Multiple Targets Geolocation Using SIFT and Stereo Vision on Airborne Video Sequences

We propose a robust and accurate method for multi-target geo-localization from airborne video. The difference between our approach and other approaches in the literature is fourfold: 1) it does not require gimbal control of the camera or any particular path planning control for the UAV; 2) it can instantaneously geolocate multiple targets even if they were not previously observed by the camera; 3) it does not require a georeferenced terrain database nor an altimeter for estimating the UAV's and the target's altitudes; and 4) it requires only one camera, but it employs a multi-stereo technique using the image sequence for increased accuracy in target geo-location. The only requirements for our approach are: that the intrinsic parameters of the camera be known; that the on board camera be equipped with global positioning system (GPS) and inertial measurement unit (IMU); and that enough feature points can be extracted from the surroundings of the target. Since the first two constraints are easily satisfied, the only real requirement is regarding the feature points. However, as we explain later, this last constraint can also be alleviated if the ground is approximately planar. The result is a method that can reach a few meters of accuracy for an UAV flying at a few hundred meters above the ground. Such performance is demonstrated by computer simulation, in-scale data using a model city, and real airborne video with ground truth

U.S. Unmanned Aerial Vehicles (UAVS) and Network Centric Warfare (NCW) impacts on combat aviation tactics from Gulf War I through 2007 Iraq

Unmanned, aerial vehicles (UAVs) are an increasingly important element of many modern militaries. Their success on battlefields in Afghanistan, Iraq, and around the globe has driven demand for a variety of types of unmanned vehicles. Their proven value consists in low risk and low cost, and their capabilities include persistent surveillance, tactical and combat reconnaissance, resilience, and dynamic re-tasking. This research evaluates past, current, and possible future operating environments for several UAV platforms to survey the changing dynamics of combat-aviation tactics and make recommendations regarding UAV employment scenarios to the Turkish military. While UAVs have already established their importance in military operations, ongoing evaluations of UAV operating environments, capabilities, technologies, concepts, and organizational issues inform the development of future systems. To what extent will UAV capabilities increasingly define tomorrow's missions, requirements, and results in surveillance and combat tactics? Integrating UAVs and concepts of operations (CONOPS) on future battlefields is an emergent science. Managing a transition from manned- to unmanned and remotely piloted aviation platforms involves new technological complexity and new aviation personnel roles, especially for combat pilots. Managing a UAV military transformation involves cultural change, which can be measured in decades.http://archive.org/details/usunmannedaerial109454211Turkish Air Force authors.Approved for public release; distribution is unlimited

Military Application of Aerial Photogrammetry Mapping Assisted by Small Unmanned Air Vehicles

This research investigated the practical military applications of the photogrammetric methods using remote sensing assisted by small unmanned aerial vehicles (SUAVs). The research explored the feasibility of UAV aerial mapping in terms of the specific military purposes, focusing on the geolocational and measurement accuracy of the digital models, and image processing time. The research method involved experimental flight tests using low-cost Commercial off-the-shelf (COTS) components, sensors and image processing tools to study key features of the method required in military like location accuracy, time estimation, and measurement capability. Based on the results of the data analysis, two military applications are defined to justify the feasibility and utility of the methods. The first application is to assess the damage of an attacked military airfield using photogrammetric digital models. Using a hex-rotor test platform with Sony A6000 camera, georeferenced maps with 1 meter accuracy was produced and with sufficient resolution (about 1 cm/pixel) to identify foreign objects on the runway. The other case examines the utility and quality of the targeting system using geo-spatial data from reconstructed 3-Dimensional (3-D) photogrammetry models. By analyzing 3-D model, operable targeting under 1meter accuracy with only 5 percent error on distance, area, and volume wer

Transforming scientific research and development in precision agriculture : the case of hyperspectral sensing and imaging : a thesis presented in partial fulfilment of the requirements for the degree of Doctor in Philosophy in Agriculture at Massey University, Manawatū, New Zealand. EMBARGOED until 30 September 2023.

Embargoed until 30 September 2023There has been increasing social and academic debate in recent times surrounding the arrival of agricultural big data. Capturing and responding to real world variability is a defining objective of the rapidly evolving field of precision agriculture (PA). While data have been central to knowledge-making in the field since its inception in the 1980s, research has largely operated in a data-scarce environment, constrained by time-consuming and expensive data collection methods. While there is a rich tradition of studying scientific practice within laboratories in other fields, PA researchers have rarely been the explicit focal point of detailed empirical studies, especially in the laboratory setting. The purpose of this thesis is to contribute to new knowledge of the influence of big data technologies through an ethnographic exploration of a working PA laboratory. The researcher spent over 30 months embedded as a participant observer of a small PA laboratory, where researchers work with nascent data rich remote sensing technologies. To address the research question: “How do the characteristics of technological assemblages affect PA research and development?” the ethnographic case study systematically identifies and responds to the challenges and opportunities faced by the science team as they adapt their scientific processes and resources to refine value from a new data ecosystem. The study describes the ontological characteristics of airborne hyperspectral sensing and imaging data employed by PA researchers. Observations of the researchers at work lead to a previously undescribed shift in the science process, where effort moves from the planning and performance of the data collection stage to the data processing and analysis stage. The thesis develops an argument that changing data characteristics are central to this shift in the scientific method researchers are employing to refine knowledge and value from research projects. Importantly, the study reveals that while researchers are working in a rapidly changing environment, there is little reflection on the implications of these changes on the practice of science-making. The study also identifies a disjunction to how science is done in the field, and what is reported. We discover that the practices that provide disciplinary ways of doing science are not established in this field and moments to learn are siloed because of commercial constraints the commercial structures imposed in this case study of contemporary PA research

PHALANX: Expendable Projectile Sensor Networks for Planetary Exploration

Technologies enabling long-term, wide-ranging measurement in hard-to-reach areas are a critical need for planetary science inquiry. Phenomena of interest include flows or variations in volatiles, gas composition or concentration, particulate density, or even simply temperature. Improved measurement of these processes enables understanding of exotic geologies and distributions or correlating indicators of trapped water or biological activity. However, such data is often needed in unsafe areas such as caves, lava tubes, or steep ravines not easily reached by current spacecraft and planetary robots. To address this capability gap, we have developed miniaturized, expendable sensors which can be ballistically lobbed from a robotic rover or static lander - or even dropped during a flyover. These projectiles can perform sensing during flight and after anchoring to terrain features. By augmenting exploration systems with these sensors, we can extend situational awareness, perform long-duration monitoring, and reduce utilization of primary mobility resources, all of which are crucial in surface missions. We call the integrated payload that includes a cold gas launcher, smart projectiles, planning software, network discovery, and science sensing: PHALANX. In this paper, we introduce the mission architecture for PHALANX and describe an exploration concept that pairs projectile sensors with a rover mothership. Science use cases explored include reconnaissance using ballistic cameras, volatiles detection, and building timelapse maps of temperature and illumination conditions. Strategies to autonomously coordinate constellations of deployed sensors to self-discover and localize with peer ranging (i.e. a local GPS) are summarized, thus providing communications infrastructure beyond-line-of-sight (BLOS) of the rover. Capabilities were demonstrated through both simulation and physical testing with a terrestrial prototype. The approach to developing a terrestrial prototype is discussed, including design of the launching mechanism, projectile optimization, micro-electronics fabrication, and sensor selection. Results from early testing and characterization of commercial-off-the-shelf (COTS) components are reported. Nodes were subjected to successful burn-in tests over 48 hours at full logging duty cycle. Integrated field tests were conducted in the Roverscape, a half-acre planetary analog environment at NASA Ames, where we tested up to 10 sensor nodes simultaneously coordinating with an exploration rover. Ranging accuracy has been demonstrated to be within +/-10cm over 20m using commodity radios when compared to high-resolution laser scanner ground truthing. Evolution of the design, including progressive miniaturization of the electronics and iterated modifications of the enclosure housing for streamlining and optimized radio performance are described. Finally, lessons learned to date, gaps toward eventual flight mission implementation, and continuing future development plans are discussed

Monitoring snow depth change across a range of landscapes with ephemeral snowpacks using structure from motion applied to lightweight unmanned aerial vehicle videos

Differencing of digital surface models derived from structure from motion (SfM) processing of airborne imagery has been used to produce snow depth (SD) maps with between  ∼ 2 and  ∼ 15&thinsp;cm horizontal resolution and accuracies of ±10&thinsp;cm over relatively flat surfaces with little or no vegetation and over alpine regions. This study builds on these findings by testing two hypotheses across a broader range of conditions: (i) that the vertical accuracy of SfM processing of imagery acquired by commercial low-cost unmanned aerial vehicle (UAV) systems can be adequately modelled using conventional photogrammetric theory and (ii) that SD change can be more accurately estimated by differencing snow-covered elevation surfaces rather than differencing a snow-covered and snow-free surface. A total of 71 UAV missions were flown over five sites, ranging from short grass to a regenerating forest, with ephemeral snowpacks. Point cloud geolocation performance agreed with photogrammetric theory that predicts uncertainty is proportional to UAV altitude and linearly related to horizontal uncertainty. The root-mean-square difference (RMSD) over the observation period, in comparison to the average of in situ measurements along  ∼ 50&thinsp;m transects, ranged from 1.58 to 10.56&thinsp;cm for weekly SD and from 2.54 to 8.68&thinsp;cm for weekly SD change. RMSD was not related to microtopography as quantified by the snow-free surface roughness. SD change uncertainty was unrelated to vegetation cover but was dominated by outliers corresponding to rapid in situ melt or onset; the median absolute difference of SD change ranged from 0.65 to 2.71&thinsp;cm. These results indicate that the accuracy of UAV-based estimates of weekly snow depth change was, excepting conditions with deep fresh snow, substantially better than for snow depth and was comparable to in situ methods.</p

Forest disturbance and recovery: A general review in the context of spaceborne remote sensing of impacts on aboveground biomass and canopy structure

Abrupt forest disturbances generating gaps \u3e0.001 km2 impact roughly 0.4–0.7 million km2a−1. Fire, windstorms, logging, and shifting cultivation are dominant disturbances; minor contributors are land conversion, flooding, landslides, and avalanches. All can have substantial impacts on canopy biomass and structure. Quantifying disturbance location, extent, severity, and the fate of disturbed biomass will improve carbon budget estimates and lead to better initialization, parameterization, and/or testing of forest carbon cycle models. Spaceborne remote sensing maps large-scale forest disturbance occurrence, location, and extent, particularly with moderate- and fine-scale resolution passive optical/near-infrared (NIR) instruments. High-resolution remote sensing (e.g., ∼1 m passive optical/NIR, or small footprint lidar) can map crown geometry and gaps, but has rarely been systematically applied to study small-scale disturbance and natural mortality gap dynamics over large regions. Reducing uncertainty in disturbance and recovery impacts on global forest carbon balance requires quantification of (1) predisturbance forest biomass; (2) disturbance impact on standing biomass and its fate; and (3) rate of biomass accumulation during recovery. Active remote sensing data (e.g., lidar, radar) are more directly indicative of canopy biomass and many structural properties than passive instrument data; a new generation of instruments designed to generate global coverage/sampling of canopy biomass and structure can improve our ability to quantify the carbon balance of Earth\u27s forests. Generating a high-quality quantitative assessment of disturbance impacts on canopy biomass and structure with spaceborne remote sensing requires comprehensive, well designed, and well coordinated field programs collecting high-quality ground-based data and linkages to dynamical models that can use this information

Application of the Augmented Operator Function Model for Developing Performance Metrics in Persistent Surveillance

Difficulties with the implementation of persistent Wide Area Motion Imagery (WAMI) sensors to support real-time military missions have risen within Intelligence, Surveillance, and Reconnaissance organizations. In this study, cognitive models were developed of operators performing real-time missions currently supported by narrow field of view Full Motion Video (FMV) and WAMI sensors. These models were used in conjunction with a cognitive task analysis, creating an augmented operator function model (OFM-COG). This thesis describes the OFM-COG and demonstrates how this model-based analysis technique can document the cognitive implications of persistent surveillance with motion imagery. The analytic procedures required to build this model result in a methodology for the definition of an information display system specific for intelligence analysis tasks. Specifically, the models developed examine the cognitive demands of an Imagery Analyst (IA) during a real-time mission, with WAMI and/or FMV. From this, a set of cognitive metrics for analyst performance were identified for the real-time military missions in persistent surveillance

Development of high-precision snow mapping tools for Arctic environments

Le manteau neigeux varie grandement dans le temps et l’espace, il faut donc de nombreux points d’observation pour le décrire précisément et ponctuellement, ce qui permet de valider et d’améliorer la modélisation de la neige et les applications en télédétection. L’analyse traditionnelle par des coupes de neige dévoile des détails pointus sur l’état de la neige à un endroit et un moment précis, mais est une méthode chronophage à laquelle la distribution dans le temps et l’espace font défaut. À l’opposé sur la fourchette de la précision, on retrouve les solutions orbitales qui couvrent la surface de la Terre à intervalles réguliers, mais à plus faible résolution. Dans l’optique de recueillir efficacement des données spatiales sur la neige durant les campagnes de terrain, nous avons développé sur mesure un système d’aéronef télépiloté (RPAS) qui fournit des cartes d’épaisseur de neige pour quelques centaines de mètres carrés, selon la méthode Structure from motion (SfM). Notre RPAS peut voler dans des températures extrêmement froides, au contraire des autres systèmes sur le marché. Il atteint une résolution horizontale de 6 cm et un écart-type d’épaisseur de neige de 39 % sans végétation (48,5 % avec végétation). Comme la méthode SfM ne permet pas de distinguer les différentes couches de neige, j’ai développé un algorithme pour un radar à onde continue à modulation de fréquence (FM-CW) qui permet de distinguer les deux couches principales de neige que l’on retrouve régulièrement en Arctique : le givre de profondeur et la plaque à vent. Les distinguer est crucial puisque les caractéristiques différentes des couches de neige font varier la quantité d’eau disponible pour l’écosystème lors de la fonte. Selon les conditions sur place, le radar arrive à estimer l’épaisseur de neige selon un écart-type entre 13 et 39 %. vii Finalement, j’ai équipé le radar d’un système de géolocalisation à haute précision. Ainsi équipé, le radar a une marge d’erreur de géolocalisation d’en moyenne <5 cm. À partir de la mesure radar, on peut déduire la distance entre le haut et le bas du manteau neigeux. En plus de l’épaisseur de neige, on obtient également des points de données qui permettent d’interpoler un modèle d’élévation de la surface solide sous-jacente. J’ai utilisé la méthode de structure triangulaire (TIN) pour toutes les interpolations. Le système offre beaucoup de flexibilité puisqu’il peut être installé sur un RPAS ou une motoneige. Ces outils épaulent la modélisation du couvert neigeux en fournissant des données sur un secteur, plutôt que sur un seul point. Les données peuvent servir à entraîner et à valider les modèles. Ainsi améliorés, ils peuvent, par exemple, permettre de prédire la taille, le niveau de santé et les déplacements de populations d’ongulés, dont la survie dépend de la qualité de la neige. (Langlois et coll., 2017.) Au même titre que la validation de modèles de neige, les outils présentés permettent de comparer et de valider d’autres données de télédétection (par ex. satellites) et d’élargir notre champ de compréhension. Finalement, les cartes ainsi créées peuvent aider les écologistes à évaluer l’état d’un écosystème en leur donnant accès à une plus grande quantité d’information sur le manteau neigeux qu’avec les coupes de neige traditionnelles.Abstract: Snow is highly variable in time and space and thus many observation points are needed to describe the present state of the snowpack accurately. This description of the state of the snowpack is necessary to validate and improve snow modeling efforts and remote sensing applications. The traditional snowpit analysis delivers a highly detailed picture of the present state of the snow in a particular location but lacks the distribution in space and time as it is a time-consuming method. On the opposite end of the spatial scale are orbital solutions covering the surface of the Earth in regular intervals, but at the cost of a much lower resolution. To improve the ability to collect spatial snow data efficiently during a field campaign, we developed a custom-made, remotely piloted aircraft system (RPAS) to deliver snow depth maps over a few hundred square meters by using Structure-from-Motion (SfM). The RPAS is capable of flying in extremely low temperatures where no commercial solutions are available. The system achieves a horizontal resolution of 6 cm with snow depth RMSE of 39% without vegetation (48.5% with vegetation) As the SfM method does not distinguish between different snow layers, I developed an algorithm for a frequency modulated continuous wave (FMCW) radar that distinguishes between the two main snow layers that are found regularly in the Arctic: “Depth Hoar” and “Wind Slab”. The distinction is important as these characteristics allow to determine the amount of water stored in the snow that will be available for the ecosystem during the melt season. Depending on site conditions, the radar estimates the snow depth with an RMSE between 13% and 39%. v Finally, I equipped the radar with a high precision geolocation system. With this setup, the geolocation uncertainty of the radar on average < 5 cm. From the radar measurement, the distance to the top and the bottom of the snowpack can be extracted. In addition to snow depth, it also delivers data points to interpolate an elevation model of the underlying solid surface. I used the Triangular Irregular Network (TIN) method for any interpolation. The system can be mounted on RPAS and snowmobiles and thus delivers a lot of flexibility. These tools will assist snow modeling as they provide data from an area instead of a single point. The data can be used to force or validate the models. Improved models will help to predict the size, health, and movements of ungulate populations, as their survival depends on it (Langlois et al., 2017). Similar to the validation of snow models, the presented tools allow a comparison and validation of other remote sensing data (e.g. satellite) and improve the understanding limitations. Finally, the resulting maps can be used by ecologist to better asses the state of the ecosystem as they have a more complete picture of the snow cover on a larger scale that it could be achieved with traditional snowpits