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COMBINING TRADITIONAL AND IMAGE ANALYSIS TECHNIQUES FOR UNCONSOLIDATED EXPOSED TERRIGENOUS BEACH SAND CHARACTERIZATION
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)
EVALUATING UNMANNED AIRCRAFT SYSTEM PHOTOGRAMMETRY FOR COASTAL FLORIDA EVERGLADES RESTORATION AND MANAGEMENT
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)