Experimental and Theoretical Basis for a Closed-Form Spectral BRDF Model

The microfacet class of BRDF models is frequently used to calculate optical scatter from realistic surfaces using geometric optics, but has the disadvantage of not being able to consider wavelength dependence. This dissertation works toward development of a closed-form approximation to the BRDF that is suitable for hyperspectral remote sensing by presenting measured BRDF data of 12 different materials at four different incident angles and up to seven different wavelengths between 3.39 and 10.6 micrometer. The data was intended to be fit to various microfacet BRDF models to determine an appropriate form of the wavelength scaling. However, when fitting the microfacet models to measured data, the results indicated a breakdown in the microfacet model itself. To overcome this deficiency, elements of microfacet BRDF models are compared to elements of scalar wave optics BRDF models, which inherently contain a wavelength dependence. This analysis led to a theoretical understanding of how to modify microfacet BRDF models to maintain the simplicity of a closed-form model, while better approximating the underlying physics

Microwave remote sensing and its application to soil moisture detection

The author has identified the following significant results. Experimental measurements were utilized to demonstrate a procedure for estimating soil moisture, using a passive microwave sensor. The investigation showed that 1.4 GHz and 10.6 GHz can be used to estimate the average soil moisture within two depths; however, it appeared that a frequency less than 10.6 GHz would be preferable for the surface measurement. Average soil moisture within two depths would provide information on the slope of the soil moisture gradient near the surface. Measurements showed that a uniform surface roughness similar to flat tilled fields reduced the sensitivity of the microwave emission to soil moisture changes. Assuming that the surface roughness was known, the approximate soil moisture estimation accuracy at 1.4 GHz calculated for a 25% average soil moisture and an 80% degree of confidence, was +3% and -6% for a smooth bare surface, +4% and -5% for a medium rough surface, and +5.5% and -6% for a rough surface

Facial Age Analysis using an Absorptive Rough Laminar Light Scattering Model

Facial aging research concerns the way aging affects a person’s appearance and how we can use knowledge of this process. It has been an interesting topic for fields such as human perception, pattern recognition, computer vision, graphics, and skin optics. Most studies acknowledge that facial appearance changes with age. As a person grows older, certain characteristics of their skin will change, notably the light scattering. If a model is used to predict a person’s skin light scattering, its parameter(s) may be used to predict the age of its owner. The aim of this thesis is to observe whether a light scattering model parameter is suitable to be used as an age estimator/classifier. This is done by investigating and analyzing the relationship between the parameter of an analytical-based light scattering model and skins of various ages. In the end, this thesis has shown that the parameter(s) for an analytical-based light scattering model can be used as an alternative method for estimating/classifying a person’s age

CHANNEL MODELING FOR FIFTH GENERATION CELLULAR NETWORKS AND WIRELESS SENSOR NETWORKS

In view of exponential growth in data traffic demand, the wireless communications industry has aimed to increase the capacity of existing networks by 1000 times over the next 20 years. A combination of extreme cell densification, more bandwidth, and higher spectral efficiency is needed to support the data traffic requirements for fifth generation (5G) cellular communications. In this research, the potential improvements achieved by using three major 5G enabling technologies (i.e., small cells, millimeter-wave spectrum, and massive MIMO) in rural and urban environments are investigated. This work develops SPM and KA-based ray models to investigate the impact of geometrical parameters on terrain-based multiuser MIMO channel characteristic. Moreover, a new directional 3D channel model is developed for urban millimeter-wave (mmW) small cells. Path-loss, spatial correlation, coverage distance, and coherence length are studied in urban areas. Exploiting physical optics (PO) and geometric optics (GO) solutions, closed form expressions are derived for spatial correlation. Achievable spatial diversity is evaluated using horizontal and vertical linear arrays as well as planar 2D arrays. In another study, a versatile near-ground field prediction model is proposed to facilitate accurate wireless sensor network (WSN) simulations. Monte Carlo simulations are used to investigate the effects of antenna height, frequency of operation, polarization, and terrain dielectric and roughness properties on WSNs performance

S-193 scatterometer backscattering cross section precision/accuracy for Skylab 2 and 3 missions

Procedures for measuring the precision and accuracy with which the S-193 scatterometer measured the background cross section of ground scenes are described. Homogeneous ground sites were selected, and data from Skylab missions were analyzed. The precision was expressed as the standard deviation of the scatterometer-acquired backscattering cross section. In special cases, inference of the precision of measurement was made by considering the total range from the maximum to minimum of the backscatter measurements within a data segment, rather than the standard deviation. For Skylab 2 and 3 missions a precision better than 1.5 dB is indicated. This procedure indicates an accuracy of better than 3 dB for the Skylab 2 and 3 missions. The estimates of precision and accuracy given in this report are for backscattering cross sections from -28 to 18 dB. Outside this range the precision and accuracy decrease significantly

Data Driven Investigation into the Off-Axis BRDF to Develop an Algorithm to Classify Anisotropicity

The Bi-directional Reflectance Distribution Function (BRDF) is used to describe reflectances of materials by calculating the ratio of the reflected radiance to the incident irradiance. While it was found that isotropic BRDF microfacet models maintained symmetry about ɸs = π, such symmetry was not maintained about the θs = θi axis, except for close to the specular peak. This led to development of a novel data-driven metric for how isotropic a BRDF measurement is. Research efforts centered around developing an algorithm that could determine material anisotropy without having to fit to models. The algorithm developed here successfully classified the degree of anisotropicity in 4 out of 5 samples

Quantitative Estimation of Surface Soil Moisture in Agricultural Landscapes using Spaceborne Synthetic Aperture Radar Imaging at Different Frequencies and Polarizations

Soil moisture and its distribution in space and time plays an important role in the surface energy balance at the soil-atmosphere interface. It is a key variable influencing the partitioning of solar energy into latent and sensible heat flux as well as the partitioning of precipitation into runoff and percolation. Due to their large spatial variability, estimation of spatial patterns of soil moisture from field measurements is difficult and not feasible for large scale analyses. In the past decades, Synthetic Aperture Radar (SAR) remote sensing has proven its potential to quantitatively estimate near surface soil moisture at high spatial resolutions. Since the knowledge of the basic SAR concepts is important to understand the impact of different natural terrain features on the quantitative estimation of soil moisture and other surface parameters, the fundamental principles of synthetic aperture radar imaging are discussed. Also the two spaceborne SAR missions whose data was used in this study, the ENVISAT of the European Space Agency (ESA) and the ALOS of the Japanese Aerospace Exploration Agency (JAXA), are introduced. Subsequently, the two essential surface properties in the field of radar remote sensing, surface soil moisture and surface roughness are defined, and the established methods of their measurement are described. The in situ data used in this study, as well as the research area, the River Rur catchment, with the individual test sites where the data was collected between 2007 and 2010, are specified. On this basis, the important scattering theories in radar polarimetry are discussed and their application is demonstrated using novel polarimetric ALOS/PALSAR data. A critical review of different classical approaches to invert soil moisture from SAR imaging is provided. Five prevalent models have been chosen with the aim to provide an overview of the evolution of ideas and techniques in the field of soil moisture estimation from active microwave data. As the core of this work, a new semi-empirical model for the inversion of surface soil moisture from dual polarimetric L-band SAR data is introduced. This novel approach utilizes advanced polarimetric decomposition techniques to correct for the disturbing effects from surface roughness and vegetation on the soil moisture retrieval without the use of a priori knowledge. The land use specific algorithms for bare soil, grassland, sugar beet, and winter wheat allow quantitative estimations with accuracies in the order of 4 Vol.-%. Application of remotely sensed soil moisture patterns is demonstrated on the basis of mesoscale SAR data by investigating the variability of soil moisture patterns at different spatial scales ranging from field scale to catchment scale. The results show that the variability of surface soil moisture decreases with increasing wetness states at all scales. Finally, the conclusions from this dissertational research are summarized and future perspectives on how to extend the proposed model by means of improved ground based measurements and upcoming advances in sensor technology are discussed. The results obtained in this thesis lead to the conclusion that state-of-the-art spaceborne dual polarimetric L-band SAR systems are not only suitable to accurately retrieve surface soil moisture contents of bare as well as of vegetated agricultural fields and grassland, but for the first time also allow investigating within-field spatial heterogeneities from space