Automated image analysis for petrographic image assessments

In this thesis, the algorithms developed for an automated image analysis toolkit called PetrograFX for petrographic image assessments, particularly thin section images, are presented. These algorithms perform two main functions, porosity determination and quartz grain measurements. For porosity determination, the pore space is segmented using a seeded region growing scheme in color space where the seeds are generated automatically based on the absolute R - B differential image. The porosity is then derived by pixel-counting to identify the pore space regions. For quartz grain measurements, adaptive thresholding is applied to make the system robust to the color variations in the entire image for the segmentation of the quartz grains. Median filtering and blob analysis are used to remove lines of fluid inclusions, which appear as black speckles and spots, on the quartz grains before the subsequent measurement operations are performed. The distance transformation and watershed transformation are then performed to separate connected objects. A modified watershed transformation is developed to eliminate false watersheds based on the physical nature of quartz grains. Finally, the grain are characterized in terms of NSD, which is the nominal sectional diameter, NSD distribution and sorting

Digital Image Processing

This book presents several recent advances that are related or fall under the umbrella of 'digital image processing', with the purpose of providing an insight into the possibilities offered by digital image processing algorithms in various fields. The presented mathematical algorithms are accompanied by graphical representations and illustrative examples for an enhanced readability. The chapters are written in a manner that allows even a reader with basic experience and knowledge in the digital image processing field to properly understand the presented algorithms. Concurrently, the structure of the information in this book is such that fellow scientists will be able to use it to push the development of the presented subjects even further

Post-depositional fracturing and subsidence of pumice flow deposits: Lascar Volcano, Chile

Unconsolidated pyroclastic flow deposits of the 1993 eruption of Lascar Volcano, Chile, have, with time, become increasingly dissected by a network of deeply penetrating fractures. The fracture network comprises orthogonal sets of decimeter-wide linear voids that form a pseudo-polygonal grid visible on the deposit surface. In this work, we combine shallow surface geophysical imaging tools with remote sensing observations and direct field measurements of the deposit to investigate these fractures and their underlying causal mechanisms. Based on ground penetrating radar images, the fractures are observed to have propagated to depths of up to 10 m. In addition, orbiting radar interferometry shows that deposit subsidence of up to 1 cm/year occurred between 1993 and 1996 with continued subsidence occurring at a slower rate thereafter. In situ measurements show that 1 m below the surface, the 1993 deposits remain 5°C to 15°C hotter, 18 years after emplacement, than adjacent deposits. Based on the observed subsidence as well as estimated cooling rates, the fractures are inferred to be the combined result of deaeration, thermal contraction, and sedimentary compaction in the months to years following deposition. Significant environmental factors, including regional earthquakes in 1995 and 2007, accelerated settling at punctuated moments in time. The spatially variable fracture pattern relates to surface slope and lithofacies variations as well as substrate lithology. Similar fractures have been reported in other ignimbrites but are generally exposed only in cross section and are often attributed to formation by external forces. Here we suggest that such interpretations should be invoked with caution, and deformation including post-emplacement subsidence and fracturing of loosely packed ash-rich deposits in the months to years postemplacement is a process inherent in the settling of pyroclastic material

Automatic target recognition based on cross-plot

Automatic target recognition that relies on rapid feature extraction of real-time target from photo-realistic imaging will enable efficient identification of target patterns. To achieve this objective, Cross-plots of binary patterns are explored as potential signatures for the observed target by high-speed capture of the crucial spatial features using minimal computational resources. Target recognition was implemented based on the proposed pattern recognition concept and tested rigorously for its precision and recall performance. We conclude that Cross-plotting is able to produce a digital fingerprint of a target that correlates efficiently and effectively to signatures of patterns having its identity in a target repository.Kelvin Kian Loong Wong and Derek Abbot

Advanced Geoscience Remote Sensing

Nowadays, advanced remote sensing technology plays tremendous roles to build a quantitative and comprehensive understanding of how the Earth system operates. The advanced remote sensing technology is also used widely to monitor and survey the natural disasters and man-made pollution. Besides, telecommunication is considered as precise advanced remote sensing technology tool. Indeed precise usages of remote sensing and telecommunication without a comprehensive understanding of mathematics and physics. This book has three parts (i) microwave remote sensing applications, (ii) nuclear, geophysics and telecommunication; and (iii) environment remote sensing investigations

Satellite interferometric data for landslide intensity evaluation in mountainous regions

Multi-Temporal Interferometric Synthetic Aperture Radar (MTInSAR) data offer a valuable support to landslide mapping and to landslide activity estimation in mountain environments, where in situ measures are sometimes difficult to gather. Nowadays, the interferometric approach is more and more used for wide-areas analysis, providing useful information for risk management actors but at the same time requiring a lot of efforts to correctly interpret what satellite data are telling us. In this context, hot-spot-like analyses that select and highlight the fastest moving areas in a region of interest, are a good operative solution for reducing the time needed to inspect a whole interferometric dataset composed by thousands or millions of points. In this work, we go beyond the concept of MTInSAR data as simple mapping tools by proposing an approach whose final goal is the quantification of the potential loss experienced by an element at risk hit by a potential landslide. To do so, it is mandatory to evaluate landslide intensity. Here, we estimate intensity using Active Deformation Areas (ADA) extracted from Sentinel-1 MTInSAR data. Depending on the localization of each ADA with respect to the urban areas, intensity is derived in two different ways. Once exposure and vulnerability of the elements at risk are estimated, the potential loss due to a landslide of a given intensity is calculated. We tested our methodology in the Eastern Valle d'Aosta (north-western Italy), along four lateral valleys of the Dora Baltea Valley. This territory is characterized by steep slopes and by numerous active and dormant landslides. The goal of this work is to develop a regional scale methodology based on satellite radar interferometry to assess the potential impact of landslides on the urban fabric

HUMAN AND CLIMATE IMPACTS ON FLOODING VIA REMOTE SENSING, BIG DATA ANALYTICS, AND MODELING

Over the last 20 years, the amount of streamflow has greatly increased and spring snowmelt floods have occurred more frequently in the north-central U.S. In the Red River of the North Basin (RRB) overlying portions of North Dakota and Minnesota, six of the 13 major floods over the past 100 years have occurred since the late 1990s. Based on numerous previous studies as well as senior flood forecasters’ experiences, recent hydrological changes related to human modifications [e.g. artificial subsurface drainage (SSD) expansion] and climate change are potential causes of notable forecasting failures over the past decade. My dissertation focuses on the operational and scientific gaps in current forecasting models and observational data and provides insights and value to both the practitioner and the research community. First, the current flood forecasting model needs both the location and installation timing of SSD and SSD physics. SSD maps were developed using satellite “big” data and a machine learning technique. Next, using the maps with a land surface model, the impacts of SSD expansion on regional hydrological changes were quantified. In combination with model physics, the inherent uncertainty in the airborne gamma snow survey observations hinders the accurate flood forecasting model. The operational airborne gamma snow water equivalent (SWE) measurements were improved by updating antecedent surface moisture conditions using satellite observations on soil moisture. From a long-term perspective, flood forecasters and state governments need knowledge of historical changes in snowpack and snowmelt to help flood management and to develop strategies to adapt to climate changes. However, historical snowmelt trends have not been quantified in the north-central U.S. due to the limited historical snow data. To overcome this, the current available historical long-term SWE products were evaluated across diverse regions and conditions. Using the most reliable SWE product, a trend analysis quantified the magnitude of change extreme snowpack and melt events over the past 36 years. Collectively, this body of research demonstrates that human and climate impacts, as well as limited and noisy data, cause uncertainties in flood prediction in the great plains, but integrated approaches using remote sensing, big data analytics, and modeling can quantify the hydrological changes and reduce the uncertainties. This dissertation improves the practice of flood forecasting in Red River of the North Basin and advances research in hydrology and snow science

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition