágenes deberían procesarse o comprimirse in situ para enviar solo una fracción de los datos obtenidos. Dados el entorno espacial y las características de este tipo de algoritmos, las FPGAs o ASICs se postulan como un sistema óptimo para su implementación. Este trabajo presenta una implementación sobre FPGAs del algoritmo Reed-Xiaoli de detección de anomalías para imágenes hiperespectrales. Para su implementación se ha realizado un análisis de las operaciones del algoritmo, centrada en una versión en punto flotante y otra en aritmética de enteros, y de las repercusiones que tienen ciertas decisiones con la precisión que se alcanza. Demostrando de esta manera cómo algoritmos complejos con operaciones en punto flotante pueden ser ejecutados en FPGAs al transformarlos para utilizar aritmética de enteros.Depto. de Arquitectura de Computadores y AutomáticaFac. de InformáticaTRUEunpu

Models and Methods for Automated Background Density Estimation in Hyperspectral Anomaly Detection

Detecting targets with unknown spectral signatures in hyperspectral imagery has been proven to be a topic of great interest in several applications. Because no knowledge about the targets of interest is assumed, this task is performed by searching the image for anomalous pixels, i.e. those pixels deviating from a statistical model of the background. According to the hyperspectral literature, there are two main approaches to Anomaly Detection (AD) thus leading to the definition of different ways for background modeling: global and local. Global AD algorithms are designed to locate small rare objects that are anomalous with respect to the global background, identified by a large portion of the image. On the other hand, in local AD strategies, pixels with significantly different spectral features from a local neighborhood just surrounding the observed pixel are detected as anomalies. In this thesis work, a new scheme is proposed for detecting both global and local anomalies. Specifically, a simplified Likelihood Ratio Test (LRT) decision strategy is derived that involves thresholding the background log-likelihood and, thus, only needs the specification of the background Probability Density Function (PDF). Within this framework, the use of parametric, semi-parametric (in particular finite mixtures), and non-parametric models is investigated for the background PDF estimation. Although such approaches are well known and have been widely employed in multivariate data analysis, they have been seldom applied to estimate the hyperspectral background PDF, mostly due to the difficulty of reliably learning the model parameters without the need of operator intervention, which is highly desirable in practical AD tasks. In fact, this work represents the first attempt to jointly examine such methods in order to asses and discuss the most critical issues related to their employment for PDF estimation of hyperspectral background with specific reference to the detection of anomalous objects in a scene. Specifically, semi- and non-parametric estimators have been successfully employed to estimate the image background PDF with the aim of detecting global anomalies in a scene by means of the use of ad hoc learning procedures. In particular, strategies developed within a Bayesian framework have been considered for automatically estimating the parameters of mixture models and one of the most well-known non-parametric techniques, i.e. the fixed kernel density estimator (FKDE). In this latter, the performance and the modeling ability depend on scale parameters, called bandwidths. It has been shown that the use of bandwidths that are fixed across the entire feature space, as done in the FKDE, is not effective when the sample data exhibit different local peculiarities across the entire data domain, which generally occurs in practical applications. Therefore, some possibilities are investigated to improve the image background PDF estimation of FKDE by allowing the bandwidths to vary over the estimation domain, thus adapting the amount of smoothing to the local density of the data so as to more reliably and accurately follow the background data structure of hyperspectral images of a scene. The use of such variable bandwidth kernel density estimators (VKDE) is also proposed for estimating the background PDF within the considered AD scheme for detecting local anomalies. Such a choice is done with the aim to cope with the problem of non-Gaussian background for improving classical local AD algorithms involving parametric and non-parametric background models. The locally data-adaptive non-parametric model has been chosen since it encompasses the potential, typical of non-parametric PDF estimators, in modeling data regardless of specific distributional assumption together with the benefits deriving from the employment of bandwidths that vary across the data domain. The ability of the proposed AD scheme resulting from the application of different background PDF models and learning methods is experimentally evaluated by employing real hyperspectral images containing objects that are anomalous with respect to the background

How much BiGAN and CycleGAN-learned hidden features are effective for COVID-19 detection from CT images? A comparative study

Bidirectional generative adversarial networks (BiGANs) and cycle generative adversarial networks (CycleGANs) are two emerging machine learning models that, up to now, have been used as generative models, i.e., to generate output data sampled from a target probability distribution. However, these models are also equipped with encoding modules, which, after weakly supervised training, could be, in principle, exploited for the extraction of hidden features from the input data. At the present time, how these extracted features could be effectively exploited for classification tasks is still an unexplored field. Hence, motivated by this consideration, in this paper, we develop and numerically test the performance of a novel inference engine that relies on the exploitation of BiGAN and CycleGAN-learned hidden features for the detection of COVID-19 disease from other lung diseases in computer tomography (CT) scans. In this respect, the main contributions of the paper are twofold. First, we develop a kernel density estimation (KDE)-based inference method, which, in the training phase, leverages the hidden features extracted by BiGANs and CycleGANs for estimating the (a priori unknown) probability density function (PDF) of the CT scans of COVID-19 patients and, then, in the inference phase, uses it as a target COVID-PDF for the detection of COVID diseases. As a second major contribution, we numerically evaluate and compare the classification accuracies of the implemented BiGAN and CycleGAN models against the ones of some state-of-the-art methods, which rely on the unsupervised training of convolutional autoencoders (CAEs) for attaining feature extraction. The performance comparisons are carried out by considering a spectrum of different training loss functions and distance metrics. The obtained classification accuracies of the proposed CycleGAN-based (resp., BiGAN-based) models outperform the corresponding ones of the considered benchmark CAE-based models of about 16% (resp., 14%)

Air Force Institute of Technology Research Report 2013

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems Engineering and Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

Optical Satellite Remote Sensing of the Coastal Zone Environment — An Overview

Optical remote-sensing data are a powerful source of information for monitoring the coastal environment. Due to the high complexity of coastal environments, where different natural and anthropogenic phenomenon interact, the selection of the most appropriate sensor(s) is related to the applications required, and the different types of resolutions available (spatial, spectral, radiometric, and temporal) need to be considered. The development of specific techniques and tools based on the processing of optical satellite images makes possible the production of information useful for coastal environment management, without any destructive impacts. This chapter will highlight different subjects related to coastal environments: shoreline change detection, ocean color, water quality, river plumes, coral reef, alga bloom, bathymetry, wetland mapping, and coastal hazards/vulnerability. The main objective of this chapter is not an exhaustive description of the image processing methods/algorithms employed in coastal environmental studies, but focus in the range of applications available. Several limitations were identified. The major challenge still is to have remote-sensing techniques adopted as a routine tool in assessment of change in the coastal zone. Continuing research is required into the techniques employed for assessing change in the coastal environment

Assessing skin lesion evolution from multispectral image sequences

During the evaluation of skin disease treatments, dermatologists have to clinically measure the evolution of the pathology severity of each patient during treatment periods. Such a process is sensitive to intra- and inter- dermatologist diagnosis. To make this severity measurement more objective we quantify the pathology severity using a new image processing based method. We focus on a hyperpigmentation disorder called melasma. During a treatment period, multispectral images are taken on patients receiving the same treatment. After co-registration and segmentation steps, we propose an algorithm to measure the intensity, the size and the homogeneity evolution of the pathological areas. Obtained results are compared with a dermatologist diagnosis using statistical tests on two clinical studies containing respectively 384 images from 16 patients and 352 images from 22 patients.This research report is an update of the report 8136. It describes methods and experiments in more details and provides more references.Lors de l'évaluation des traitements des maladies de peau, les dermatologues doivent mesurer la sévérité de la pathologie de chaque patient tout au long d'une période de traitement. Un tel procédé est sensible aux variations intra- et inter- dermatologues. Pour rendrecette mesure de sévérité plus robuste, nous proposons d'utiliser l'imagerie spectrale. Nous nous concentrons sur une pathologie d'hyperpigmentation cutanée appelée mélasma. Au cours d'une période de traitement, des images multispectrales sont acquises sur une population de patients sous traitement. Après des étapes de recalage des séries temporelles d'images et de classification des régions d'intérêt, nous proposons une méthodologie permettant de mesurer, dans le temps, la variation de contraste, de surface et d'homogénéité de la zone pathologique pour chaque patient. Les résultats obtenus sont comparés à un diagnostique clinique à l'aide de tests statistiques réalisés sur une étude clinique complète.Ce rapport de recherche est un complément du rapport de recherche 8136, afin de compléter la bibliographie, et de décrire plus en détail les méthodes et résultat

Anomaly and Change Detection in Remote Sensing Images

Earth observation through satellite sensors, models and in situ measurements provides a way to monitor our planet with unprecedented spatial and temporal resolution. The amount and diversity of the data which is recorded and made available is ever-increasing. This data allows us to perform crop yield prediction, track land-use change such as deforestation, monitor and respond to natural disasters and predict and mitigate climate change. The last two decades have seen a large increase in the application of machine learning algorithms in Earth observation in order to make efficient use of the growing data-stream. Machine learning algorithms, however, are typically model agnostic and too flexible and so end up not respecting fundamental laws of physics. On the other hand there has, in recent years, been an increase in research attempting to embed physics knowledge in machine learning algorithms in order to obtain interpretable and physically meaningful solutions. The main objective of this thesis is to explore different ways of encoding physical knowledge to provide machine learning methods tailored for specific problems in remote sensing.Ways of expressing expert knowledge about the relevant physical systems in remote sensing abound, ranging from simple relations between reflectance indices and biophysical parameters to complex models that compute the radiative transfer of electromagnetic radiation through our atmosphere, and differential equations that explain the dynamics of key parameters. This thesis focuses on inversion problems, emulation of radiative transfer models, and incorporation of the above-mentioned domain knowledge in machine learning algorithms for remote sensing applications. We explore new methods that can optimally model simulated and in-situ data jointly, incorporate differential equations in machine learning algorithms, handle more complex inversion problems and large-scale data, obtain accurate and computationally efficient emulators that are consistent with physical models, and that efficiently perform approximate Bayesian inversion over radiative transfer models

Multispectral persistent surveillance

The goal of a successful surveillance system to achieve persistence is to track everything that moves, all of the time, over the entire area of interest. The thrust of this thesis is to identify and improve upon the motion detection and object association aspect of this challenge by adding spectral information to the equation. Traditional motion detection and tracking systems rely primarily on single-band grayscale video, while more current research has focused on sensor fusion, specifically combining visible and IR data sources. A further challenge in covering an entire area of responsibility (AOR) is a limited sensor field of view, which can be overcome by either adding more sensors or multi-tasking a single sensor over multiple areas at a reduced frame rate. As an essential tool for sensor design and mission development, a trade study was conducted to measure the potential advantages of adding spectral bands of information in a single sensor with the intention of reducing sensor frame rates. Thus, traditional motion detection and object association algorithms were modified to evaluate system performance using five spectral bands (visible through thermal IR), while adjusting frame rate as a second variable. The goal of this research was to produce an evaluation of system performance as a function of the number of bands and frame rate. As such, performance surfaces were generated to assess relative performance as a function of the number of bands and frame rate