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Avery Guild-Bingham will present these slides during the 9th Annual AMS Technical Exchange meeting (virtual) held on Oct 6-7, 2021. Attendees include Aerial Measuring System staff and partners from US laboratories, as well as Aerial Radiation detection scientists from partner countries such as the UK, Germany, Switzerland, etc. Overall support provided by NA-81, Office of Nuclear Incident Policy and Cooperation.
Accurate 3D reconstruction/modelling from unmanned aerial vehicle (UAV)-based imagery has become the key prerequisite in various applications. Although current commercial software has automated the process of image-based reconstruction, a transparent system, which can be incorporated with different user-defined constraints, is still preferred by the photogrammetric research community. In this regard, this paper presents a transparent framework for the automated aerial triangulation of UAV images. The proposed framework is conducted in three steps. In the first step, two approaches, which take advantage of prior information regarding the flight trajectory, are implemented for reliable relative orientation recovery. Then, initial recovery of image exterior orientation parameters (EOPs) is achieved through either an incremental or global approach. Finally, a global bundle adjustment involving Ground Control Points (GCPs) and check points is carried out to refine all estimated parameters in the defined mapping coordinate system. Four real image datasets, which are acquired by two different UAV platforms, have been utilized to evaluate the feasibility of the proposed framework. In addition, a comparative analysis between the proposed framework and the existing commercial software is performed. The derived experimental results demonstrate the superior performance of the proposed framework in providing an accurate 3D model, especially when dealing with acquired UAV images containing repetitive pattern and significant image distortions.
In low-resolution wide-area aerial imagery, object detection algorithms are categorized as feature extraction and machine learning approaches, where the former often requires a post-processing scheme to reduce false detections and the latter demands multi-stage learning followed by post-processing. In this paper, we present an approach on how to select post-processing schemes for aerial object detection. We evaluated combinations of each of ten vehicle detection algorithms with any of seven post-processing schemes, where the best three schemes for each algorithm were determined using average F-score metric. The performance improvement is quantified using basic information retrieval metrics as well as the classification of events, activities and relationships (CLEAR) metrics. We also implemented a two-stage learning algorithm using a hundred-layer densely connected convolutional neural network for small object detection and evaluated its degree of improvement when combined with the various post-processing schemes. The highest average F-scores after post-processing are 0.902, 0.704 and 0.891 for the Tucson, Phoenix and online VEDAI datasets, respectively. The combined results prove that our enhanced three-stage post-processing scheme achieves a mean average precision (mAP) of 63.9% for feature extraction methods and 82.8% for the machine learning approach.
Measurement of bridge displacements is important for ensuring the safe operation of railway bridges. Traditionally, contact sensors such as Linear Variable Displacement Transducers (LVDT) and accelerometers have been used to measure the displacement of the railway bridges. However, these sensors need significant effort in installation and maintenance. Therefore, railroad management agencies are interested in new means to measure bridge displacements. This research focuses on mounting Laser Doppler Vibrometer (LDV) on an Unmanned Aerial System (UAS) to enable contact-free transverse dynamic displacement of railroad bridges. Researchers conducted three field tests by flying the Unmanned Aerial Systems Laser Doppler Vibrometer (UAS-LDV) 1.5 m away from the ground and measured the displacement of a moving target at various distances. The accuracy of the UAS-LDV measurements was compared to the Linear Variable Differential Transducer (LVDT) measurements. The results of the three field tests showed that the proposed system could measure non-contact, reference-free dynamic displacement with an average peak and root mean square (RMS) error for the three experiments of 10% and 8% compared to LVDT, respectively. Such errors are acceptable for field measurements in railroads, as the interest prior to bridge monitoring implementation of a new approach is to demonstrate similar success for different flights, as reported in the three results. This study also identified barriers for industrial adoption of this technology and proposed operational development practices for both technical and cost-effective implementation.
This report discusses work completed over the summer at the Jet Propulsion Laboratory (JPL), California Institute of Technology. A system is presented to guide ground or aerial unmanned robots using computer vision. The system performs accurate camera calibration, camera pose refinement and surface extraction from images collected by a camera mounted on the vehicle. The application motivating the research is planetary exploration and the vehicles are typically rovers or unmanned aerial vehicles. The information extracted from imagery is used primarily for navigation, as robot location is the same as the camera location and the surfaces represent the terrain that rovers traverse. The processed information must be very accurate and acquired very fast in order to be useful in practice. The main challenge being addressed by this project is to achieve high estimation accuracy and high computation speed simultaneously, a difficult task due to many technical reasons.
This document is a slide show type presentation concerning DOE and Aerial Measuring System (AMS) activities and results with respect to assessing the consequences of the releases from the Fukushima Dai-ichi Nuclear Power Plant. These include ground monitoring and aerial monitoring.
Aerial radiation detection is routinely used by many organizations (DHS, DOE, EPA, etc.) for the purposes of identifying the presence of and quantifying the existence of radiation along the ground. This work involves the search for lost or missing sources, as well as the characterization of large-scale releases such as might occur in a nuclear power plant accident. The standard in aerial radiological surveys involves flying large arrays of sodium-iodide detectors at altitude (15 to 700 meters) to acquire geo-referenced, 1 Hz, 1024-channel spectra. The historical shortfalls of this technology include: • Very low spatial resolution (typical field of view is circle of two-times altitude) • Relatively low detectability associated with large stand-off distances • Fundamental challenges in performing ground-level quantification This work uses modern computational power in conjunction with multi-dimensional deconvolution algorithms in an effort to improve spatial resolution, enhance detectability, and provide a robust framework for quantification.
The Office of Legacy management asked the Aerial Measurement System to perform an aerial survey and analyze the results for a three uranium mines in Wyoming
Unmanned aerial vehicles (UAVs) are now widely employed as part of numerous applications including defence, search and rescue as well as within scientific fields such as high-altitude atmospheric sampling and remote sensing, to name but a few. However, their application to the high-resolution detection of radiation anomalies (specifically as part of the routine monitoring on nuclear sites) has been less well explored. In this work, we present the results of the radiation monitoring via a lightweight aerial platform on an active nuclear site (Sellafield Ltd.); having already deployed the device in the Fukushima-contaminated region. The system employed was able to detect regions of elevated radiation at the sub-meter scale as well as attributing the species responsible. Such a system presents an extremely powerful tool where it is not desirable, nor practical, to send human operators. Results presented show that the platform is easily capable of operating within the challenging and confined settings of a site such as Sellafield (or other similar sites worldwide). (authors)
This paper examines two lesser known, but technological mature, unmanned aerial vehicle (UAVs) approaches to working in indoor spaces. Tethered UAVs reduce safety concerns by physically limiting the range of the UAV. The tether can provide other advantages including supplying long-term power, carrying heavier payloads, enabling uninterrupted communications, better performance in high winds, and providing more flexibility in viewpoints than an aerial mast. Caged UAVs reduce safety concerns by protecting the UAV, and its environment, form damage due to collisions or contact. The protective cage can offer other advantages including bracing or rolling the UAV over a surface to simply control or reduce energy, lessen wind resistance, and allow the UAV to inspect a structure at close, repeatable distances. The paper established the problems associated with flying UAVs in indoor spaces. It reviews the state of the practice for both tethered and caged UAVs, then compares the two approaches to traditional multi-rotor UAVs. The paper concludes that commercially available tethered and caged UAVs are useful and ready for immediate adoption. (authors)
The P- and R-Area Reactor buildings, located at SRS near Aiken, SC, were in-situ decommissioned by grouting below-grade portions of the buildings and demolishing some above-grade structures to grade level. Other reactor building structures were left above-grade and sealed to prevent human or animal access. Because the above grade-structures are expected to continue in their present state for hundreds of years, the condition of the building roofs is critical to mitigate rainwater intrusion. For this reason, building roof areas were strengthened with high strength concrete to ensure the long-term integrity of the roof structure. Periodic inspections of the roof structures are required to ensure that the roofs are functioning properly and to identify damage areas. The traditional method of inspecting closed reactor buildings requires the use of a helicopter and photographer to generate photographs and videos for review. This method proved sufficient but lacked the resolution and clarity that is required for a thorough inspection of the building structure. Fortunately, the Savannah River National Laboratory (SRNL) has established a Small Unmanned Aircraft System (sUAS) program using commercially available and custom-built remote-controlled aircraft. EC and ACP contacted the SRNL program to evaluate whether the sUAS technology could be employed for periodic reactor building inspections. The sUAS can fly within a few meters of the reactor buildings and hover, allowing for a more thorough aerial inspection. The initial sUAS inspection of the P-Area Reactor building was completed in February 2018 and at the R-Area Reactor building in August 2018 and were successful in providing higher resolution photos and videos. The sUAS inspections also revealed that vegetation had begun to grow on the roofs that could potentially damage the structure integrity. SRNL partnered with Virginia Polytechnic Institute and State University to build a custom heavy lift sUAS that could be equipped with herbicides to remotely treat the vegetative growth. Herbicides were successfully applied to the R-Area Reactor building roof using the custom sUAS in September 2018. The use of unmanned aircraft systems at SRS to perform aerial inspections and herbicide treatment in otherwise inaccessible areas has proven to be an efficient and cost-effective technology that provides high value, increases knowledge of facility conditions, and provides for early detection of damage. Periodic inspections of the reactor building roofs using sUAS technology are performed safely, efficiently, and at a significant cost and schedule savings while reducing emissions, noise, and fossil fuel use. The use of sUAS equipment for building inspections and herbicide application at SRS is unique within the DOE complex. The goal of SRS is to apply this remote technology to other waste unit operations and maintenance activities in the future. (authors)
In this research, we examine the relationship between aerial IR defect analysis and photovoltaic (PV) performance data for twelve utility- and commercial-scale solar sites in the United States. To do this, we fuse the site diagram geoJSON's, aerial infrared thermography (aIRT) defect analyses, and associated inverter time series, allowing for a direct comparison between site defects and time series data. Defect analyses were provided by Zeitview, under its Solar Insights platform. Following the data fusion process, we look at the relationship between system performance and aIRT defects. We investigate the relationship between degradation and hotspot defects, as well as the relationship between AC power data and offline strings and misaligned modules. In general, system degradation was not affected by long-term or balance-of-system (BoS) defects as they occurred infrequently in the data set. However, for one system, a near statistically significant relationship (p-value=0.057) was found when comparing the degradation of inverter blocks with several multi-hotspot defects to all other inverter blocks without this particular defect. There was strong alignment when comparing short-term recoverable module defects such as stuck trackers and offline strings to time series data. In general, we found that when an inverter block has more than 80% of modules flagged for one of these defects, its AC power time data is flat-lined and the inverter block is not producing.
Tracking Earth’s past helps us to move from hindsight to foresight in seeking landscape sustainability, a pursuit aided by modern mapping capabilities but hindered by a dearth of historical landscape information. To fill the data gap and exemplify the use of old map for land-use change sciences, we combined a paper-based US civil war map and aerial photos to derive land-use history and landscape dynamics at fine scales for a region near Chancellorsville, VA, USA, from 1867 to 2014. We also tested how advanced algorithms—object-based analysis and random forests (RF)—could aid in data processing. Automatic classification of the 1867 map proved difficult, but its manual digitization could benefit from object-based image segmentation. Classifying digital aerial images was more accurate via the object-based than pixel-based method, but only if the images were segmented appropriately. In the object-based classification, spectral-based features were much more important than shape/geometry features, as ranked by RF. During the 147 years, 32% of the region changed in land type. Settlement and roads increased in extent by 1850% and 691%, respectively, and woodland decreased by 19%. These changes fragmented the landscape, altered the hydrological regime, and affected river morphology. The utility of old maps exemplified here provides an impetus for exploring and leveraging extant old maps or historical records to support land-use and global change research. Our study also connotes the importance of geotagging current data, such as photos and videos, that may serve as a baseline to document future landscape change.
Machine learning (ML) can accelerate biological research. However, the adoption of such tools to facilitate phenotyping based on sensor data has been limited by (i) the need for a large amount of human-annotated training data for each context in which the tool is used and (ii) phenotypes varying across contexts defined in terms of genetics and environment. This is a major bottleneck because acquiring training data is generally costly and time-consuming. This study demonstrates how a ML approach can address these challenges by minimizing the amount of human supervision needed for tool building. A case study was performed to compare ML approaches that examine images collected by an uncrewed aerial vehicle to determine the presence/absence of panicles (i.e. “heading”) across thousands of field plots containing genetically diverse breeding populations of 2 Miscanthus species. Automated analysis of aerial imagery enabled the identification of heading approximately 9 times faster than in-field visual inspection by humans. Leveraging an Efficiently Supervised Generative Adversarial Network (ESGAN) learning strategy reduced the requirement for human-annotated data by 1 to 2 orders of magnitude compared to traditional, fully supervised learning approaches. The ESGAN model learned the salient features of the data set by using thousands of unlabeled images to inform the discriminative ability of a classifier so that it required minimal human-labeled training data. This method can accelerate the phenotyping of heading date as a measure of flowering time in Miscanthus across diverse contexts (e.g. in multistate trials) and opens avenues to promote the broad adoption of ML tools.
Feature representation is a key step in automated visual content interpretation. In this letter, we present a robust feature representation technique, referred to as bag of lines (BoL), for high-resolution aerial scenes. The proposed technique involves extracting and compactly representing low-level line primitives from the scene. The compact scene representation is generated by counting the different types of lines representing various linear structures in the scene. Through extensive experiments, we show that the proposed scene representation is invariant to scale changes and scene conditions and can discriminate urban scene categories accurately. We compare the BoL representation with the popular scale invariant feature transform (SIFT) and Gabor wavelets for their classification and clustering performance on an aerial scene database consisting of images acquired by sensors with different spatial resolutions. The proposed BoL representation outperforms the SIFT- and Gabor-based representations.
The importance of satellite imagery analysis has increased dramatically over the last several years, keeping pace with the rapid improvements seen in both remote sensing platforms and sensors. As this field expands, so too does the interest in using machine learning methods to automate parts of the imagery analyst’s workflow. In this paper we address one aspect of this challenge: the development of a method for the automatic extraction of parking lots from aerial imagery. To the best of our knowledge, there has been no prior work conducted on the development of an end-to-end pipeline for this particular task. Due to the limited size of our dataset and to accommodate the potentially limited size of future datasets, we propose a deep learning approach using transfer learning. This process hinges upon the use of state of the art Convolutional Neural Networks (CNNs), trained on general image classification datasets. These networks were then fine-tuned on our custom dataset, to establish a comprehensive benchmark for this task. Our method exhibits promising results for automatic parking lot extraction, and is generalizable enough to work with different input types, including high resolution aerial orthoimagery, satellite imagery, full motion video (FMV), and UAV imagery.
Airborne gamma-ray surveys are useful for many applications, ranging from geology and mining to public health and nuclear security. In all these contexts, the ability to decompose a measured spectrum into a linear combination of background source terms can provide useful insights into the data and lead to improvements in the techniques that use spectral energy windows. Multiple methods for the linear decomposition of spectra exist but are subject to various drawbacks, such as allowing negative photon fluxes or requiring detailed Monte Carlo modeling. In this work, we propose using non-negative matrix factorization (NMF) as a data-driven approach to spectral decomposition. Using aerial surveys that include flights over water, we demonstrate that the mathematical approach of NMF finds physically relevant structure in the aerial gamma-ray background, namely, that measured spectra can be expressed as the sum of nearby terrestrial emission, distant terrestrial emission, and radon and cosmic emission. Furthermore, these NMF background components are compared with the background components obtained by noise-adjusted singular value decomposition (NASVD), which contain negative photon fluxes and, thus, do not represent the emission spectra in as straightforward a way. Finally, we comment on the potential areas of research that are enabled by NMF decompositions, such as new approaches to spectral anomaly detection and data fusion.
In this second part of a multipaper series, we present results from outdoor aerial measurements of surrogate distributed gamma-ray sources. Here we detail the design, manufacture, and testing of 300 individual ~7 mCi Cu-64 sealed sources at the Washington State University (WSU) research reactor and their deployment in various source patterns (each comprising up to 100 point sources) during the aerial measurement campaign. We show the results of two such measurements, in which approximate source shapes and qualitative source intensities can be seen from the count rate versus position plots, even without performing reconstructions. We also detail our efforts in ground-truthing the deployed sources and comparing measured gamma-ray data to model predictions. In particular, we compare measured versus expected count data using the Poisson deviance formalism of Part I to evaluate whether the fielded surrogate point-source arrays “look like” their truly continuous distributed source analogs. More generally, we find that the point-source array technique provides high source placement accuracy, relative ease of quantifying the true source configuration, scalability to source dimensions of ≲100 m, ease of reconfiguration and removal, and relatively low dose to personnel. Finally, we consider potential improvements and generalizations of the point-source array technique for future measurement campaigns.