December 18, 2018 at 10:00am - Ph.D. Thesis Defense - Kevan Donlon - On Interpixel Capacitive Coupling in Hybridized HgCdTe Arrays: Theory, Characterization and Correction

DIRS Laboratory 76-3215
December 18, 2018 at 10:00am
Kevan Donlon
On Interpixel Capacitive Coupling in Hybridized HgCdTe Arrays: Theory, Characterization and Correction
Ph.D. Thesis Defense
Abstract: 

 

 

Hybridization is a process by which detector arrays and read out circuitry can be independently fabricated and then bonded together, typically using indium bumps. This technique allows for the use of exotic detector materials such as HgCdTe for the desired spectral response while benefiting from established and proven silicon CMOS readout structures. However, the introduction of an intermediate layer composed of conductors (indium) and insulators (epoxy) results in a capacitive link between adjacent pixels.

This interpixel capacitance (IPC) results in charge collected on one pixel, giving rise to a change in voltage on the output node of adjacent pixels. In imaging arrays, this capacitance manifests itself as a blur, attenuating high spatial frequency information and causing single pixel events to be spread over a local neighborhood. Due to the nature of the electric fields in proximity to the depletion region of the diodes in the detector array, the magnitude of this capacitance changes as the diode depletes. This change in capacitance manifests itself as a change in fractional coupling. This results in a blur kernel that is non-homogeneous both spatially across the array and temporally from exposure to exposure, varying as a function of charge collected in each pixel. This signal dependent behavior invalidates underlying assumptions key for conventional deconvolution/deblurring techniques such as Weiner filtering or Lucy-Richardson deconvolution. As such, these techniques cannot be relied upon to restore scientific accuracy and appropriately solve this inverse problem.

This dissertation uses first principle physics simulations to elucidate the mechanisms of IPC, establishes a data processing technique which allows for characterization of IPC, formalizes and implements a nonlinear deconvolution method by which the effects of IPC can be undone, and examines the impact that IPC can have on scientific conclusions if left uncorrected.

 

 

December 17, 2018 at 10:00am - M.S. Thesis Defense - TIMOTHY RUPRIGHT - Multi-Modal Analysis of Deciduous Tree Stands Toward Ash Tree Inventory: Biomass Estimation and Genus-Level Discrimination In a Mixed Deciduous Forest

DIRS Laboratory 76-3215
December 17, 2018 at 10:00am
TIMOTHY RUPRIGHT
Multi-Modal Analysis of Deciduous Tree Stands Toward Ash Tree Inventory: Biomass Estimation and Genus-Level Discrimination In a Mixed Deciduous Forest
M.S. Thesis Defense
Abstract: 

The invasive species Agrilus planipennis (Emerald Ash Borer or EAB) is currently impacting ash trees in a large part of the continental United States.  One way of measuring the effect of this infestation on the US markets is to determine the spread of the species and the biomass destruction/loss due to this invasive pest.  In order to assess such impacts, it is necessary to determine how and where ash trees are located toward accurately measuring the relevant ash species biomass and tree count.

 

This study utilizes data captured over the campus of RIT in Rochester, NY.  Hyperspectral data, captured by SpecTIR, and discrete LiDAR data collected by an ALS 50 are used in an attempt to accomplish two tasks:  1) estimate ash biomass and 2) create a discriminant model to determine what types of trees (genera) are located within our surveyed plots.  We surveyed four deciduous plots on the RIT campus and integrated the surveyed areas, the LiDAR data, and the hyperspectral data into a single comprehensive dataset.  Our assessment incorporated different underlying models, including hyperspectral data only, LiDAR data only, and a combination of hyperspectral and LiDAR data.

 

The results indicate that we can predict biomass with an R2 value between 0.55-0.69, at an α=0.01 statistical threshold and an R2 value between 0.85-0.92 (α=0.05 threshold) with the best models.  The results indicate that smaller plot radius hyperspectral plus LiDAR and larger radius hyperspectral approaches scored best for R2 values, but the best RMSE was returned by the model utilizing the larger-radius hyperspectral data plus LiDAR returns. 

 

The genus-level classification analysis utilized a stepwise discriminant model to extract relevant variables, followed by a linear discriminant classification which classified each tree based on the stepwise results.  These models found that one-meter hyperspectral data plus LiDAR could accurately assess the genus level of the trees 86% of the time, with a KHAT score of 0.86.  User and producer accuracies on that model vary from 73-100%, depending on the genus.

 

This study contributes to the effort for combining hyperspectral and LiDAR data to assess deciduous tree stands.  Such an approach to biomass modeling is often used in coniferous forests with high accuracy; however, the variability in uneven-aged and complex deciduous forest typically leads to poorer structural assessment outcomes, even though such species are spectrally more differentiable. Results here indicate that utilizing more robust LiDAR scans (point density) and techniques (data fusion), these methods could yield valuable genus-level biomass or carbon maps per forest genus. Such maps in turn could prove useful to decision-makers from policy experts to conservationists.

 

 

December 7, 2018 at 8:00am - Ph.D. Thesis Defense - ZHAOYU CUI - System Engineering Analyses for the Study of Future Multispectral Land Imaging Satellite Sensors for Vegetation Monitoring

DIRS Laboratory 76-3215
December 7, 2018 at 8:00am
ZHAOYU CUI
System Engineering Analyses for the Study of Future Multispectral Land Imaging Satellite Sensors for Vegetation Monitoring
Ph.D. Thesis Defense
Abstract: 

  Vegetation monitoring is one of the key applications of earth observing systems. Landsat data have spatial resolution of 30 meters, moderate temporal coverage, and reasonable spectral sampling to capture key vegetation features. These characteristics of Landsat make it a good candidate for generating vegetation monitoring products. Recently, the next satellite in the Landsat series has been under consideration and different concepts have been proposed. In this research, we studied the impact on vegetation monitoring of two proposed potential design concepts: a wider field-of-view (FOV) instrument and the addition of red-edge spectral band(s). Three aspects were studied in this thesis.

First, inspired by the potential wider FOV design, the impacts of a detector relative spectral response (RSR) central wavelength shift effect at high angles of incidence (AOI) on the radiance signal were studied and quantified. Results indicated that the RSR shift effect was more significant in green, red and SWIR2 bands, and will cause a radiance difference exceeding sensor noise specifications in all bands except SWIR1 band.

Second, the impacts of the potential new wider angular observations on vegetation monitoring scientific products were studied. Both crop classification and biophysical parameter retrieval applications were studied using the simulation code DIRSIG and the canopy radiative transfer model PROSAIL. Results show that for single view observation based analysis, the new higher angular observations have limited influence. However, for situations where two different angular observations are available potentially from two platforms, up to 4% and 2.9% improvement for crop classification and leaf chlorophyll content retrieval were found.

Third, the benefits of a potential new design with red-edge band(s) in future Landsat instruments on agroecosystem leaf area index (LAI) and canopy chlorophyll content (CCC) retrieval were studied and quantified using a real dataset. Three major retrieval approaches were tested and results show that retrieval performance were slightly improved.

November 19, 2018 at 11:00am - Ph.D. Thesis Defense - Shagan Sah - Multi-Modal Deep Learning to Understand Vision and Language

DIRS Laboratory 76-3215
November 19, 2018 at 11:00am
Shagan Sah
Multi-Modal Deep Learning to Understand Vision and Language
Ph.D. Thesis Defense
Abstract: 

Developing intelligent agents that can perceive and understand the rich visual world around us has been a long-standing goal in the field of artificial intelligence.  In the last few years, significant progress has been made towards this goal and deep learning has been attributed to recent incredible advances in general visual and language understanding.  Convolutional neural networks have been used to learn image representations while recurrent neural networks have demonstrated the ability to generate text from visual stimuli.  In this thesis, we develop methods and techniques using hybrid convolutional and recurrent neural network architectures that connect visual data and natural language utterances.

 

Towards appreciating these methods, this work is divided into two broad groups.  Firstly, we introduce a general purpose attention mechanism modeled using a continuous function for video understanding.  The use of an attention based hierarchical approach along with automatic boundary detection advances state-of-the-art video captioning results.  We also develop techniques for summarizing and annotating long videos.  In the second part, we introduce architectures along with training techniques to produce a common connection space where natural language sentences are efficiently and accurately connected with visual modalities.  In this connection space, similar concepts lie close, while dissimilar concepts lie far apart, irrespective of their modality.  We discuss four modality transformations: visual to text, text to visual, visual to visual and text to text. We introduce a novel attention mechanism to align multi-modal embeddings which are learned through a multi-modal metric loss function.  The common vector space is shown to enable bidirectional generation of images and text.  The learned common vector space is evaluated on multiple image-text datasets for cross-modal retrieval and zero-shot recognition.  The models are shown to advance the state-of-the-art on tasks that require joint processing of images and natural language.

 

November 2, 2018 at 9:00am - Ph.D. Thesis Defense - ANTON TRAVINSKY - Evaluating the performance of digital micromirror devicEvaluating the performance of digital micromirror devices for use as slit masks in multi-object spectrometerses for use as slit masks in multi-object spectrometers

DIRS Laboratory 76-3215
November 2, 2018 at 9:00am
ANTON TRAVINSKY
Evaluating the performance of digital micromirror devicEvaluating the performance of digital micromirror devices for use as slit masks in multi-object spectrometerses for use as slit masks in multi-object spectrometers
Ph.D. Thesis Defense
Abstract: 

Multi-object spectrometers (MOSs) are extremely useful astronomical instruments that allow for spectral observations of up to several thousands of objects simultaneously by using an object input selector commonly referred to as slit mask. Studies performed with such instruments in the last three decades placed unique constraints on cosmology, large scale structure, galaxy evolution, and Galactic structure. Terrestrial MOSs use large discrete components for object selection, which, aside from not being transferable to space-based applications, are limited in both minimal slit width and minimal time required to reconfigure the slit mask to a new field of objects. There is a pressing need in remotely addressable and fast-re-configurable slit masks for allowing space-based instruments with MOS capabilities. Digital micromirror devices (DMDs) can be viable candidates for the role of remotely re-configurable slit mask in both terrestrial and space-based MOSs. These devices were originally developed by Texas Instruments (TI) for projection systems and are the core part of the TI digital light processing (DLP) technology. This work focused on assessing the suitability of DMDs to be used as slit masks in space-based astronomical MOSs. The results of typical pre-launch tests such as radiation testing, vibration testing, and mechanical shock testing suggest that commercially available DMDs are mechanically suitable for space-deployment. Series of tests to assess the performance and the behaviour of DMDs in cryogenic temperatures (down to 70 K) did not identify any problems with subjecting commercially available DMDs to such temperatures for extended periods of time. An early prototype of terrestrial DMD-based MOS (Rochester Institute of Technology Multi Object Spectrometer-RITMOS) was updated with a newer DMD model and tested through two deployments at the CEK Mees observatory in Naples, NY. The results of all experiments strongly suggest that DMDs are well-positioned to serve as slit masks in terrestrial MOS and to enable a new generation of space-based instruments - with MOS capabilities.


 

 

 

October 29, 2018 at 3:00am - Ph.D. Thesis Defense - ZICHAO HAN - The design and realization of a dual mode photoacoustic and ultrasound imaging camera

DIRS Laboratory 76-3215
October 29, 2018 at 3:00am
ZICHAO HAN
The design and realization of a dual mode photoacoustic and ultrasound imaging camera
Ph.D. Thesis Defense
Abstract: 

Prostate cancer is currently the second leading cause of cancer death in American men. Diagnosis of the disease is based on persisting elevated prostate-specific antigen (PSA) levels and suspicious lesion felt on digital rectal examination (DRE), prompting transrectal ultrasound (TRUS) imaging guided biopsy. This method, however, has long been criticized for its poor sensitivity in detecting cancerous lesions, leading to the fact that these biopsies generally are not targeted but systematic multi-core in nature that try to sample the entire gland. The thesis presents a new modality that, in combination of ultrasound (US) imaging with multi-wavelength photoacoustic (PA) imaging, improves the physician’s ability to locate the suspicious cancerous regions during biopsy.

Here, building further on the innovation of an acoustic lens based focusing technology for fast PA imaging, a novel concept with the use of a polyvinylidene fluoride (PVDF) film that incorporates US imaging into our existing PA imaging probe is presented. The method takes advantage of the lens based PA signal focusing technology, while simultaneously incorporates US imaging modality without interfering with the current PA imaging system design and structure. Simulation and experimental support on tissue equivalent phantoms is provided in detail. The thesis also elaborates on the signal-to-noise ratio (SNR) improvement of the US imaging component by driving the film with frequency modulated (FM) signals. In addition, a custom-designed US simulation software that is developed to explore and evaluate various system design options is discussed. The dual modality transrectal probe is only intended as a first step. The long term goal of the study is to facilitate locating the cancer region in-vivo with PA imaging, transfer it to co-registered US image, and use the real-time US imaging for needle guidance during biopsy.

October 18, 2018 at 1:00am - Ph.D. Thesis Defense - GREG BADURA - Studies on the Photometric Effect of Roughness

DIRS Laboratory 76-3215
October 18, 2018 at 1:00am
GREG BADURA
Studies on the Photometric Effect of Roughness
Ph.D. Thesis Defense
Abstract: 

A major focus of the Goniometer of Rochester Institute of Technology (GRIT) Laboratory is using Hapke’s photometric model to retrieve geophysical parameters of sediment surfaces from bidirectional reflectance measurements. An important parameter of the model is the mean slope angle roughness metric.  A series of laboratory experiments was performed to isolate the mean slope angle parameter and correlate the parameter to observed spectral phenomena. In the first experiment, BRF and surface digital elevation measurements were performed on dry clay sediments of varying roughness. The Hapke mean slope angle parameter was derived for each sample. We found that spectral variability, especially near spectral absorption features correlates strongly with quantified measures of surface roughness. This suggests that roughness parameters used in some radiative transfer models, such as the Hapke model, might be directly determined from the spectrum itself. In the second experiment, assumptions made by Hapke in deriving the photometric roughness correction are tested by generating sand samples of constant sample density and grain size distribution, but varying roughness. The type of roughness was also classified into two different cases: “wave-like” and “grid-like.” The “grid-like” roughness parameter meets the criterion outlined by Hapke in his correction factor, while the “wave-like” roughness parameter does not. By experimentally forward propagating Hapke’s roughness correction factor for the “grid-like” roughness samples, we find that the Shadowing function potentially does not account for centimeter scale roughness accurately. By examining the bidirectional reflectance measurements of the “wave-like” roughness at different orientations to the principal plane, we found evidence that the multiple scattering term should be incorporated into future correction factors for surface roughness. A third experiment that outlines a processing chain for deriving structural parameters of marshgrass vegetation using similar computer vision and data science techniques will also be discussed.

October 17, 2018 at 2:00am - Ph.D. Thesis Defense - CHI ZHANG - Evolution of A Common Vector Space Approach to Computer Vision Problems

DIRS Laboratory 76-3215
October 17, 2018 at 2:00am
CHI ZHANG
Evolution of A Common Vector Space Approach to Computer Vision Problems
Ph.D. Thesis Defense
Abstract: 

 

 

The concept of the Common Vector Space (CVS) is introduced in this research to deal with multi-modal conversion problems. Focusing on image and text, image (or video frame) understanding can be achieved using CVS. With this concept, modality generation and other relevant applications are also considered in this research, for example, automatic image description, text paraphrasing, etc. Specifically, video sequences can be modeled by Recurrent Neural Networks (RNN), the greater depth of the RNN leads to smaller error, but that makes the gradient in the network unstable during training. To overcome this problem, a Batch-Normalized Recurrent Highway Network (BNRHN) was developed and tested on the image captioning (image-to-text) task. In BNRHN, the highway layers are incorporated with batch normalization which diminish the gradient vanishing and exploding problem. In addition, a sentence-to-vector encoding framework that is suitable for advanced natural language processing is developed. This semantic text embedding makes use of the encoder-decoder model which is trained on sentence paraphrase pairs (text-to-text). With this scheme, the latent representation of the text is shown to encode sentences with common semantic information with similar vector representations. In addition to image-to-text and text-to-text, an image generation model is developed to generate image from text (text-to-image) or another image (image-to-image) based on the semantics of the content. The developed model, which refers to the Multi-Modal Vector Representation (MMVR), builds and encodes different modalities into a common vector space that achieve the goal of keeping semantics and conversion between text and image bidirectional. In theory, this method works not only on text and image, but also can be generalized to other modalities, such as video and audio. The characteristics and performance are supported by both theoretical analysis and experimental results. Interestingly, the MMVR model is one of the many possible ways to build CVS. In the final stages of this research, a simple and straightforward framework to build CVS, which is considered as an alternative to the MMVR model, is presented.

October 3, 2018 at 10:30am - Studies on the Photometric Effect of Roughness

October 3, 2018 at 10:30am
Studies on the Photometric Effect of Roughness

October 3, 2018 at 10:30am - GREG BADURA - Studies on the Photometric Effect of Roughness

October 3, 2018 at 10:30am
GREG BADURA
Studies on the Photometric Effect of Roughness

September 13, 2018 at 11:15am - CHI ZHANG - Evolution of A Common Vector Space Approach to Computer Vision Problems

September 13, 2018 at 11:15am
CHI ZHANG
Evolution of A Common Vector Space Approach to Computer Vision Problems

July 18, 2018 at 9:00am - MS Thesis Defense - Michael P. McClelland II - An Assessment of Small Unmanned Aerial Systems in Support of Sustainable Forestry Management Initiatives

DIRS Laboratory 76-3215
July 18, 2018 at 9:00am
Michael P. McClelland II
An Assessment of Small Unmanned Aerial Systems in Support of Sustainable Forestry Management Initiatives
MS Thesis Defense
Abstract: 

Abstract

 

            Sustainable forest management practices are receiving renewed attention in the growing effort to make efficient use of natural resources. Sustainable management approaches require accurate and timely measurement of the world’s forests to monitor biomass, and changes in sequestered carbon. It is in this context that remote sensing technologies, which possess the capability to rapidly capture structural data of entire forests, have become a key research area. Laser scanning systems, also known as lidar (light detection and ranging), have reached a maturity level where they may be considered a standard data source for structural measurements of forests; however, airborne lidar mounted on manned aircraft can be cost-prohibitive. The increasing performance capabilities and reduction of cost associated with small, unmanned aerial systems (sUAS) provide the potential for a cost effective alternative. Our objectives were to assess the extensibility of lidar algorithms to sUAS data and to evaluate the use of more cost-effective structure-from-motion (SfM) point cloud generation techniques. A data collection was completed by both manned and sUAS sensing systems in Lebanon, VA and Asheville, NC. A cost analysis, two carbon models and a harvest detection algorithm were explored to test performance. It was found that the sUAS performed similarly on one of the two biomass models with competitive costs of $8.12/acre, compared to the manned aircraft’s cost of $8.09/acre, excluding mobilization costs of the manned system. The sUAS effort did not include enough data for training the second model. However, a proxy data set was generated from the manned aircraft, with similar results to the full resolution data, which then was compared to four overlapping plots of each data set, noting good agreement (RMSE = 4.33 Mg/ha). Producer’s accuracy, User’s accuracy, and the Kappa statistic for detection of harvested plots were 94%, 87% and 87%, respectively. A leave-one-out cross validation scheme was applied to the classifier, using 1000 iterations, with the mean values presented in this study. This classifier showed that the detection of harvested and non-harvested forest is possible with simple metrics derived from the vertical structure of the forest. Due to the closed nature of the forest canopy, the SfM data did not contain many ground points, and thus, was not able to match the airborne lidar’s performance. It did provide fine detail of the forest canopy from the sUAS platform. Overall, we concluded that sUAS is a viable alternative to airborne manned sensing platforms for fine-scale, local forest assessments, but there is a level of system maturity that needs to be attained for larger area applications.

 

July 2, 2018 at 2:00am - Ph.D. Thesis Defense - Chao Zhang - Functional Imaging Connectome of the Human Brain and its Associations with Biological and Behavioral Characteristics

DIRS Laboratory 76-3215
July 2, 2018 at 2:00am
Chao Zhang
Functional Imaging Connectome of the Human Brain and its Associations with Biological and Behavioral Characteristics
Ph.D. Thesis Defense
Abstract: 

Abstract

 

Functional connectome of the human brain explores the temporal associations of different brain regions. Functional connectivity (FC) measures derived from resting state functional magnetic resonance imaging (rfMRI) characterize the brain network at rest and studies have shown that rfMRI FC is closely related to individual subject’s biological and behavioral measures. In this thesis we investigate a large rfMRI dataset from the Human Connectome Project (HCP) and utilize statistical methods to facilitate the understanding of fundamental FC–behavior associations of the human brain. Our studies include reliability analysis of FC statistics, demonstration of FC spatial patterns, and predictive analysis of individual biological and behavioral measures using FC features. Covering both static and dynamic FC (sFC and dFC) characterizations, the baseline FC patterns in healthy young adults are illustrated. Predictive analyses demonstrate that individual biological and behavioral measures, such as gender, age, fluid intelligence and language scores, can be predicted using FC. While dFC by itself performs worse than sFC in prediction accuracy, if appropriate parameters and models are utilized, adding dFC features to sFC can significantly increase the predictive power. Results of this thesis contribute to the understanding of the neural underpinnings of individual biological and behavioral differences in the human brain.

June 8, 2018 at 10:00am - MS Thesis Defense - NILAY MOKASHI - EMPIRICAL SATELLITE CHARACTERIZATION FOR REALISTIC IMAGERY GENERATION

CAR-3215
June 8, 2018 at 10:00am
NILAY MOKASHI
EMPIRICAL SATELLITE CHARACTERIZATION FOR REALISTIC IMAGERY GENERATION
MS Thesis Defense
Abstract: 

Abstract

 

There is an increasing interest in the use of machine learning deep networks to automatically analyze satellite imagery. However, there are limited annotated satellite imagery datasets available for training these networks. Synthetic image generation offers a solution to this need, but only if the simulated images have comparable characteristics to the real data. This work deals with analysis of commercial satellite imagery to characterize their imaging systems for the purpose of increasing the realism of the synthetic imagery generated by RIT’s Digital Imaging and Remote Sensing Image Generation (DIRSIG) model.

 

The analysis was applied to satellite imagery from Planet Labs and Digital Globe. Local spatial correlation was leveraged for noise estimation and the EMVA1288 standard was used for noise modeling. Real world calibration targets across the world were used together with the slanted edge method based on the ISO 12233 standard for estimation of the sensor optical systems’ point spread function (PSF). The estimated camera models were then used to generate synthetic imagery using DIRSIG. The PSF was applied within DIRSIG using its in-built functionality while noise was added in post processing. Analysis similar to real imagery was performed on the simulated scenes to verify the application of the model on synthetic scenes. Future work is recommended to further characterize the various imagery products produced by the satellite companies to better represent artifacts present in these processed images.

 

June 2, 2018 at 2:00am - Ph.D. Thesis Defense - Chao Zhang - Functional Imaging Connectome of the Human Brain and its Associations Functional Imaging Connectome of the Human Brain and its Associations with Biological and Behavioral Characteristics with Biological and Behavioral Characteristics

DIRS Laboratory 76-3215
June 2, 2018 at 2:00am
Chao Zhang
Functional Imaging Connectome of the Human Brain and its Associations Functional Imaging Connectome of the Human Brain and its Associations with Biological and Behavioral Characteristics with Biological and Behavioral Characteristics
Ph.D. Thesis Defense
Abstract: 

Abstract

 

Functional connectome of the human brain explores the temporal associations of different brain regions. Functional connectivity (FC) measures derived from resting state functional magnetic resonance imaging (rfMRI) characterize the brain network at rest and studies have shown that rfMRI FC is closely related to individual subject’s biological and behavioral measures. In this thesis we investigate a large rfMRI dataset from the Human Connectome Project (HCP) and utilize statistical methods to facilitate the understanding of fundamental FC–behavior associations of the human brain. Our studies include reliability analysis of FC statistics, demonstration of FC spatial patterns, and predictive analysis of individual biological and behavioral measures using FC features. Covering both static and dynamic FC (sFC and dFC) characterizations, the baseline FC patterns in healthy young adults are illustrated. Predictive analyses demonstrate that individual biological and behavioral measures, such as gender, age, fluid intelligence and language scores, can be predicted using FC. While dFC by itself performs worse than sFC in prediction accuracy, if appropriate parameters and models are utilized, adding dFC features to sFC can significantly increase the predictive power. Results of this thesis contribute to the understanding of the neural underpinnings of individual biological and behavioral differences in the human brain.

 

April 25, 2018 at 12:00pm - Ph.D. Thesis Defense - RONALD M. KEMKER - LOW-SHOT LEARNING FOR THE SEMANTIC SEGMENTATION OF REMOTE SENSING IMAGERY

DIRS Laboratory 76-3215
April 25, 2018 at 12:00pm
RONALD M. KEMKER
LOW-SHOT LEARNING FOR THE SEMANTIC SEGMENTATION OF REMOTE SENSING IMAGERY
Ph.D. Thesis Defense
Abstract: 

Abstract

 

Deep-learning frameworks have made remarkable progress thanks to the creation of large annotated datasets such as ImageNet, which has over one million training images.  Although this works well for color (RGB) imagery, labeled datasets for other sensor modalities (e.g., multispectral and hyperspectral) are minuscule in comparison.  This is because annotated datasets are expensive and man-power intensive to complete; and since this would be impractical to accomplish for each type of sensor, current state-of-the-art approaches in computer vision are not ideal for remote sensing problems.  The shortage of annotated remote sensing imagery beyond the visual spectrum has forced researchers to embrace unsupervised feature extracting frameworks.  These features are learned on a per-image basis, so they tend to not generalize well across other datasets.  In this dissertation, we propose three new strategies for learning feature extracting frameworks with only a small quantity of annotated image data; including 1) self-taught feature learning, 2) domain adaptation with synthetic imagery, and 3) semi-supervised classification.  ``Self-taught''  feature learning frameworks are trained with large quantities of unlabeled imagery, and then these networks extract spatial-spectral features from annotated data for supervised classification.  Synthetic remote sensing imagery can be used to boot-strap a deep convolutional neural network, and then we can fine-tune the network with real imagery.  Semi-supervised classifiers prevent overfitting by jointly optimizing the supervised classification task along side one or more unsupervised learning tasks (i.e., reconstruction).  Although obtaining large quantities of annotated image data would be ideal, our work shows that we can make due with less cost-prohibitive methods which are more practical to the end-user.

April 25, 2018 at 3:00am - MS Thesis Defense - NAJAT A. ALHARBI - ALKALI ACTIVATED SLAG CHARACTERIZATION BY SCANNING ELECTRON MICROSCOPY AND X-RAY MICROSANALYSIS

CAR-3215
April 25, 2018 at 3:00am
NAJAT A. ALHARBI
ALKALI ACTIVATED SLAG CHARACTERIZATION BY SCANNING ELECTRON MICROSCOPY AND X-RAY MICROSANALYSIS
MS Thesis Defense

Abstract

 

Blast furnace slag is a non-metallic byproduct generated by the production of iron and steel in a blast furnace at temperatures in the range of 1400°-1600° C. The alkali activation of blast furnace slag has the potential to reduce the environmental impact of cementitious materials and to be applied in geographic zones where weather is a factor that negatively affects performance of materials based on Ordinary Portland Cement. Alkali-activated blast furnace slag cements have been studied since the 1930s due to its high compressive strength; they can exceed 100 MPa in 28 days. The low Ca/Si ratio in slag improves its resistance to aggressive chemical materials such as acids, chlorides and sulphates. Blast furnace slag is a highly heterogeneous material. It is well known that its chemical composition affects the physical properties of the alkali activated material, however there is little work on how these inhomogeneities affect the microstructure and pore formation. In this study we characterize slag cement activated with KOH using several methods: x-ray diffraction (XRD), transmission electron microscopy (TEM), scanning electron microscopy (SEM), x-ray microanalysis (EDS), and quantitative element mapping. Attention is focused on delineating the phases induced by the alkali activation, as these phases are important in determining the mechanical properties of the material. For the alkaline activated slag, we found four phases. One phase was the particles carried over from the unactivated slag, but with significant changes in the chemical composition. In addition, three other phases were found -- one is rich in hydrotalcite and two phases were calcium aluminum silicate hydrate (C-A-S-H) predominant..

 

January 16, 2018 at 9:30am - Ph.D. Thesis Defense - KAMRAN BINAEE - Study of Human Eye-Hand Coordination Using Machine Learning Techniques in a Virtual Reality Setup

DIRS Laboratory 76-3215
January 16, 2018 at 9:30am
KAMRAN BINAEE
Study of Human Eye-Hand Coordination Using Machine Learning Techniques in a Virtual Reality Setup
Ph.D. Thesis Defense
Abstract: 
Theories of visually guided action are characterized as closed-loop control in the presence
of reliable sources of visual information, and predictive control to compensate for
visuomotor delay and temporary occlusion. However, prediction is not well understood. To
investigate, a series of studies was designed to characterize the role of predictive
strategies in humans as they perform visually guided actions, and to guide the
development of computational models that capture these strategies. During data collection,
subjects immersed in virtual reality (VR) were tasked with using a paddle to intercept a
virtual ball. To force subjects into a predictive mode of control, the ball was occluded or
made invisible for a portion of its 3D parabolic trajectory. The subject’s gaze, hand and
head movements were recorded during the performance. To improve the quality of gaze
estimation, new algorithms were developed for the measurement and calibration of spatial
and temporal errors of an eye tracking system.
The analysis focused on the subjects’ gaze and hand movements reveal that, when the
temporal constraints of the task did not allow the subjects to use closed-loop control, they
utilized a short-term predictive strategy. Insights gained through behavioral analysis were
formalized into computational models of visual prediction using machine learning
techniques. In one study, LSTM recurrent neural networks were utilized to explain how
information is integrated and used to guide predictive movement of the hand and eyes. In a
subsequent study, subject data was used to train an inverse reinforcement learning (IRL)
model that captures the full spectrum of strategies from closed-loop to predictive control of
gaze and paddle placement. A comparison of recovered reward values between occlusion
and no-occlusion conditions revealed a transition from online to predictive control
strategies within a single course of action. This work has shed new insights into predictive
strategies that guide our eye and hand movements.

February 21, 2017 at 2:00am - PhD Imaging Science Thesis Defense - KELLY LARABY - Landsat Land Surface Temperature Product: Global Validation and Uncertainty Estimation

DIRS Lab 76-3215
February 21, 2017 at 2:00am
KELLY LARABY
Landsat Land Surface Temperature Product: Global Validation and Uncertainty Estimation
PhD Imaging Science Thesis Defense

 

Advisor: Dr. John Schott

 

 

Abstract: 

Abstract

 

Land surface temperature (LST) is an Earth system data record that is important to many areas of study such as change detection, climate research, and smaller scale applications such as monitoring lakes and farms. LST is often derived from satellite thermal imagery to achieve adequate spatial and temporal coverage. The Landsat series of satellites are an unparalleled and attractive choice for developing an LST product, because they provide the longest running source of continuously acquired multispectral imagery. Landsat also has moderate spatial and temporal resolutions, and its sensors and data archives are well calibrated. The land surface temperature can be derived from a single Landsat thermal band if the atmosphere and surface emissivity are well known for each scene. The primary function of our algorithm is to perform atmospheric compensation on a per-pixel level, but eventually our process will be integrated with a global emissivity database to form the full LST product. 
 

The LST algorithm was initially limited to Landsat scenes in North America, which motivated our efforts to extend the algorithm’s operability to the entire globe. This effort allowed us to perform a thorough global validation for Landsat 7. Another portion of our work was focused on developing a method for estimating the uncertainty in the LST retrievals, so that users can make informed decisions on which pixels to use. This was accomplished by dividing the global validation data into different ranges of cloud proximity and transmission, then using the root mean square error (RMSE) for each group to help define uncertainty. When transmission was greater than 0.7 and clouds were at least 5 km away from the pixel of interest, the difference between our predictions and the observed error in LST had RMSEs of roughly 1 K. When a bias removal technique was used on the observed LST errors, the RMSEs for the same conditions were reduced to around 0.75 K. Based on these values, we are confident that our uncertainty estimation method will be a useful addition to the LST product.

October 12, 2016 at 8:00am - MS Thesis Defense - David Rhodes - Radiometrically Correct Synthetic Video Development of Thermal Vehicle Targets

76-2155
October 12, 2016 at 8:00am
David Rhodes
Radiometrically Correct Synthetic Video Development of Thermal Vehicle Targets
MS Thesis Defense
Abstract: 

 

 

 

Collecting large scientific quality thermal infrared image and video data sets is an expensive time consuming endeavor. Thermal infrared imagers cost much more than comparable visible systems and require skilled experienced operators. Also, time and experienced personnel are required to collect quality ground truth. Often it is advantageous to perform computer simulations as an alternative to collecting image and video data with real camera systems. As long as enough physics is incorporated into the models to give accurately comparable results to real imagery, simulated data can be used interchangeably. Generating synthetic images and video has the added benefit of being flexible as the user has control over every aspect of the simulation. Simulations are not subject to restrictions such as location, weather conditions, time of day, or time of year. Ground truth is assigned instead of measured in the synthetic world so it is known a priori. This thesis illustrates a method of using the Digital Image and Remote Sensing Image Generation (DIRSIG) software to create simulated infrared images and video of validated thermal target vehicle models inside thermal infrared wide-area scenes. A finite difference heat propagation and surface temperature solver, ThermoAnalytics Multi-Service Electro-optic Signature (MuSES TM),was used to accurately model the emissive thermal target vehicles. Validation of the thermal target vehicle model was performed using images taken from a laboratory calibrated MWIR camera. Images taken with the calibrated camera of the same type of vehicle as the target model were compared to the synthetic images for the same conditions for validation. Target vehicle motion was added to the simulations through the use of Simulation of Urban Mobility (SUMO), DIRSIGs movement files, and custom python scripting. The output images from DIRSIG were then laced together into video. The resulting video was used to test three tracking algorithms illuminating each one’s strengths and weaknesses.

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