Information fusion for robust detection of scarce features/objects in high resolution electro-optical satellite imagery

We conducted research to develop and test methods to improve the detection of scarce objects in high-resolution electro-optical satellite imagery. We demonstrated improvements through various forms of information and data fusion that included heuristic analyses, fuzzy integrals, and neural learning. Scarce objects of interest included Surface-to-Air Missile (SAM) Sites, SAM Launch Pads (SAM LP), SAM Transporter Erectile Launchers (SAM TELs), Construction Sites, and Engineering Vehicles. We demonstrated improved detection and reduced error in broad area search for SAM Sites in Southeast China by fusing the larger feature with the detection of smaller, multi-scale elements/components (i.e. SAM TELs and LPs) within the larger feature using heuristics and neural learning. We expanded these techniques to demonstrate improved detection of Construction Sites. We next demonstrated the improved detection of small, scarce objects (i.e. Engineering Vehicles) by fusing the outputs from multiple deep CNNs of various architectures and sizes through multi-layer perceptrons and fuzzy integrals. Major improvements were then achieved by leveraging existing CNN models designed for 3-band (RGB) models to train and process 8-band multi-spectral imagery partitioned into a set of three 3-band images and then fusing the results using fuzzy integrals. We finally demonstrated that bounding-box object detection for SAM TELs could be improved by ensembling/fusing bounding-box results from multiple detectors using a novel Pseudo-Cell State Long-Short Term Memory (PCS-LSTM) neural network developed in this research. The PCS-LSTM is a two-layer, non-serial LSTM neural network architecture that utilizes bounding-box context to act as a first-layer quasi/pseudo cell memory.Includes bibliographical references

An Optimal Retinanet Model For Automatic Satellite Image Based Missile Site Detection

Satellite image processing is a manually tedious job and offers scope for automation as part of the information extraction process from satellite images. The process of information extraction involves object detection and one of the challenges is ascertaining the minimum number of images required to train the deep learning model to achieve a certain minimum accuracy. To the best of the authors’ knowledge, work in missile site detection is relatively limited, with an existing exploration of the latest one-shot detection methods, such as RetinaNet, being absent. This work proposes an optimal deep learning model based on the RetinaNet framework and training on a minimal dataset. A comparative analysis with previous work paves the road for future research in one-shot methods and optimally trained models. As part of the study, the key findings are that an optimal training scheme based on a minimal training dataset is possible. This step enables a reduction in training time for the development of an optimal missile site detection model is concerned. One of the many techniques to determine the minimal number of training images required to train the object detection model is plotting the number of training images versus the mean average precision. The same is validated in our work. Further, a hybrid scheme based on the two-model concept is tested wherein one model prioritizes Recall while the other prioritizes Precision. Thus a combination of both models to detect a set of targets provides an optimal framework for object detection. Lastly, the study finds that the single-stage RetinaNet algorithm offers the advantage of balancing speed and accuracy over erstwhile two-stage and other single-stage methods

The University Defence Research Collaboration In Signal Processing

This chapter describes the development of algorithms for automatic detection of anomalies from multi-dimensional, undersampled and incomplete datasets. The challenge in this work is to identify and classify behaviours as normal or abnormal, safe or threatening, from an irregular and often heterogeneous sensor network. Many defence and civilian applications can be modelled as complex networks of interconnected nodes with unknown or uncertain spatio-temporal relations. The behavior of such heterogeneous networks can exhibit dynamic properties, reflecting evolution in both network structure (new nodes appearing and existing nodes disappearing), as well as inter-node relations. The UDRC work has addressed not only the detection of anomalies, but also the identification of their nature and their statistical characteristics. Normal patterns and changes in behavior have been incorporated to provide an acceptable balance between true positive rate, false positive rate, performance and computational cost. Data quality measures have been used to ensure the models of normality are not corrupted by unreliable and ambiguous data. The context for the activity of each node in complex networks offers an even more efficient anomaly detection mechanism. This has allowed the development of efficient approaches which not only detect anomalies but which also go on to classify their behaviour

The University Defence Research Collaboration In Signal Processing: 2013-2018

Signal processing is an enabling technology crucial to all areas of defence and security. It is called for whenever humans and autonomous systems are required to interpret data (i.e. the signal) output from sensors. This leads to the production of the intelligence on which military outcomes depend. Signal processing should be timely, accurate and suited to the decisions to be made. When performed well it is critical, battle-winning and probably the most important weapon which you’ve never heard of. With the plethora of sensors and data sources that are emerging in the future network-enabled battlespace, sensing is becoming ubiquitous. This makes signal processing more complicated but also brings great opportunities. The second phase of the University Defence Research Collaboration in Signal Processing was set up to meet these complex problems head-on while taking advantage of the opportunities. Its unique structure combines two multi-disciplinary academic consortia, in which many researchers can approach different aspects of a problem, with baked-in industrial collaboration enabling early commercial exploitation. This phase of the UDRC will have been running for 5 years by the time it completes in March 2018, with remarkable results. This book aims to present those accomplishments and advances in a style accessible to stakeholders, collaborators and exploiters

Robust Modular Feature-Based Terrain-Aided Visual Navigation and Mapping

The visual feature-based Terrain-Aided Navigation (TAN) system presented in this thesis addresses the problem of constraining inertial drift introduced into the location estimate of Unmanned Aerial Vehicles (UAVs) in GPS-denied environment. The presented TAN system utilises salient visual features representing semantic or human-interpretable objects (roads, forest and water boundaries) from onboard aerial imagery and associates them to a database of reference features created a-priori, through application of the same feature detection algorithms to satellite imagery. Correlation of the detected features with the reference features via a series of the robust data association steps allows a localisation solution to be achieved with a finite absolute bound precision defined by the certainty of the reference dataset. The feature-based Visual Navigation System (VNS) presented in this thesis was originally developed for a navigation application using simulated multi-year satellite image datasets. The extension of the system application into the mapping domain, in turn, has been based on the real (not simulated) flight data and imagery. In the mapping study the full potential of the system, being a versatile tool for enhancing the accuracy of the information derived from the aerial imagery has been demonstrated. Not only have the visual features, such as road networks, shorelines and water bodies, been used to obtain a position ’fix’, they have also been used in reverse for accurate mapping of vehicles detected on the roads into an inertial space with improved precision. Combined correction of the geo-coding errors and improved aircraft localisation formed a robust solution to the defense mapping application. A system of the proposed design will provide a complete independent navigation solution to an autonomous UAV and additionally give it object tracking capability

Explainable parts-based concept modeling and reasoning

State-of-the-art artificial intelligence (AI) learning algorithms heavily rely on deep learning methods that exploit correlation between inputs and outputs. While effective, these methods typically provide little insight to the reasoning process used by the machine, which makes it difficult for human users to understand the process, trust the decisions made by the system, and control emergent behaviors in the system. One method to fix this is eXplainable AI (XAI), which aims to create algorithms that perform well while also providing explanations to users about the reasoning process to mitigate the problems outlined above. In this thesis, I focus on advancing the research around XAI techniques by introducing systems that provide explanations through the use of partsbased concept modeling and reasoning. Instead of correlating input to output, I correlate input to sub-parts or features of the overall concept being learned by the system. These features are used to model and reason about a concept using an explicitly defined structure. These structures provide explanations to the user by nature of how they are defined. Specifically, I introduce a shallow and deep Adaptive Neuro-Fuzzy Inference System (ANFIS) that can reason in noisy and uncertain contexts. ANFIS provides explanations in the form of learned rules that combine features to determine the overall output concept. I apply this system to real geospatial parts-based reasoning problems and evaluate the performance and explainability of the algorithm. I discover some drawbacks to the ANFIS system as traditionally defined due to dead and diminishing gradients. This leads me to focus on how to model parts-based concepts and their inherent uncertainty in other ways, namely through Spatially Attributed Relation Graphs (SARGs). I incorporate human feedback to refine the machine learning of concepts using SARGs. Finally, I present future directions for research to build on the progress presented in this thesis.Includes bibliographical references

The Scientific Investigation of Unidentified Aerial Phenomena (UAP) Using Multimodal Ground-based Observatories

Peer reviewedPublisher PD

Modern Telemetry

Telemetry is based on knowledge of various disciplines like Electronics, Measurement, Control and Communication along with their combination. This fact leads to a need of studying and understanding of these principles before the usage of Telemetry on selected problem solving. Spending time is however many times returned in form of obtained data or knowledge which telemetry system can provide. Usage of telemetry can be found in many areas from military through biomedical to real medical applications. Modern way to create a wireless sensors remotely connected to central system with artificial intelligence provide many new, sometimes unusual ways to get a knowledge about remote objects behaviour. This book is intended to present some new up to date accesses to telemetry problems solving by use of new sensors conceptions, new wireless transfer or communication techniques, data collection or processing techniques as well as several real use case scenarios describing model examples. Most of book chapters deals with many real cases of telemetry issues which can be used as a cookbooks for your own telemetry related problems

Summary of Research 1994

The views expressed in this report are those of the authors and do not reflect the official policy or position of the Department of Defense or the U.S. Government.This report contains 359 summaries of research projects which were carried out under funding of the Naval Postgraduate School Research Program. A list of recent publications is also included which consists of conference presentations and publications, books, contributions to books, published journal papers, and technical reports. The research was conducted in the areas of Aeronautics and Astronautics, Computer Science, Electrical and Computer Engineering, Mathematics, Mechanical Engineering, Meteorology, National Security Affairs, Oceanography, Operations Research, Physics, and Systems Management. This also includes research by the Command, Control and Communications (C3) Academic Group, Electronic Warfare Academic Group, Space Systems Academic Group, and the Undersea Warfare Academic Group