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MACHINE LEARNING APPROACH FOR VEGETATION CLASSIFICATION USING UAS MULTISPECTRAL IMAGERY
Title
MACHINE LEARNING APPROACH FOR VEGETATION CLASSIFICATION USING UAS MULTISPECTRAL IMAGERY.
Creator
Kesavan, Pandiyan, Sudhagar Nagarajan, Florida Atlantic University, Department of Civil, Environmental and Geomatics Engineering, College of Engineering and Computer Science
Abstract/Description
Vegetation monitoring plays a significant role in improving the quality of life above the earth's surface. However, vegetation resources management is challenging due to climate change, global warming, and urban development. The research aims to identify and extract vegetation communities for Jupiter Inlet Lighthouse Outstanding Natural Area (JILONA) using developed Unmanned Aerial Systems (UAS) deployed with five bands of RedEdge Micasence Multispectral Sensor. UAS has a lot of potential for...
Show moreVegetation monitoring plays a significant role in improving the quality of life above the earth's surface. However, vegetation resources management is challenging due to climate change, global warming, and urban development. The research aims to identify and extract vegetation communities for Jupiter Inlet Lighthouse Outstanding Natural Area (JILONA) using developed Unmanned Aerial Systems (UAS) deployed with five bands of RedEdge Micasence Multispectral Sensor. UAS has a lot of potential for various applications as it provides high-resolution imagery at lower altitudes. In this study, spectral reflectance values for each vegetation species were collected using a spectroradiometer instrument. Those values were correlated with five band UAS Image values to understand the sensor's performance, also added with reflectance’s similarities and divergence for vegetation species. Pixel and Object-based classification methods were performed using 0.15 ft Multispectral Imagery to identify the vegetation classes. Supervised Machine Learning Support Vector Machine (SVM) and Random Forest (RF) algorithms with topographical information were used to produce thematic vegetation maps. The Pixel-based procedure using the SVM algorithm generated an overall accuracy and kappa coefficient of above 90 percent. Both classification approaches have provided aesthetic vegetation thematic maps. According to statistical cross-validation findings and visual interpretation of vegetation communities, the pixel classification method outperformed object-based classification.
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Date Issued
2021
PURL
http://purl.flvc.org/fau/fd/FA00013768
Subject Headings
Vegetation classification, Machine learning, Multispectral imaging, Unmanned aerial vehicles
Format
Document (PDF)
SPACE-TIME GRAPH PATH PLANNING FOR UAS TRAFFIC MANAGEMENT SYSTEMS
Title
SPACE-TIME GRAPH PATH PLANNING FOR UAS TRAFFIC MANAGEMENT SYSTEMS.
Creator
Papa, Rafael, Cardei, Mihaela, Cardei, Ionut, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description
The unmanned aerial vehicle (UAV) technology has evolved considerably in recent years and the global demand for package delivery is expected to grow even more during COVID-19 and the social distance era. The low cost of acquisition, payload capacity, maneuverability, and the ability to y at low-altitude with a very low cost of operation, make UAVs a perfect fit to revolutionize the payload transportation of small items. The large-scale adoption of drone package delivery in high-density urban...
Show moreThe unmanned aerial vehicle (UAV) technology has evolved considerably in recent years and the global demand for package delivery is expected to grow even more during COVID-19 and the social distance era. The low cost of acquisition, payload capacity, maneuverability, and the ability to y at low-altitude with a very low cost of operation, make UAVs a perfect fit to revolutionize the payload transportation of small items. The large-scale adoption of drone package delivery in high-density urban areas can be challenging and the Unmanned Aircraft Systems (UAS) operators must ensure safety, security, efficiency and equity of the airspace system. In order to address some of these challenges, FAA and NASA have developed a new architecture that will support a set of services to enable cooperative management of low-altitude operations between UAS operators. The architecture is still in its conceptual stage and designing a mechanism that ensures the fair distribution of the available airspace to commercial applications has become increasingly important. Considering that, the path planning is one of the most important problems to be explored. The objective is not only to find an optimal and shortest path but also to provide a collision-free environment to the UAVs. Taking into consideration all these important aspects and others such as serving on-demand requests, flight duration limitation due to energy constraints, maintaining the safety distance to avoid collisions, and using warehouses as starting and ending points in parcel delivery, this dissertation proposes: (i) an energy-constrained scheduling mechanism using a multi-source A* algorithm variant, and (ii) a generalized path planning mechanism using a space-time graph with multi-source multi-destination BFS generalization to ensure pre-flight UAV collision-free trajectories. This dissertation also uses the generalized path planning mechanism to solve the energy-constrained drone delivery problem. The experimental results show that the proposed algorithms are computationally efficient and scalable with the number of requests and graph size.
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Date Issued
2021
PURL
http://purl.flvc.org/fau/fd/FA00013861
Subject Headings
Unmanned aerial vehicles, Drone aircraft, Space and time
Format
Document (PDF)
Real Time Traffic Monitoring System from a UAV Platform
Title
Real Time Traffic Monitoring System from a UAV Platform.
Creator
Biswas, Debojit, Su, Hongbo, Florida Atlantic University, College of Engineering and Computer Science, Department of Civil, Environmental and Geomatics Engineering
Abstract/Description
Today transportation systems are facing big transitions all over the world. We created fly overs, roads under the ground, bridges over the river and ocean to get efficient access and to increase the road connectivity. Our transportation system is more intelligent than ever. Our traffic signaling system became adaptive. Our vehicles equipped with new gadgets and we developed new tools for more efficient analysis of traffic. Our research relies on existing traffic infrastructure to generate...
Show moreToday transportation systems are facing big transitions all over the world. We created fly overs, roads under the ground, bridges over the river and ocean to get efficient access and to increase the road connectivity. Our transportation system is more intelligent than ever. Our traffic signaling system became adaptive. Our vehicles equipped with new gadgets and we developed new tools for more efficient analysis of traffic. Our research relies on existing traffic infrastructure to generate better understanding of traffic. More specifically, this research focused on traffic and UAV cameras to extract information about the traffic. Our first goal was to create an automatic system to count the cars using traffic cameras. To achieve this goal, we implemented Background Subtraction Method (BSM) and OverFeat Framework. BSM compares consecutive frames to detect the moving objects. Because BSM only works for ideal lab conditions, therefor we implemented a Convolutional Neural Network (CNN) based classification algorithm called OverFeat Framework. We created different segments on the road in various lanes to tabulate the number of passing cars. We achieved 96.55% accuracy for car counting irrespective of different visibility conditions of the day and night. Our second goal was to find out traffic density. We implemented two CNN based algorithms: Single Shot Detection (SSD) and MobileNet-SSD for vehicle detection. These algorithms are object detection algorithms. We used traffic cameras to detect vehicles on the roads. We utilized road markers and light pole distances to determine distances on the road. Using the distance and count information we calculated density. SSD is a more resource intense algorithm and it achieved 92.97% accuracy. MobileNet-SSD is a lighter algorithm and it achieved 79.30% accuracy. Finally, from a moving platform we estimated the velocity of multiple vehicles. There are a lot of roads where traffic cameras are not available, also traffic monitoring is necessary for special events. We implemented Faster R-CNN as a detection algorithm and Discriminative Correlation Filter (with Channel and Spatial Reliability Tracking) for tracking. We calculated the speed information from the tracking information in our study. Our framework achieved 96.80% speed accuracy compared to manual observation of speeds.
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Date Issued
2019
PURL
http://purl.flvc.org/fau/fd/FA00013188
Subject Headings
Traffic monitoring, Intelligent transportation systems, Neural networks (Computer science), Vehicle detectors, Unmanned aerial vehicles
Format
Document (PDF)
OBJECT-BASED LAND COVER CLASSIFICATION OF UAV TRUE COLOR IMAGERY
Title
OBJECT-BASED LAND COVER CLASSIFICATION OF UAV TRUE COLOR IMAGERY.
Creator
Castillo, Stephen M., Nagarajan, Sudhagar, Florida Atlantic University, Department of Civil, Environmental and Geomatics Engineering, College of Engineering and Computer Science
Abstract/Description
Land cover classification is necessary for understanding the state of the surface of the Earth at varying regions of interest. Knowledge of the Earth’s surface is critical in land-use planning, especially for the project study area Jupiter Inlet Lighthouse Outstanding Natural Area, where various vegetation, wild-life, and cultural components rely on adequate land-cover knowledge. The purpose of this research is to demonstrate the capability of UAV true color imagery for land cover...
Show moreLand cover classification is necessary for understanding the state of the surface of the Earth at varying regions of interest. Knowledge of the Earth’s surface is critical in land-use planning, especially for the project study area Jupiter Inlet Lighthouse Outstanding Natural Area, where various vegetation, wild-life, and cultural components rely on adequate land-cover knowledge. The purpose of this research is to demonstrate the capability of UAV true color imagery for land cover classification. In addition to the objective of land cover classification, comparison of varying spatial resolutions of the imagery will be analyzed in the accuracy assessment of the output thematic maps. These resolutions will also be compared at varying training sample sizes to see which configuration performed best.
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Date Issued
2020
PURL
http://purl.flvc.org/fau/fd/FA00013454
Subject Headings
Land cover, Unmanned aerial vehicles, Drone aircraft in remote sensing, Images, Classification
Format
Document (PDF)
PATH PLANNING ALGORITHMS FOR UNMANNED AIRCRAFT SYSTEMS WITH A SPACE-TIME GRAPH
Title
PATH PLANNING ALGORITHMS FOR UNMANNED AIRCRAFT SYSTEMS WITH A SPACE-TIME GRAPH.
Creator
Steinberg, Andrew, Cardei, Mihaela, Cardei, Ionut, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description
Unmanned Aircraft Systems (UAS) have grown in popularity due to their widespread potential applications, including efficient package delivery, monitoring, surveillance, search and rescue operations, agricultural uses, along with many others. As UAS become more integrated into our society and airspace, it is anticipated that the development and maintenance of a path planning collision-free system will become imperative, as the safety and efficiency of the airspace represents a priority. The...
Show moreUnmanned Aircraft Systems (UAS) have grown in popularity due to their widespread potential applications, including efficient package delivery, monitoring, surveillance, search and rescue operations, agricultural uses, along with many others. As UAS become more integrated into our society and airspace, it is anticipated that the development and maintenance of a path planning collision-free system will become imperative, as the safety and efficiency of the airspace represents a priority. The dissertation defines this problem as the UAS Collision-free Path Planning Problem. The overall objective of the dissertation is to design an on-demand, efficient and scalable aerial highway path planning system for UAS. The dissertation explores two solutions to this problem. The first solution proposes a space-time algorithm that searches for shortest paths in a space-time graph. The solution maps the aerial traffic map to a space-time graph that is discretized on the inter-vehicle safety distance. This helps compute safe trajectories by design. The mechanism uses space-time edge pruning to maintain the dynamic availability of edges as vehicles move on a trajectory. Pruning edges is critical to protect active UAS from collisions and safety hazards. The dissertation compares the solution with another related work to evaluate improvements in delay, run time scalability, and admission success while observing up to 9000 flight requests in the network. The second solution to the path planning problem uses a batch planning algorithm. This is a new mechanism that processes a batch of flight requests with prioritization on the current slack time. This approach aims to improve the planning success ratio. The batch planning algorithm is compared with the space-time algorithm to ascertain improvements in admission ratio, delay ratio, and running time, in scenarios with up to 10000 flight requests.
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Date Issued
2021
PURL
http://purl.flvc.org/fau/fd/FA00013696
Subject Headings
Unmanned aerial vehicles, Drone aircraft, Drone aircraft--Automatic control, Space and time, Algorithms
Format
Document (PDF)