Engineering Commons^™

Sc-Fuse: A Feature Fusion Approach For Unpaved Road Detection From Remotely Sensed Images, Aniruddh Saxena

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

Road network extraction from remote sensing imagery is crucial for numerous applications, ranging from autonomous navigation to urban and rural planning. A particularly challenging aspect is the detection of unpaved roads, often underrepresented in research and data. These roads display variability in texture, width, shape, and surroundings, making their detection quite complex. This thesis addresses these challenges by creating a specialized dataset and introducing the SC-Fuse model.

Our custom dataset comprises high resolution remote sensing imagery which primarily targets unpaved roads of the American Midwest. To capture the diverse seasonal variation and their impact, the dataset includes images from different …

Reducing Uncertainty In Sea-Level Rise Prediction: A Spatial-Variability-Aware Approach, Subhankar Ghosh, Shuai An, Arun Sharma, Jayant Gupta, Shashi Shekhar, Aneesh Subramanian

I-GUIDE Forum

Given multi-model ensemble climate projections, the goal is to accurately and reliably predict future sea-level rise while lowering the uncertainty. This problem is important because sea-level rise affects millions of people in coastal communities and beyond due to climate change's impacts on polar ice sheets and the ocean. This problem is challenging due to spatial variability and unknowns such as possible tipping points (e.g., collapse of Greenland or West Antarctic ice-shelf), climate feedback loops (e.g., clouds, permafrost thawing), future policy decisions, and human actions. Most existing climate modeling approaches use the same set of weights globally, during either regression or …

A Comparative Study Of Vinti-Based Orbit Propagation And Estimation For Cubesats In Very Low Earth Orbits, Ethan Michael Senecal

Mechanical & Aerospace Engineering Theses & Dissertations

In recent years, there has been a growing interest in CubeSats and very low Earth orbit (VLEO) space missions. Mission SeaLion, a collaborative CubeSat mission between Old Dominion University, the U.S. Coast Guard Academy, and U.S. Air Force Institute of Technology, planned to launch a 3U CubeSat into VLEO. The VLEO mission is a particularly challenging environment for navigation and orbit propagation because drag introduces a significant perturbation for orbit models such as SGP4. Additionally, mission requirements left no capacity for attitude determination or control, further reducing knowledge of drag behavior of the satellite in flight. This deficiency is a …

Power Outage And Environmental Justice In Winter Storm Uri: An Analytical Workflow Based On Nighttime Light Remote Sensing, Jinwen Xu, Yi Qiang, Heng Cai, Lei Zou

GIS Center

The intensity of extreme weather events has been increasing, posing a unique threat to society and highlighting the importance of our electrical power system, a key component in our infrastructure. In severe weather events, quickly identifying power outage impact zones and affected communities is crucial for informed disaster response. However, a lack of household-level power outage data impedes timely and precise assessments. To address these challenges, we introduced an analytical workflow using NASA’s Black Marble daily nighttime light (NTL) images to detect power outages from the 2021 Winter Storm Uri. This workflow includes adjustments to mitigate viewing angle and snow …

Lidar Buoy Detection For Autonomous Marine Vessel Using Pointnet Classification, Christopher Adolphi, Dorothy Dorie Parry, Yaohang Li, Masha Sosonkina, Ahmet Saglam, Yiannis E. Papelis

Modeling, Simulation and Visualization Student Capstone Conference

Maritime autonomy, specifically the use of autonomous and semi-autonomous maritime vessels, is a key enabling technology supporting a set of diverse and critical research areas, including coastal and environmental resilience, assessment of waterway health, ecosystem/asset monitoring and maritime port security. Critical to the safe, efficient and reliable operation of an autonomous maritime vessel is its ability to perceive on-the-fly the external environment through onboard sensors. In this paper, buoy detection for LiDAR images is explored by using several tools and techniques: machine learning methods, Unity Game Engine (herein referred to as Unity) simulation, and traditional image processing. The Unity Game …

Small Unmanned Aircraft Systems: Operator Workload And Situation Awareness Utilizing First Person View Techniques, Ross Lucas Stephenson Jr

Doctoral Dissertations and Master's Theses

The small, unmanned aircraft systems (sUAS) sector within the aviation industry is experiencing unprecedented growth. However, the regulatory guidance for the safe integration of sUAS into the National Airspace System (NAS) has not kept pace with this technological growth within the market. Current regulatory limitations of line-of-sight operations may have an impact on the establishment of an equivalent level of safety for sUAS operations as maintained by manned aircraft. The focal point of the discussion of line-of-sight operations has been the ability of the sUAS pilot to see and avoid all obstacles and other aircraft in a safe and timely …

Chatgpt As Metamorphosis Designer For The Future Of Artificial Intelligence (Ai): A Conceptual Investigation, Amarjit Kumar Singh (Library Assistant), Dr. Pankaj Mathur (Deputy Librarian)

Library Philosophy and Practice (e-journal)

Abstract

Purpose: The purpose of this research paper is to explore ChatGPT’s potential as an innovative designer tool for the future development of artificial intelligence. Specifically, this conceptual investigation aims to analyze ChatGPT’s capabilities as a tool for designing and developing near about human intelligent systems for futuristic used and developed in the field of Artificial Intelligence (AI). Also with the helps of this paper, researchers are analyzed the strengths and weaknesses of ChatGPT as a tool, and identify possible areas for improvement in its development and implementation. This investigation focused on the various features and functions of ChatGPT that …

Estimating Solar Energy Production In Urban Areas For Electric Vehicles, Shaimaa Ahmed

Theses and Dissertations

Cities have a high potential for solar energy from PVs installed on buildings' rooftops. There is an increased demand for solar energy in cities to reduce the negative effect of climate change. The thesis investigates solar energy potential in urban areas. It tries to determine how to detect and identify available rooftop areas, how to calculate suitable ones after excluding the effects of the shade, and the estimated energy generated from PVs. Geographic Information Sciences (GIS) and Remote Sensing (RS) are used in solar city planning. The goal of this research is to assess available and suitable rooftops areas using …

Extended Cross-Referenced Analysis Using Data From The Landsat 8 And 9 Underfly Event, Garrison Gross

Electronic Theses and Dissertations

The Landsat 8 and 9 Underfly Event occurred in November 2021, where Landsat 9 flew beneath Landsat 8 in the final stages before settling in its final orbiting path. An analysis was performed on the images taken during this event, which resulted in a cross-referenced with uncertainties estimated to be less than 0.5%. This level of precision was due in part to the near-identical sensors aboard each instrument as well as the underfly event itself, which allowed the sensors to take nearly the exact same image at nearly the exact same time. This initial calibration was applied before the end …

Impact Of Atmospheric Correction On Classification And Quantification Of Seagrass Density From Worldview-2 Imagery, Victoria J. Hill, Richard C. Zimmerman, Paul Bissett, David Kohler, Blake Schaeffer, Megan Coffer, Jiang Li, Kazi Aminul Islam

OES Faculty Publications

Mapping the seagrass distribution and density in the underwater landscape can improve global Blue Carbon estimates. However, atmospheric absorption and scattering introduce errors in space-based sensors’ retrieval of sea surface reflectance, affecting seagrass presence, density, and above-ground carbon (AGC_seagrass) estimates. This study assessed atmospheric correction’s impact on mapping seagrass using WorldView-2 satellite imagery from Saint Joseph Bay, Saint George Sound, and Keaton Beach in Florida, USA. Coincident in situ measurements of water-leaving radiance (L_w), optical properties, and seagrass leaf area index (LAI) were collected. Seagrass classification and the retrieval of LAI were compared after empirical line …

The Development Of Dark Hyperspectral Absolute Calibration Model Using Extended Pseudo Invariant Calibration Sites At A Global Scale: Dark Epics-Global, Padam Bahadur Karki

Electronic Theses and Dissertations

This research aimed to develop a novel dark hyperspectral absolute calibration (DAHAC) model using stable dark targets of "Global Cluster - 36" (GC-36), one of the clusters from "300 Class Global Classification." The stable dark sites were identified from GC-36 called "Dark EPICS-Global" covering the surface types viz; dark rock, volcanic area, and dark sand. The Dark EPICS-Global shows a temporal variation of 0.02 unit reflectance. This work uses the Landsat-8 (L8) Operational Land Imager (OLI) , Sentinel-2A (S2A) Multispectral Instrument (MSI) , and Earth Observing One (EO-1) Hyperion data for the DAHAC model development, where well-calibrated L8 and S2A …

Method Of Validating Satellite Surface Reflectance Product Using Empirical Line Method, Meghraj Kc

Electronic Theses and Dissertations

Atmospherically corrected surface reflectance (SR) products are used for reliable monitoring of land surfaces and are the standard products of Landsat sensors. Due to increased demand for SR products, a need exists to verify that the L2C2 (Level-2 Collection-2) SR products are precise and accurate. The Level-2 Collection 2 (L2C2) SR Product is processed satellite imagery data that corrects for atmospheric effects such as absorption and scattering, providing a more accurate representation of Earth's surface. The validation of SR products using ground truth measurement is essential. This study aims to develop and evaluate a validation methodology for satellite SR products. …

Evaluation Of Low-Cost Radiometer For Surface Reflectance Re-Trieval And Orbital Sensor’S Validation, Dinithi Siriwardana Pathiranage

Electronic Theses and Dissertations

This paper evaluates the Arable Mark 2 sensor, an automated and low-cost radiometer, for its potential to retrieve surface reflectance data and validate orbital sensors such as the Landsat-8 (L8) Operational Land Imager (OLI) Level 2 product. While orbital sensors are widely used for monitoring solar radiation changes, managing natural resources, and understanding climatic trends, atmospheric effects can make it challenging to obtain accurate measurements. Equipped with multiple sensors, including long-wave and short-wave radiometers, the Arable Mark 2 sensor can measure upwelling and downwelling irradiance to calculate surface reflectance. To assess the accuracy and consistency of the Arable Mark 2 …

Precision Weed Management Based On Uas Image Streams, Machine Learning, And Pwm Sprayers, Jason Allen Davis

Graduate Theses and Dissertations

Weed populations in agricultural production fields are often scattered and unevenly distributed; however, herbicides are broadcast across fields evenly. Although effective, in the case of post-emergent herbicides, exceedingly more pesticides are used than necessary. A novel weed detection and control workflow was evaluated targeting Palmer amaranth in soybean (Glycine max) fields. High spatial resolution (0.4 cm) unmanned aircraft system (UAS) image streams were collected, annotated, and used to train 16 object detection convolutional neural networks (CNNs; RetinaNet, Faster R-CNN, Single Shot Detector, and YOLO v3) each trained on imagery with 0.4, 0.6, 0.8, and 1.2 cm spatial resolutions. Models were …

Quantifying Spatial Heterogeneity Of Wild Blueberries And Crop Water Stress Monitoring Using Remote Sensing Technologies, Kallol Barai

Electronic Theses and Dissertations

The wild blueberry is one of the major crops of Maine, with significant economic value and potential health benefits. Due to global climate change, drought impacts have been increasing significantly in recent years in the northeast region of the USA, causing significant economic losses in the agricultural sectors. It has been predicted to increase further in the future. Changing patterns of the elevated atmospheric temperatures, increased rainfall variabilities, and more frequent drought events have made the wild blueberry industry of Maine vulnerable, suggesting the adoption of novel approaches to mitigate the negative impacts of global climate changes. Also, wild blueberry …

Deep Learning Applications In Industrial And Systems Engineering, Winthrop Harvey

Graduate Theses and Dissertations

Deep learning - the use of large neural networks to perform machine learning - has transformed the world. As the capabilities of deep models continue to grow, deep learning is becoming an increasingly valuable and practical tool for industrial engineering. With its wide applicability, deep learning can be turned to many industrial engineering tasks, including optimization, heuristic search, and functional approximation. In this dissertation, the major concepts and paradigms of deep learning are reviewed, and three industrial engineering projects applying these methods are described. The first applies a deep convolutional network to the task of absolute aerial geolocalization - the …

Composite Style Pixel And Point Convolution-Based Deep Fusion Neural Network Architecture For The Semantic Segmentation Of Hyperspectral And Lidar Data, Kevin T. Decker, Brett J. Borghetti

Faculty Publications

Multimodal hyperspectral and lidar data sets provide complementary spectral and structural data. Joint processing and exploitation to produce semantically labeled pixel maps through semantic segmentation has proven useful for a variety of decision tasks. In this work, we identify two areas of improvement over previous approaches and present a proof of concept network implementing these improvements. First, rather than using a late fusion style architecture as in prior work, our approach implements a composite style fusion architecture to allow for the simultaneous generation of multimodal features and the learning of fused features during encoding. Second, our approach processes the higher …

A Comparison Of Sporadic-E Occurrence Rates Using Gps Radio Occultation And Ionosonde Measurements, Rodney Carmona, Omar A. Nava, Eugene V. Dao, Daniel J. Emmons

Faculty Publications

Sporadic-E (E_s) occurrence rates from Global Position Satellite radio occultation (GPS-RO) measurements have shown to vary by a factor of five between studies, motivating the need for a comparison with ground-based measurements. In an attempt to find accurate GPS-RO techniques for detecting E_s formation, occurrence rates derived using five previously developed GPS-RO techniques are compared to ionosonde measurements over an eight-year period from 2010–2017. GPS-RO measurements within 170 km of a ionosonde site are used to calculate E_s occurrence rates and compared to the ground-truth ionosonde measurements. The techniques are compared individually for each ionosonde site …

Machine Learning Land Cover And Land Use Classification Of 4-Band Satellite Imagery, Lorelei Turner [*], Torrey J. Wagner, Paul Auclair, Brent T. Langhals

Faculty Publications

Land-cover and land-use classification generates categories of terrestrial features, such as water or trees, which can be used to track how land is used. This work applies classical, ensemble and neural network machine learning algorithms to a multispectral remote sensing dataset containing 405,000 28x28 pixel image patches in 4 electromagnetic frequency bands. For each algorithm, model metrics and prediction execution time were evaluated, resulting in two families of models; fast and precise. The prediction time for an 81,000-patch group of predictions wasmodels, and >5s for the precise models, and there was not a significant change in prediction time when a …

Arithfusion: An Arithmetic Deep Model For Temporal Remote Sensing Image Fusion, Md Reshad Ul Hoque, Jian Wu, Chiman Kwan, Krzysztof Koperski, Jiang Li

Electrical & Computer Engineering Faculty Publications

Different satellite images may consist of variable numbers of channels which have different resolutions, and each satellite has a unique revisit period. For example, the Landsat-8 satellite images have 30 m resolution in their multispectral channels, the Sentinel-2 satellite images have 10 m resolution in the pan-sharp channel, and the National Agriculture Imagery Program (NAIP) aerial images have 1 m resolution. In this study, we propose a simple yet effective arithmetic deep model for multimodal temporal remote sensing image fusion. The proposed model takes both low- and high-resolution remote sensing images at t₁ together with low-resolution images at a …

Industry 4.0 Remanufacturing: A Novel Approach Towards Smart Remanufacturing, Prashansa Ragampeta

Masters Theses

“Smart remanufacturing has become more popular in recent years as a result of its multiple benefits and the growing need for society to encourage a circular economy that leads to sustainability. One of the most common end-of-life (EoL) choices that can lead to a circular economy is remanufacturing. As a result, at the end-of-life stage of a product, it is critical to prioritize this choice over other accessible options because it is the only recovery option that retains the same quality as a new product. This work focuses on the numerous technologies that can aid in the improvement of smart …

Relative Radiometric Correction Of Pushbroom Satellites Using The Yaw Maneuver, Christopher Begeman

Electronic Theses and Dissertations

Earth imaging satellites commonly acquire multispectral imagery using linear array detectors formatted as a pushbroom scanner. Landsat 8, a well-known example, uses pushbroom scanning and thus has 73,000 individual detectors. These 73,000 detectors are split among 14 different focal plane modules (FPM), and each detector and FPM exhibit unique behavior when monitoring a uniform radiance value. To correct for each detectors differences in sensor measurement a novel technique of relative gain estimation that employs an optimized modified Signal-to-Noise Ratio through a 90˚ yaw maneuver, also known as side slither, is presented that allows for both FPM and detector level relative …

Assessing The Vertical Displacement Of The Grand Ethiopian Renaissance Dam During Its Filling Using Dinsar Technology And Its Potential Acute Consequences On The Downstream Countries, Hesham El-Askary, Amr Fawzy, Rejoice Thomas, Wenzhao Li, Nicholas Lahaye, Erik Linstead, Thomas Piechota, Daniele Struppa, Mohamed Abdelaty Sayed

Mathematics, Physics, and Computer Science Faculty Articles and Research

The Grand Ethiopian Renaissance Dam (GERD), formerly known as the Millennium Dam, is currently under construction and has been filling at a fast rate without sufficient known analysis on possible impacts on the body of the structure. The filling of GERD not only has an impact on the Blue Nile Basin hydrology, water storage and flow but also poses massive risks in case of collapse. Rosaries Dam located in Sudan at only 116 km downstream of GERD, along with the 20 million Sudanese benefiting from that dam, would be seriously threatened in case of the collapse of GERD. In this …

Unmanned Aircraft Systems For Archaeology Using Photogrammetry And Lidar In Southwestern United States, Imai Bates-Domingo, Alexandra Gates, Patrick Hunter, Blake Neal, Kyle Snowden, Destin Webster

Study America

Researchers can use small unmanned aircraft systems (sUAS), also known as drones, to make observations of historical sites, help interpret locations, and make new discoveries that may not be visible to the naked eye. A student team from Embry-Riddle Aeronautical University gathered data for historical site documentation in New Mexico using the DJI Phantom 4 Pro V2, DJI Mavic Pro 2, DJI M210 and DJI M600, and senseFly eBee. Utilizing these drones, student analysts were able to take the data gathered and create georectified orthomosaic images and 3D virtual objects. At Tularosa Canyon, at a site known as the Creekside …

Downscaling Of Goes-16'S Land Surface Temperature Product Using Epitomes, Roberto Garcia

Open Access Theses & Dissertations

Land surface temperature (LST) is an environmental variable derived from thermal infrared (TIR) imagery. Satellite platforms are a good source of TIR imagery because of their ability to provide widespread and frequent coverage of the Earthâ??s surface. It is common that a single satellite remote sensing platform is able to provide images with good spatial resolution or temporal resolution but not both. LST is an important parameter for studies on the urban heat island (UHI) effect. These studies are limited by the spatial or temporal resolutions of available LST products. This Thesis presents an algorithm to estimate land surface temperature …

Creating A Field-Wide Forage Canopy Model Using Uavs And Photogrammetry Processing, Cameron Minch, Joseph S. Dvorak, Joshua J. Jackson, Stuart Tucker Sheffield

Biosystems and Agricultural Engineering Faculty Publications

Alfalfa canopy structure reveals useful information for managing this forage crop, but manual measurements are impractical at field-scale. Photogrammetry processing with images from Unmanned Aerial Vehicles (UAVs) can create a field-wide three-dimensional model of the crop canopy. The goal of this study was to determine the appropriate flight parameters for the UAV that would enable reliable generation of canopy models at all stages of alfalfa growth. Flights were conducted over two separate fields on four different dates using three different flight parameters. This provided a total of 24 flights. The flight parameters considered were the following: 30 m altitude with …

A Quantitative Validation Of Multi-Modal Image Fusion And Segmentation For Object Detection And Tracking, Nicholas Lahaye, Michael J. Garay, Brian D. Bue, Hesham El-Askary, Erik Linstead

Mathematics, Physics, and Computer Science Faculty Articles and Research

In previous works, we have shown the efficacy of using Deep Belief Networks, paired with clustering, to identify distinct classes of objects within remotely sensed data via cluster analysis and qualitative analysis of the output data in comparison with reference data. In this paper, we quantitatively validate the methodology against datasets currently being generated and used within the remote sensing community, as well as show the capabilities and benefits of the data fusion methodologies used. The experiments run take the output of our unsupervised fusion and segmentation methodology and map them to various labeled datasets at different levels of global …

Detecting Recent Crop Phenology Dynamics In Corn And Soybean Cropping Systems Of Kentucky, Yanjun Yang, Bo Tao, Liang Liang, Yawen Huang, Christopher J. Matocha, Chad D. Lee, Michael Sama, Bassil El Masri, Wei Ren

Geography Faculty Publications

Accurate phenological information is essential for monitoring crop development, predicting crop yield, and enhancing resilience to cope with climate change. This study employed a curve-change-based dynamic threshold approach on NDVI (Normalized Differential Vegetation Index) time series to detect the planting and harvesting dates for corn and soybean in Kentucky, a typical climatic transition zone, from 2000 to 2018. We compared satellite-based estimates with ground observations and performed trend analyses of crop phenological stages over the study period to analyze their relationships with climate change and crop yields. Our results showed that corn and soybean planting dates were delayed by 0.01 …

Statistical Analysis And Comparison Of Optical Classification Of Atmospheric Aerosol Lidar Data, Mohammed Alqawba, Norou Diawara, Kwasi G. Afrifa, Mohamed I. Elbakary, Mecit Cetin, Khan Iftekharuddin

Mathematics & Statistics Faculty Publications

In this article, we present a new study for the analysis and classification of atmospheric aerosols in remote sensing LIDAR data. Information on particle size and associated properties are extracted from these remote sensing atmospheric data which are collected by a ground-based LIDAR system. This study first considers optical LIDAR parameter-based classification methods for clustering and classification of different types of harmful aerosol particles in the atmosphere. Since accurate methods for aerosol prediction behaviors are based upon observed data, computational approaches must overcome design limitations, and consider appropriate calibration and estimation accuracy. Consequently, two statistical methods based on generalized linear …

Viability And Application Of Mounting Personal Pid Voc Sensors To Small Unmanned Aircraft Systems, Cheryl Lynn Marcham, Scott Burgess, Joseph Cerreta, Patti J. Clark, James P. Solti, Brandon Breault, Joshua G. Marcham

Publications

Using a UAS-mounted sensor to allow for a rapid response to areas that may be difficult to reach or potentially dangerous to human health can increase the situational awareness of first responders of an aircraft crash site through the remote detection, identification, and quantification of airborne hazardous materials. The primary purpose of this research was to evaluate the remote sensing viability and application of integrating existing commercial-off-the-shelf (COTS) sensors with small unmanned aircraft system (UAS) technology to detect potentially hazardous airborne contaminants in emergency leak or spill response situations. By mounting the personal photoionization detector (PID) with volatile organic compound …