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Title: Image classification and image resolution issues for DOQQ analysis.
Creator: Boruff, Bryan Jeffery., Florida Atlantic University, Roberts, Charles
Abstract/Description: High-resolution imagery is becoming readily available to the public. Private firms and government organizations are using high-resolution images but are running into problems with storage space and processing time. High-resolution images are extremely large files, and have proven cumbersome to work with and control. By resampling fine resolution imagery to a lower resolution, storage and processing space can be dramatically reduced. Fine-resolution imagery is not needed to map most features...
Show moreHigh-resolution imagery is becoming readily available to the public. Private firms and government organizations are using high-resolution images but are running into problems with storage space and processing time. High-resolution images are extremely large files, and have proven cumbersome to work with and control. By resampling fine resolution imagery to a lower resolution, storage and processing space can be dramatically reduced. Fine-resolution imagery is not needed to map most features and resampled high-resolution imagery can be used as a replacement for low-resolution satellite imagery in some cases. The effects of resampling on the spectral quality of a high-resolution image can be demonstrated by answering the following questions: (1) Is the quality of spectral information on a color infrared DOQQ comparable to SPOT and TM Landsat satellite imagery for the purpose of digital image classification? (2) What is the appropriate resolution for mapping surface features using high-resolution imagery for spectral categories of information? (3) What is the appropriate resolution for mapping surface features using high-resolution imagery for land-use land-cover information?
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Date Issued: 2000
PURL: http://purl.flvc.org/fcla/dt/15787
Subject Headings: Remote sensing, Image processing, Image analysis
Format: Document (PDF)

Title: Fourier telescopy test system.
Creator: Randunu-Pathirannehelage, Nishantha, Torres Moreno, Yezid, Rhodes, William T., Graduate College
Date Issued: 2013-04-12
PURL: http://purl.flvc.org/fcla/dt/3361948
Subject Headings: Fourier transform optics, Imaging systems, Remote sensing
Format: Document (PDF)

Title: Supervised classification of a digital orthophoto quadrangle quarter.
Creator: Vacchiano, James., Florida Atlantic University, Roberts, Charles
Abstract/Description: Digital orthophoto quadrangle quarter (DOQQ) is a new type of imagery. The DOQQ is a product of the United States Geological Survey, which has many uses. The spectral and spatial qualities of a DOQQ are equal to and surpass the qualities of traditional satellite imagery. These qualities can be demonstrated by answering the following questions: (1) Is the quality of the spectral information on a color infrared DOQQ comparable to a SPOT and TM Landsat satellite imagery for the purpose of...
Show moreDigital orthophoto quadrangle quarter (DOQQ) is a new type of imagery. The DOQQ is a product of the United States Geological Survey, which has many uses. The spectral and spatial qualities of a DOQQ are equal to and surpass the qualities of traditional satellite imagery. These qualities can be demonstrated by answering the following questions: (1) Is the quality of the spectral information on a color infrared DOQQ comparable to a SPOT and TM Landsat satellite imagery for the purpose of digital image classification? (2) What are the optimal number of classes for achieving an accuracy supervised classification of a one-meter resolution DOQQ? (3) What is the effect of changing the spatial resolution on image classification of a DOQQ?
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Date Issued: 1998
PURL: http://purl.flvc.org/fcla/dt/15584
Subject Headings: Digital mapping, Remote-sensing images
Format: Document (PDF)

Title: MACHINE LEARNING METHODS FOR IMAGE ENHANCEMENT IN DEGRADED VISUAL ENVIRONMENTS.
Creator: Estrada, Dennis, Tang, Yufei, Ouyang, Bing, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Significant reduction in space, weight, power, and cost (SWAP-C) of imaging hardware has induced a paradigm shift in remote sensing where unmanned platforms have become the mainstay. However, mitigating the degraded visual environment (DVE) remains an issue. DVEs can cause a loss of contrast and image detail due to particle scattering and distortion due to turbulence-induced effects. The problem is especially challenging when imaging from unmanned platforms such as autonomous underwater...
Show moreSignificant reduction in space, weight, power, and cost (SWAP-C) of imaging hardware has induced a paradigm shift in remote sensing where unmanned platforms have become the mainstay. However, mitigating the degraded visual environment (DVE) remains an issue. DVEs can cause a loss of contrast and image detail due to particle scattering and distortion due to turbulence-induced effects. The problem is especially challenging when imaging from unmanned platforms such as autonomous underwater vehicles (AUV) and unmanned ariel vehicles (UAV). While single-frame image restoration techniques have been studied extensively in recent years, single image capture is not adequate to address the effects of DVEs due to under-sampling, low dynamic range, and chromatic aberration. Significant development has been made to employ multi-frame image fusion techniques to take advantage of spatial and temporal information to aid in the recovery of corrupted image detail and high-frequency content and increasing dynamic range.
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Date Issued: 2022
PURL: http://purl.flvc.org/fau/fd/FA00013987
Subject Headings: Image Enhancement, Machine learning, Remote sensing
Format: Document (PDF)

Title: Mapping urban land cover using multi-scale and spatial autocorrelation information in high resolution imagery.
Creator: Johnson, Brian A., Charles E. Schmidt College of Science, Department of Geosciences
Abstract/Description: Fine-scale urban land cover information is important for a number of applications, including urban tree canopy mapping, green space analysis, and urban hydrologic modeling. Land cover information has traditionally been extracted from satellite or aerial images using automated image classification techniques, which classify pixels into different categories of land cover based on their spectral characteristics. However, in fine spatial resolution images (4 meters or better), the high degree of...
Show moreFine-scale urban land cover information is important for a number of applications, including urban tree canopy mapping, green space analysis, and urban hydrologic modeling. Land cover information has traditionally been extracted from satellite or aerial images using automated image classification techniques, which classify pixels into different categories of land cover based on their spectral characteristics. However, in fine spatial resolution images (4 meters or better), the high degree of within-class spectral variability and between-class spectral similarity of many types of land cover leads to low classification accuracy when pixel-based, purely spectral classification techniques are used. Object-based classification methods, which involve segmenting an image into relatively homogeneous regions (i.e. image segments) prior to classification, have been shown to increase classification accuracy by incorporating the spectral (e.g. mean, standard deviation) and non-spectral (e.g. te xture, size, shape) information of image segments for classification. One difficulty with the object-based method, however, is that a segmentation parameter (or set of parameters), which determines the average size of segments (i.e. the segmentation scale), is difficult to choose. Some studies use one segmentation scale to segment and classify all types of land cover, while others use multiple scales due to the fact that different types of land cover typically vary in size. In this dissertation, two multi-scale object-based classification methods were developed and tested for classifying high resolution images of Deerfield Beach, FL and Houston, TX. These multi-scale methods achieved higher overall classification accuracies and Kappa coefficients than single-scale object-based classification methods., Since the two dissertation methods used an automated algorithm (Random Forest) for image classification, they are also less subjective and easier to apply to other study areas than most existing multi-scale object-based methods that rely on expert knowledge (i.e. decision rules developed based on detailed visual inspection of image segments) for classifying each type of land cover.
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Date Issued: 2012
PURL: http://purl.flvc.org/FAU/3342110
Subject Headings: Image processing, Digital techniques, Remote sensing, Mathematics, Remote-sensing images, Computational intelligence, Cities and towns, Remote sensing, Environmental sciences, Remote sensing, Spatial analysis (Statistics)
Format: Document (PDF)

Title: Commercialization of high-resolution earth observation satellite remote sensing.
Creator: Jarica, Cornelia Christa, Florida Atlantic University, Tata, Robert J., Charles E. Schmidt College of Science, Department of Geosciences
Abstract/Description: The imminent availability of high resolution satellite imagery is causing a paradigm shift in remote sensing. Detente brought about new policy directives in the U.S. and abroad, which opened up for civilian use former Earth observation spy technology down to one meter resolution, previously considered classified and strictly used by the intelligence communities for national security. This study describes a number of new ventures in the private sector which have been formed to launch...
Show moreThe imminent availability of high resolution satellite imagery is causing a paradigm shift in remote sensing. Detente brought about new policy directives in the U.S. and abroad, which opened up for civilian use former Earth observation spy technology down to one meter resolution, previously considered classified and strictly used by the intelligence communities for national security. This study describes a number of new ventures in the private sector which have been formed to launch commercial high resolution systems. The satellites' technical capabilities are analyzed, and application development options for the new imagery are discussed in detail. This new remote sensing data source is also seen within the framework of the larger GeoTechnology Industry to which it belongs, and the author proposes appropriate business strategies for successful commercialization.
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Date Issued: 1996
PURL: http://purl.flvc.org/fcla/dt/15321
Subject Headings: Earth resources technology satellites, Remote sensing, Remote-sensing images, Geographic information systems
Format: Document (PDF)

Title: Object detection in low resolution video sequences.
Creator: Pava, Diego F., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: With augmenting security concerns and decreasing costs of surveillance and computing equipment, research on automated systems for object detection has been increasing, but the majority of the studies focus their attention on sequences where high resolution objects are present. The main objective of this work is the detection and extraction of information of low resolution objects (e.g. objects that are so far away from the camera that they occupy only tens of pixels) in order to provide a...
Show moreWith augmenting security concerns and decreasing costs of surveillance and computing equipment, research on automated systems for object detection has been increasing, but the majority of the studies focus their attention on sequences where high resolution objects are present. The main objective of this work is the detection and extraction of information of low resolution objects (e.g. objects that are so far away from the camera that they occupy only tens of pixels) in order to provide a base for higher level information operations such as classification and behavioral analysis. The system proposed is composed of four stages (preprocessing, background modeling, information extraction, and post processing) and uses context based region of importance selection, histogram equalization, background subtraction and morphological filtering techniques. The result is a system capable of detecting and tracking low resolution objects in a controlled background scene which can be a base for systems with higher complexity.
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Date Issued: 2009
PURL: http://purl.flvc.org/FAU/186685
Subject Headings: Computer systems, Security measures, Remote sensing, Image processing, Digital techniques, Imaging systems, Mathematical models
Format: Document (PDF)

Title: Water and Soil Salinity Mapping for Southern Everglades using Remote Sensing Techniques and In Situ Observations.
Creator: Khadim, Fahad Khan, Su, Hongbo, Florida Atlantic University, College of Engineering and Computer Science, Department of Civil, Environmental and Geomatics Engineering
Abstract/Description: Everglades National Park is a hydro-ecologically significant wetland experiencing salinity ingress over the years. This motivated our study to map water salinity using a spatially weighted optimization model (SWOM); and soil salinity using land cover classes and EC thresholds. SWOM was calibrated and validated at 3-km grids with actual salinity for 1998–2001, and yielded acceptable R2 (0.89-0.92) and RMSE (1.73-1.92 ppt). Afterwards, seasonal water salinity mapping for 1996–97, 2004–05, and...
Show moreEverglades National Park is a hydro-ecologically significant wetland experiencing salinity ingress over the years. This motivated our study to map water salinity using a spatially weighted optimization model (SWOM); and soil salinity using land cover classes and EC thresholds. SWOM was calibrated and validated at 3-km grids with actual salinity for 1998–2001, and yielded acceptable R2 (0.89-0.92) and RMSE (1.73-1.92 ppt). Afterwards, seasonal water salinity mapping for 1996–97, 2004–05, and 2016 was carried out. For soil salinity mapping, supervised land cover classification was firstly carried out for 1996, 2000, 2006, 2010 and 2015; with the first four providing average accuracies of 82%-94% against existing NLCD classifications. The land cover classes and EC thresholds helped mapping four soil salinity classes namely, the non saline (EC = 0~2 dS/m), low saline (EC = 2~4 dS/m), moderate saline (EC = 4~8 dS/m) and high saline (EC >8 dS/m) areas.
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Date Issued: 2017
PURL: http://purl.flvc.org/fau/fd/FA00004860, http://purl.flvc.org/fau/fd/FA00004860
Subject Headings: Everglades National Park (Fla.)--Environmental conditions., Florida Bay (Fla.)--Environmental conditions., Remote sensing., Multispectral imaging., Environmental monitoring--Remote sensing., Geographic information systems., Soils--Remote sensing., Soil moisture--Measurement., Soil mapping.
Format: Document (PDF)

Title: COMBINING TRADITIONAL AND IMAGE ANALYSIS TECHNIQUES FOR UNCONSOLIDATED EXPOSED TERRIGENOUS BEACH SAND CHARACTERIZATION.
Creator: Smith, Molly Elizabeth, Zhang, Caiyun, Oleinik, Anton, Florida Atlantic University, Department of Geosciences, Charles E. Schmidt College of Science
Abstract/Description: Traditional sand analysis is labor and cost-intensive, entailing specialized equipment and operators trained in geological analysis. Even a small step to automate part of the traditional geological methods could substantially improve the speed of such research while removing chances of human error. Digital image analysis techniques and computer vision have been well developed and applied in various fields but rarely explored for sand analysis. This research explores capabilities of remote...
Show moreTraditional sand analysis is labor and cost-intensive, entailing specialized equipment and operators trained in geological analysis. Even a small step to automate part of the traditional geological methods could substantially improve the speed of such research while removing chances of human error. Digital image analysis techniques and computer vision have been well developed and applied in various fields but rarely explored for sand analysis. This research explores capabilities of remote sensing digital image analysis techniques, such as object-based image analysis (OBIA), machine learning, digital image analysis, and photogrammetry to automate or semi-automate the traditional sand analysis procedure. Here presented is a framework combining OBIA and machine learning classification of microscope imagery for use with unconsolidated terrigenous beach sand samples. Five machine learning classifiers (RF, DT, SVM, k-NN, and ANN) are used to model mineral composition from images of ten terrigenous beach sand samples. Digital image analysis and photogrammetric techniques are applied and evaluated for use to characterize sand grain size and grain circularity (given as a digital proxy for traditional grain sphericity). A new segmentation process is also introduced, where pixel-level SLICO superpixel segmentation is followed by spectral difference segmentation and further levels of superpixel segmentation at the object-level. Previous methods of multi-resolution and superpixel segmentation at the object level do not provide the level of detail necessary to yield optimal sand grain-sized segments. In this proposed framework, the DT and RF classifiers provide the best estimations of mineral content of all classifiers tested compared to traditional compositional analysis. Average grain size approximated from photogrammetric procedures is comparable to traditional sieving methods, having an RMSE below 0.05%. The framework proposed here reduces the number of trained personnel needed to perform sand-related research. It requires minimal sand sample preparation and minimizes user-error that is typically introduced during traditional sand analysis.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013517
Subject Headings: Sand, Image analysis, Remote sensing, Photogrammetry--Digital techniques, Machine learning
Format: Document (PDF)

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)

Title: Statistical correlation between economic activity and DMSP-OLS night light images in Florida.
Creator: Forbes, Dolores J., Charles E. Schmidt College of Science, Department of Geosciences
Abstract/Description: The Defense Meteorological Satellite Program (DMSP) Optical Line Scan (OLS) instruments collect data from an altitude of approximately 830km above the surface of the Earth. The night light data from these instruments has been shown to correlate by lit area with national level Gross Domestic Product (GDP) and to correlate with GDP at the State level by total radiance value. Very strong correlation is found between the night light data at a new, larger scale, the Metropolitan Statistical Area ...
Show moreThe Defense Meteorological Satellite Program (DMSP) Optical Line Scan (OLS) instruments collect data from an altitude of approximately 830km above the surface of the Earth. The night light data from these instruments has been shown to correlate by lit area with national level Gross Domestic Product (GDP) and to correlate with GDP at the State level by total radiance value. Very strong correlation is found between the night light data at a new, larger scale, the Metropolitan Statistical Area (MSA) within the state of Florida. Additional statistical analysis was performed to determine which industries within each MSA explain the greatest amount of variance in the night light data. Industrial variables exhibited strong multi-collinearity. It is therefore impossible to determine which industries explain the greatest variance in the night light image data.
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Date Issued: 2011
PURL: http://purl.flvc.org/FAU/3175019
Subject Headings: Earth, Rendering (Computer graphics), Urban ecology (Sociology), Sustainable development
Format: Document (PDF)

Title: Image rectification/registration from a project management perspective: A review of various software.
Creator: Gammack-Clark, James Peter, Florida Atlantic University, Roberts, Charles
Abstract/Description: The project manager has much to deliberate when choosing a software package for image rectification/registration. He/she must be able to perform a cost analysis evaluation of the packages in question, and determine which package will provide the highest level of positional accuracy. Objective and subjective analysis of six software packages, ArcView Image Analysis, GeoMedia Pro, Arc/Info 8.1, ERMAPPER, ENVI and Idrisi 3.2, and their multiple products (polynomials and triangulations) provide...
Show moreThe project manager has much to deliberate when choosing a software package for image rectification/registration. He/she must be able to perform a cost analysis evaluation of the packages in question, and determine which package will provide the highest level of positional accuracy. Objective and subjective analysis of six software packages, ArcView Image Analysis, GeoMedia Pro, Arc/Info 8.1, ERMAPPER, ENVI and Idrisi 3.2, and their multiple products (polynomials and triangulations) provide the basis with which the project manager may attain this goal. He/she is familiarized with the user interface of each package, through detailed step-by-step methodology. Positional accuracy of each product is compared to Ground Control Points (GCPs) derived from a Differential Global Positioning System (DGPS). The accuracy of each product is also compared to the industry standard USGS DOQQ, and it is discovered that while simple rectification procedures may produce mean errors acceptable to the specifications of NMAS, the strictest application of these standards reveal that these products are not accurate enough to satisfy the USGS standards.
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Date Issued: 2001
PURL: http://purl.flvc.org/fcla/dt/12829
Subject Headings: Computer software--Evaluation, Image processing--Digital techniques, Remote sensing
Format: Document (PDF)

Title: From photo interpretation to GIS: Data quality assessments.
Creator: Conaway, Michael D., Florida Atlantic University, Roberts, Charles, Shaw, Shih-Lung, Charles E. Schmidt College of Science, Department of Geosciences
Abstract/Description: Much of the recent research concerning the use of GIS has revolved around data quality. Types of errors inherent in GIS data layers, and also errors that may be produced through the creation and manipulation of data layers have been identified. Definitions of these errors, and observations of how these errors occur have been offered. However, the majority of the research is qualitative. It is known that positional variation is produced through differing interpretations and generalization of...
Show moreMuch of the recent research concerning the use of GIS has revolved around data quality. Types of errors inherent in GIS data layers, and also errors that may be produced through the creation and manipulation of data layers have been identified. Definitions of these errors, and observations of how these errors occur have been offered. However, the majority of the research is qualitative. It is known that positional variation is produced through differing interpretations and generalization of points, lines, and polygons, but it is not known to what extent. This information would be extremely helpful in allowing the user of the information to fine tune the application, based on the accuracy of the data. Providing this type of information is the goal of this research. Quantitative analysis of the results of a series of experiments will give a numerical range of possible positional errors produced through database creation via aerial photo interpretation.
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Date Issued: 1996
PURL: http://purl.flvc.org/fcla/dt/15280
Subject Headings: Geographic information systems, Remote sensing--Data processing, Image processing, Photographic interpretation
Format: Document (PDF)

Title: AUTOMATIC DETECTION OF BUILDING DAMAGE CAUSED BY HURRICANE ON FLORIDA COASTAL AREA FROM AERIAL IMAGES.
Creator: Gyegyiri, Joseph, Su, Hongbo, Florida Atlantic University, Department of Civil, Environmental and Geomatics Engineering, College of Engineering and Computer Science
Abstract/Description: Rapid response and efficient damage assessment are life-or-death matters in the wake of natural disasters such as hurricanes and earthquakes. These events wreak havoc on infrastructure and properties and, most critically, endanger human lives. The timely and effective allocation of resources during such crises is imperative, necessitating meticulous planning based on the extent of damage incurred. This research presents an approach to automating the damage assessment using pre/post-disaster...
Show moreRapid response and efficient damage assessment are life-or-death matters in the wake of natural disasters such as hurricanes and earthquakes. These events wreak havoc on infrastructure and properties and, most critically, endanger human lives. The timely and effective allocation of resources during such crises is imperative, necessitating meticulous planning based on the extent of damage incurred. This research presents an approach to automating the damage assessment using pre/post-disaster aerial images and computer vision. Recent advancements in disaster response strategies have encouraged researchers to harness the power of satellite and aerial imagery to assess the aftermath. Usually, due to the different characteristics between training datasets and available datasets in times of disasters, retraining the model to improve detection accuracy has been the norm, even though it is time and resource intensive. Our method surpasses conventional solutions and requires no retraining or fine-tuning on disaster-specific data. An existing model was retrained and improved on a diverse building damage dataset and demonstrably generalizes to new disaster scenarios. Having achieved higher performances compared to state of the art models, we determines our models real world applicability by using Hurricane Ian as our potent study grounds.
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Date Issued: 2024
PURL: http://purl.flvc.org/fau/fd/FA00014427
Subject Headings: Remote-sensing images, Natural disasters, Natural disasters--Data processing
Format: Document (PDF)

Title: EVALUATING UNMANNED AIRCRAFT SYSTEM PHOTOGRAMMETRY FOR COASTAL FLORIDA EVERGLADES RESTORATION AND MANAGEMENT.
Creator: Durgan, Sara D., Zhang, Caiyun, Florida Atlantic University, Department of Geosciences, Charles E. Schmidt College of Science
Abstract/Description: The Florida Everglades ecosystem is experiencing increasing threats from anthropogenic modification of water flow, spread of invasive species, sea level rise (SLR), and more frequent and/or intense hurricanes. Restoration efforts aimed at rehabilitating these ongoing and future disturbances are currently underway through the implementation of the Comprehensive Everglades Restoration Plan (CERP). Efficacy of these restoration activities can be further improved with accurate and site-specific...
Show moreThe Florida Everglades ecosystem is experiencing increasing threats from anthropogenic modification of water flow, spread of invasive species, sea level rise (SLR), and more frequent and/or intense hurricanes. Restoration efforts aimed at rehabilitating these ongoing and future disturbances are currently underway through the implementation of the Comprehensive Everglades Restoration Plan (CERP). Efficacy of these restoration activities can be further improved with accurate and site-specific information on the current state of the coastal wetland habitats. In order to produce such assessments, digital datasets of the appropriate accuracy and scale are needed. These datasets include orthoimagery to delineate wetland areas and map vegetation cover as well as accurate 3-dimensional (3-D) models to characterize hydrology, physiochemistry, and habitat vulnerability.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013501
Subject Headings: Everglades (Fla )--Environmental conditions--Remote sensing, Aerial photogrammetry, Wetland restoration--Florida--Everglades, Image analysis, Aerial photogrammetry--Data processing, Drone aircraft
Format: Document (PDF)

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Remote sensing (8)	+ -
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