A deep learning approach to detecting volcano deformation from satellite imagery using synthetic datasets

Satellites enable widespread, regional or global surveillance of volcanoes and can provide the first indication of volcanic unrest or eruption. Here we consider Interferometric Synthetic Aperture Radar (InSAR), which can be employed to detect surface deformation with a strong statistical link to eruption. The ability of machine learning to automatically identify signals of interest in these large InSAR datasets has already been demonstrated, but data-driven techniques, such as convolutional neutral networks (CNN) require balanced training datasets of positive and negative signals to effectively differentiate between real deformation and noise. As only a small proportion of volcanoes are deforming and atmospheric noise is ubiquitous, the use of machine learning for detecting volcanic unrest is more challenging. In this paper, we address this problem using synthetic interferograms to train the AlexNet. The synthetic interferograms are composed of 3 parts: 1) deformation patterns based on a Monte Carlo selection of parameters for analytic forward models, 2) stratified atmospheric effects derived from weather models and 3) turbulent atmospheric effects based on statistical simulations of correlated noise. The AlexNet architecture trained with synthetic data outperforms that trained using real interferograms alone, based on classification accuracy and positive predictive value (PPV). However, the models used to generate the synthetic signals are a simplification of the natural processes, so we retrain the CNN with a combined dataset consisting of synthetic models and selected real examples, achieving a final PPV of 82%. Although applying atmospheric corrections to the entire dataset is computationally expensive, it is relatively simple to apply them to the small subset of positive results. This further improves the detection performance without a significant increase in computational burden

Characterization of Ground Deformation Associated with Shallow Groundwater Processes Using Satellite Radar Interferometry

Shallow groundwater processes maylead to ground deformation and even geohazards. With the features of day-and-night accessibility and large-scale coverage, time-series interferometric synthetic aperture radar (InSAR) has proven a useful tool for mapping the deformation over various landscapes at cm to mm level with weekly to monthly updates. However, it has limitations such as, decorrelation,atmospheric artifacts, topographic errors, andunwrapping errors, in particular for the hilly, vegetated, and complicated deformation patterns. In this dissertation, I focus on characterizing the ground deformation over landslides, aquifer systems, and mine tailings impoundment, using the designed advanced time-series InSAR strategy, as well as theinterdisciplinary knowledge of geodesy, hydrology, geophysics, and geology. Northwestern USA has been exposed to extreme landslide hazards due to steep terrain, high precipitation, and loose root support after wildfire. I characterize the rainfall-triggered movements of Crescent Lake landslide, Washington State. The seasonal deformation at the lobe, with larger magnitudes than the downslope riverbank, suggests an amplified hydrological loading effect due to a thicker unconsolidated zone. High-temporal-resolution InSAR and GPS data reveal dynamic landslide motions. Threshold rainfall intensities and durations wet seasons have been associated with observed movement upon shearing: antecedent rainfall triggered precursory slope-normal subsidence, and the consequent increase in pore pressure at the basal surface reduces friction and instigates downslope slip over the course of less than one month. In addition, a quasi-three-dimensional deformation field is created using multiple spaceborne InSAR observations constrained by the topographical slope, and is further used to invert for the complex geometry of landslide basal surface based on mass conservation. Aquifer skeletons deform in response to hydraulic head changes with various time scales of delay and sensitivity. I investigate the spatio-temporal correlation among deformation, hydrological records and earthquake records over Salt Lake Valley, Utah State. A clear long-term and seasonal correlation exists between surface uplift/subsidence and groundwater recharge/discharge, allowing me to quantify hydrogeological properties. Long-term uplift reflects the net pore pressure increase associated with prolonged water recharge, probably decades ago. The distributions of previously and newly mapped faults suggest that the faultsdisrupt the groundwater flow andpartition hydrological units. Mine tailings gradual settle as the pore pressure dissipates and the terrain subsides, andtailings embankment failures can be extremely hazardous. I investigate the dynamics of consolidation settlement over the tailings impoundment in the vicinity of Great Salt Lake, Utah State, as well as its associated impacts to the surrounding infrastructures. Largest subsidence has been observed around the low-permeable decant pond clay at the northeast corner.The geotechnical consolidation model reveals and predicts the long-term exponentially decaying settlement process. My studies have demonstrated that InSAR methods can advance our understanding about the potential anthropogenic impacts and natural hydrological modulations on various geodynamic settings in geodetic time scale

Coping with Data Scarcity in Deep Learning and Applications for Social Good

The recent years are experiencing an extremely fast evolution of the Computer Vision and Machine Learning fields: several application domains benefit from the newly developed technologies and industries are investing a growing amount of money in Artificial Intelligence. Convolutional Neural Networks and Deep Learning substantially contributed to the rise and the diffusion of AI-based solutions, creating the potential for many disruptive new businesses. The effectiveness of Deep Learning models is grounded by the availability of a huge amount of training data. Unfortunately, data collection and labeling is an extremely expensive task in terms of both time and costs; moreover, it frequently requires the collaboration of domain experts. In the first part of the thesis, I will investigate some methods for reducing the cost of data acquisition for Deep Learning applications in the relatively constrained industrial scenarios related to visual inspection. I will primarily assess the effectiveness of Deep Neural Networks in comparison with several classical Machine Learning algorithms requiring a smaller amount of data to be trained. Hereafter, I will introduce a hardware-based data augmentation approach, which leads to a considerable performance boost taking advantage of a novel illumination setup designed for this purpose. Finally, I will investigate the situation in which acquiring a sufficient number of training samples is not possible, in particular the most extreme situation: zero-shot learning (ZSL), which is the problem of multi-class classification when no training data is available for some of the classes. Visual features designed for image classification and trained offline have been shown to be useful for ZSL to generalize towards classes not seen during training. Nevertheless, I will show that recognition performances on unseen classes can be sharply improved by learning ad hoc semantic embedding (the pre-defined list of present and absent attributes that represent a class) and visual features, to increase the correlation between the two geometrical spaces and ease the metric learning process for ZSL. In the second part of the thesis, I will present some successful applications of state-of-the- art Computer Vision, Data Analysis and Artificial Intelligence methods. I will illustrate some solutions developed during the 2020 Coronavirus Pandemic for controlling the disease vii evolution and for reducing virus spreading. I will describe the first publicly available dataset for the analysis of face-touching behavior that we annotated and distributed, and I will illustrate an extensive evaluation of several computer vision methods applied to the produced dataset. Moreover, I will describe the privacy-preserving solution we developed for estimating the \u201cSocial Distance\u201d and its violations, given a single uncalibrated image in unconstrained scenarios. I will conclude the thesis with a Computer Vision solution developed in collaboration with the Egyptian Museum of Turin for digitally unwrapping mummies analyzing their CT scan, to support the archaeologists during mummy analysis and avoiding the devastating and irreversible process of physically unwrapping the bandages for removing amulets and jewels from the body

Radar Interferometry for Monitoring Crustal Deformation. Geodetic Applications in Greece

The chapatti and breadmaking quality of nine (eight Indian and one Australian) wheat (Triticum aestivum L.) cultivars was compared. The extension of a chapatti strip measured with a Kieffer dough extensibility rig correlated with chapatti scores for overall quality (r = 0.84), pliability (r = 0.91), hand feel (r = 0.72), chapatti eating quality (r = 0.68), and taste (r = 0.80). Overall chapatti quality also correlated with the resistance to extension of a chapatti strip (r = 0.68) when tested for uniaxial extension with a texture analyzer. The texture analyzer provided objectivity in the scoring of chapatti quality. The high-molecular-weight glutenin subunit protein composition assessed by sodium dodecyl sulfate polyacrylamide gel electrophoresis did not correlate with the overall chapatti score. A negative correlation was found between chapatti and bread scores (r = 0.77). The different requirements for chapatti and bread quality complicate the breeding of new wheat varieties and the exchange of germplasm between regions producing wheat for chapatti and those supplying bread producers

InSAR Deformation Analysis with Distributed Scatterers: A Review Complemented by New Advances

Interferometric Synthetic Aperture Radar (InSAR) is a powerful remote sensing technique able to measure deformation of the earth’s surface over large areas. InSAR deformation analysis uses two main categories of backscatter: Persistent Scatterers (PS) and Distributed Scatterers (DS). While PS are characterized by a high signal-to-noise ratio and predominantly occur as single pixels, DS possess a medium or low signal-to-noise ratio and can only be exploited if they form homogeneous groups of pixels that are large enough to allow for statistical analysis. Although DS have been used by InSAR since its beginnings for different purposes, new methods developed during the last decade have advanced the field significantly. Preprocessing of DS with spatio-temporal filtering allows today the use of DS in PS algorithms as if they were PS, thereby enlarging spatial coverage and stabilizing algorithms. This review explores the relations between different lines of research and discusses open questions regarding DS preprocessing for deformation analysis. The review is complemented with an experiment that demonstrates that significantly improved results can be achieved for preprocessed DS during parameter estimation if their statistical properties are used

ALOS-2/PALSAR-2 Calibration, Validation, Science and Applications

Twelve edited original papers on the latest and state-of-art results of topics ranging from calibration, validation, and science to a wide range of applications using ALOS-2/PALSAR-2. We hope you will find them useful for your future research

Novel Approaches in Landslide Monitoring and Data Analysis

Significant progress has been made in the last few years that has expanded the knowledge of landslide processes. It is, therefore, necessary to summarize, share and disseminate the latest knowledge and expertise. This Special Issue brings together novel research focused on landslide monitoring, modelling and data analysis