Clearing the Clouds: Extracting 3D information from amongst the noise

Advancements permitting the rapid extraction of 3D point clouds from a variety of imaging modalities across the global landscape have provided a vast collection of high fidelity digital surface models. This has created a situation with unprecedented overabundance of 3D observations which greatly outstrips our current capacity to manage and infer actionable information. While years of research have removed some of the manual analysis burden for many tasks, human analysis is still a cornerstone of 3D scene exploitation. This is especially true for complex tasks which necessitate comprehension of scale, texture and contextual learning. In order to ameliorate the interpretation burden and enable scientific discovery from this volume of data, new processing paradigms are necessary to keep pace. With this context, this dissertation advances fundamental and applied research in 3D point cloud data pre-processing and deep learning from a variety of platforms. We show that the representation of 3D point data is often not ideal and sacrifices fidelity, context or scalability. First ground scanning terrestrial LIght Detection And Ranging (LiDAR) models are shown to have an inherent statistical bias, and present a state of the art method for correcting this, while preserving data fidelity and maintaining semantic structure. This technique is assessed in the dense canopy of Micronesia, with our technique being the best at retaining high levels of detail under extreme down-sampling (\u3c 1%). Airborne systems are then explored with a method which is presented to pre-process data to preserve a global contrast and semantic content in deep learners. This approach is validated with a building footprint detection task from airborne imagery captured in Eastern TN from the 3D Elevation Program (3DEP), our approach was found to achieve significant accuracy improvements over traditional techniques. Finally, topography data spanning the globe is used to assess past and previous global land cover change. Utilizing Shuttle Radar Topography Mission (SRTM) and Moderate Resolution Imaging Spectroradiometer (MODIS) data, paired with the airborne preprocessing technique described previously, a model for predicting land-cover change from topography observations is described. The culmination of these efforts have the potential to enhance the capabilities of automated 3D geospatial processing, substantially lightening the burden of analysts, with implications improving our responses to global security, disaster response, climate change, structural design and extraplanetary exploration

Measurement of cortical thickness asymmetry in carotid occlusive disease

Despite being considered an important anatomical parameter directly related to neuronal density, cortical thickness is not routinely assessed in studies of the human brain in vivo. This paucity has been largely due to the size and convoluted shape of the human cortex, which has made it difficult to develop automated algorithms that can measure cortical thickness efficiently and reliably. Since the development of such an algorithm by Fischl and Dale in 2000, the number of studies investigating the relationship between cortical thickness and other physiological parameters in the brain has been on the rise. There have been no studies however that have validated cortical asymmetry against known vascular anatomy. To this aim, using high-resolution MRI, we measured cortical thickness and volume in the primary motor (M1) and primary visual (V1) cortex in patients with unilateral, high-grade carotid occlusive disease (n = 29, age = 74 ± 10 years). These regions were selected based on the hypothesis that there will be thinning of the cortical thickness of M1 in the territory supplied by the occluded carotid artery, whereas V1 will show no asymmetry since its blood supply is provided by unaffected posterior arteries. To test for an effect of handedness, cortical thickness and volume were also measured in healthy volunteers (n = 8, age = 37 ± 13 years). In patients, we found thinner cortex in M1 on the occluded side (mean = 2.07 ± 0.19 mm vs 2.15 ± 0.20 mm, p = 0.0008) but no hemispheric difference in V1 (1.80 ± 0.17 mm in occluded vs 1.78 ± 0.16 mm in unoccluded, p = 0.31). Although the mean cortical volume of M1 in the occluded hemisphere was also lower, the difference did not reach statistical significance (p = 0.09). Similarly, in healthy controls, the results showed no hemispheric asymmetry in either cortical thickness or volume in either region (p \u3e 0.1). To test for an orientation bias in the method, the analysis was repeated with images flipped from neurological to radiological orientation. While the algorithm did not yield identical results for the two orientations, the effect did not alter the findings of the study. These results provide a method for within-subject validation of a pathophysiological effect of carotid occlusive disease on the human cortex and warrant further investigation for underlying mechanisms

Validation of Abundance Map Reference Data for Spectral Unmixing

The purpose of this study is to validate the accuracy of abundance map reference data (AMRD) for three airborne imaging spectrometer (IS) scenes. AMRD refers to reference data maps (“ground truth”) that are specifically designed to quantitatively assess the performance of spectral unmixing algorithms. While classification algorithms typically label whole pixels as belonging to certain ground cover classes, spectral unmixing allows pixels to be composed of fractions or abundances of each class. The AMRD validated in this paper were generated using our previously-proposed remotely-sensed reference data (RSRD) technique, which spatially aggregates the results of standard classification or unmixing algorithms from fine spatial-scale IS data to produce AMRD for co-located coarse-scale IS data. Validation of the three scenes was accomplished by estimating AMRD in 51 randomly-selected 10 m×10 m plots, using seven independent methods and observers. These independent estimates included field surveys by two observers, imagery analysis by two observers and RSRD by three algorithms. Results indicated statistically-significant differences between all versions of AMRD. Even AMRD from our two field surveys were significantly different for two of the four ground cover classes. These results suggest that all forms of reference data require validation prior to use in assessing the performance of classification and/or unmixing algorithms. Given the significant differences between the independent versions of AMRD, we propose that the mean of all (MOA) versions of reference data for each plot and class is most likely to represent true abundances. Our independent versions of AMRD were compared to MOA to characterize error and uncertainty. Best case results were achieved by a version of imagery analysis, which had mean coverage area differences of 2.0%, with a standard deviation of 5.6%. One of the RSRD algorithms was nearly as accurate, achieving mean differences of 3.0%, with a standard deviation of 6.3%. Further analysis of statistical equivalence yielded an overall zone of equivalence between [ − 7.0 % , 7.2 % ] for this version of RSRD. The relative accuracy of RSRD methods is promising, given their potential to efficiently generate scene-wide AMRD. These results provide the first known validated abundance level reference data for airborne IS data

Effects of annealing on electrical and optical properties of a multilayer InAs/GaAs quantum dots system

A systematic investigation of the properties of the InAs/GaAs self-assembled quantum dots (SAQDs) system subjected to a post-growth annealing using capacitance-voltage, Raman scattering and photoluminescence measurements is presented. The application of both electrical and optical methods allowed us to obtain reliable information on the microscopic structural evolution of this system. The single layer and the multilayer quantum dots were found to respond differently to the annealing process, due to the differences in strain that occur in both systems. The diffusion activated by strain provoked the appearance of an InGaAs alloy layer in substitution to the quantum dots layers; this change occurred at the annealing temperature T = 600 ºC in the multilayer system. A single dot layer, however, was observed even after the annealing at T = 700 ºC. Moreover, the low temperature annealing was found to improve the homogeneity of the multilayer system and to decrease the electrical interlayer coupling