High-resolution, slant-angle scene generation and validation of concealed targets in DIRSIG

Traditionally, synthetic imagery has been constructed to simulate images captured with low resolution, nadir-viewing sensors. Advances in sensor design have driven a need to simulate scenes not only at higher resolutions but also from oblique view angles. The primary efforts of this research include: real image capture, scene construction and modeling, and validation of the synthetic imagery in the reflective portion of the spectrum. High resolution imagery was collected of an area named MicroScene at the Rochester Institute of Technology using the Chester F. Carlson Center for Imaging Science\u27s MISI and WASP sensors using an oblique view angle. Three Humvees, the primary targets, were placed in the scene under three different levels of concealment. Following the collection, a synthetic replica of the scene was constructed and then rendered with the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model configured to recreate the scene both spatially and spectrally based on actual sensor characteristics. Finally, a validation of the synthetic imagery against the real images of MicroScene was accomplished using a combination of qualitative analysis, Gaussian maximum likelihood classification, grey-level co-occurrence matrix derived texture metrics, and the RX algorithm. The model was updated following each validation using a cyclical development approach. The purpose of this research is to provide a level of confidence in the synthetic imagery produced by DIRSIG so that it can be used to train and develop algorithms for real world concealed target detection

Learn to Generalize and Adapt across Domains in Semantic Segmentation

L'abstract è presente nell'allegato / the abstract is in the attachmen

Polarimetric modeling of remotely sensed scenes in the thermal infrared

This dissertation develops a polarimetric thermal infrared (IR) framework within the Digital Image and Remote Sensing Image Generation (DIRSIG) software tool enabling users in the remote sensing community to conduct system level trades and phenomenology studies. To support polarized reflection and emission modeling within DIRSIG, a generalized bi-directional reflectance distribution function (BRDF) is presented. This generalized form is a 4x4 element Mueller matrix that may be configured to resemble the commonly utilized Beard-Maxwell or Priest-Germer BRDF models. A polarized emissivity model is derived that leverages a hemispherical integration of the polarized BRDF and Kirchoff\u27s Law. A portable experimental technique for measuring polarized long-wave IR emissivity is described. Experimental results for sixteen target and background materials are fit to the polarized emissivity model. The resulting model fit parameters are ingested by DIRSIG to simulate polarized long-wave infrared scene phenomenology. Thermally emitted radiance typically has a vertical polarization orientation, while reflected background radiance is polarized horizontally. The balance between these components dictates what polarized signature (if any) is detected for a given target. In general, specular targets have a stronger emission polarization signature compared to diffusely scattering targets consistent with visible polarimetry findings. However, the influence of reflected background radiance can reduce the polarimetric signature of specular targets below a detectable threshold. In these situations, a diffusely scattering target may actually exhibit a polarization signature stronger than a specular target material. This interesting phenomenology is confirmed by experimental scene collections and DIRSIG simulations. Understanding polarimetric IR phenomenology with this level of detail is not only key for system design, but also for determining optimal collection geometries for specific tactical missions

Automated Visual Database Creation For A Ground Vehicle Simulator

This research focuses on extracting road models from stereo video sequences taken from a moving vehicle. The proposed method combines color histogram based segmentation, active contours (snakes) and morphological processing to extract road boundary coordinates for conversion into Matlab or Multigen OpenFlight compatible polygonal representations. Color segmentation uses an initial truth frame to develop a color probability density function (PDF) of the road versus the terrain. Subsequent frames are segmented using a Maximum Apostiori Probability (MAP) criteria and the resulting templates are used to update the PDFs. Color segmentation worked well where there was minimal shadowing and occlusion by other cars. A snake algorithm was used to find the road edges which were converted to 3D coordinates using stereo disparity and vehicle position information. The resulting 3D road models were accurate to within 1 meter

Disruptive Technologies with Applications in Airline & Marine and Defense Industries

Disruptive Technologies With Applications in Airline, Marine, Defense Industries is our fifth textbook in a series covering the world of Unmanned Vehicle Systems Applications & Operations On Air, Sea, and Land. The authors have expanded their purview beyond UAS / CUAS / UUV systems that we have written extensively about in our previous four textbooks. Our new title shows our concern for the emergence of Disruptive Technologies and how they apply to the Airline, Marine and Defense industries. Emerging technologies are technologies whose development, practical applications, or both are still largely unrealized, such that they are figuratively emerging into prominence from a background of nonexistence or obscurity. A Disruptive technology is one that displaces an established technology and shakes up the industry or a ground-breaking product that creates a completely new industry.That is what our book is about. The authors think we have found technology trends that will replace the status quo or disrupt the conventional technology paradigms.The authors have collaborated to write some explosive chapters in Book 5:Advances in Automation & Human Machine Interface; Social Media as a Battleground in Information Warfare (IW); Robust cyber-security alterative / replacement for the popular Blockchain Algorithm and a clean solution for Ransomware; Advanced sensor technologies that are used by UUVs for munitions characterization, assessment, and classification and counter hostile use of UUVs against U.S. capital assets in the South China Seas. Challenged the status quo and debunked the climate change fraud with verifiable facts; Explodes our minds with nightmare technologies that if they come to fruition may do more harm than good; Propulsion and Fuels: Disruptive Technologies for Submersible Craft Including UUVs; Challenge the ammunition industry by grassroots use of recycled metals; Changing landscape of UAS regulations and drone privacy; and finally, Detailing Bioterrorism Risks, Biodefense, Biological Threat Agents, and the need for advanced sensors to detect these attacks.https://newprairiepress.org/ebooks/1038/thumbnail.jp

DRONE DELIVERY OF CBNRECy – DEW WEAPONS Emerging Threats of Mini-Weapons of Mass Destruction and Disruption (WMDD)

Drone Delivery of CBNRECy – DEW Weapons: Emerging Threats of Mini-Weapons of Mass Destruction and Disruption (WMDD) is our sixth textbook in a series covering the world of UASs and UUVs. Our textbook takes on a whole new purview for UAS / CUAS/ UUV (drones) – how they can be used to deploy Weapons of Mass Destruction and Deception against CBRNE and civilian targets of opportunity. We are concerned with the future use of these inexpensive devices and their availability to maleficent actors. Our work suggests that UASs in air and underwater UUVs will be the future of military and civilian terrorist operations. UAS / UUVs can deliver a huge punch for a low investment and minimize human casualties.https://newprairiepress.org/ebooks/1046/thumbnail.jp

Heat transfer in DIRSIG an infrared synthetic scene generation model

Improvements to the thermodynamic model in the RIT Digital Imaging and Remote Sensing Lab\u27s synthetic image generation software model, DIRSIG, were made to account for three forms of heat transfer: conduction, convection, and radiation from an internal heat source. A validation was completed that collected truth data and evaluated the performance of the modifications. The simulated contrast of the final temperature images was relatively close to truth contrast. In addition, the exposed area term from the thermodynamic model was modified with the DIRSIG shape factor calculation for four different scenarios to improve background object temperature interactions. The best scenario was a replacement of the exposed area with the shape factor in the sky/background temperature equation. Finally, interpolation on weather data to decrease discrete shadow edges was performed and evaluated. This approach significandy reduced edge effects, but due to incorrect scene geometry, previous simulated imagery and previous truth imagery did not coincide, making final conclusions difficult to predict

Unmanned Aircraft Systems in the Cyber Domain

Unmanned Aircraft Systems are an integral part of the US national critical infrastructure. The authors have endeavored to bring a breadth and quality of information to the reader that is unparalleled in the unclassified sphere. This textbook will fully immerse and engage the reader / student in the cyber-security considerations of this rapidly emerging technology that we know as unmanned aircraft systems (UAS). The first edition topics covered National Airspace (NAS) policy issues, information security (INFOSEC), UAS vulnerabilities in key systems (Sense and Avoid / SCADA), navigation and collision avoidance systems, stealth design, intelligence, surveillance and reconnaissance (ISR) platforms; weapons systems security; electronic warfare considerations; data-links, jamming, operational vulnerabilities and still-emerging political scenarios that affect US military / commercial decisions. This second edition discusses state-of-the-art technology issues facing US UAS designers. It focuses on counter unmanned aircraft systems (C-UAS) – especially research designed to mitigate and terminate threats by SWARMS. Topics include high-altitude platforms (HAPS) for wireless communications; C-UAS and large scale threats; acoustic countermeasures against SWARMS and building an Identify Friend or Foe (IFF) acoustic library; updates to the legal / regulatory landscape; UAS proliferation along the Chinese New Silk Road Sea / Land routes; and ethics in this new age of autonomous systems and artificial intelligence (AI).https://newprairiepress.org/ebooks/1027/thumbnail.jp

DIRSIG digital imaging and remote sensing imaging generation model: Infrared airborne validation & input parameter analysis

The civilian and military need for high resolution infrared imagery has dramatically increased in recent times. Regardless of the user or the need, infrared imagery can provide unique information that is not available in the visible region of the electromagnetic spectrum. Just as the need for real infrared imagery has increased, so has the need for computer generated infrared imagery, also known as synthetic imagery. Synthetic imagery is created by mathematically modeling the real world and the imaging chain, encompassing everything from the target to the sensor characteristics. The amount of faith that can be placed in a synthetic image depends on its accuracy in recreating the real world. The Digital Imaging and Remote Sensing Image Generation Model (DIRSIG) at the Rochester Institute of Technology (RIT) attempts to model the real world. It creates synthetic images through the integration of scene geometry, ray-tracer, thermal, radiometry, and sensor submodels. The focus of this project lies in evaluating the ability of DIRSIG to recreate the imaging chain and produce high resolution synthetic imagery. DIRSIG synthetic imagery of the Kodak Hawkeye plant and the surrounding area was compared to aerial infrared imagery of the same region using root mean square error and rank order correlation. This comparison helped to validate the output from DIRSIG and detect inadequacies in the image chain model. In addition to validating DIRSIG, a procedure for optimizing the input parameters, incorporating a sensitivity analysis, was developed. This reduces the time involved in creating a realistic and accurate synthetic image

Automated Analysis of X-ray Images for Cargo Security

Customs and border officers are overwhelmed by the hundreds of millions of cargo containers that constitute the backbone of the global supply chain, any one of which could contain a security- or customs-related threat. Searching for these threats is akin to searching for needles in an ever-growing field of haystacks. This thesis considers novel automated image analysis methods to automate or assist elements of cargo inspection. The four main contributions of this thesis are as follows. Methods are proposed for the measurement and correction of detector wobble in large-scale transmission radiography using Beam Position Detectors (BPDs). Wobble is estimated from BPD measurements using a Random Regression Forest (RRF) model, Bayesian fused with a prior estimate from an Auto-Regression (AR). Next, a series of image corrections are derived, and it is shown that 87% of image error due to wobble can be corrected. This is the first proposed method for correction of wobble in large-scale transmission radiography. A Threat Image Projection (TIP) framework is proposed, for training, probing and evaluating Automated Threat Detection (ATD) algorithms. The TIP method is validated experimentally, and a method is proposed to test whether algorithms can learn to exploit TIP artefacts. A system for Empty Container Verification (ECV) is proposed. The system, trained using TIP, is based on Random Forest (RF) classification of image patches according to fixed geometric features and container location. The method outperforms previous reported results, and is able to detect very small amounts of synthetically concealed smuggled contraband. Finally, a method for ATD is proposed, based on a deep Convolutional Neural Network (CNN), trained from scratch using TIP, and exploits the material information encoded within dual-energy X-ray images to suppress false alarms. The system offers a 100-fold improvement in the false positive rate over prior work