An Approach to Retrieve BRDF from Satellite and Airborne Measurements of Surface-Reflected Radiance Based on Decoupling of Atmospheric Radiative Transfer and Surface Reflection

Bi-directional Reflection Distribution Function (BRDF) defines anisotropy of the surface reflection. It is required to specify the boundary condition for radiative transfer (RT) modeling. Measurements of reflected radiance by satellite- and air-borne sensors provide information about anisotropy of surface reflection. Atmospheric correction needs to be performed to derive BRDF from the reflected radiance. Common approach for BRDF retrievals consists of the use of kernel-based BRDF and RT modeling that needs to be done anew at every step of the iterative process. The kernels weights are obtained by minimization of the difference between measured and modeled radiance. This study develops a new method of retrieving kernel-based BRDF that requires RT calculations to be done only once. The method employs the exact analytical expression of radiance at any atmospheric level through the solutions of two auxiliary atmosphere-only RT problems and the surface-reflected radiance at the surface level. The latter is related to BRDF and solutions of the auxiliary RT problems by a Fredholm integral equation of the second kind. The approach requires to perform RT calculations one time before the iterations. It can use observations taken at different atmospheric conditions assuming that surface conditions remain unchanged during the time span of observations. The algorithm accurately catches zero weights of the kernels that may be a concern if the number of kernels is greater than 3 in current mainstream approaches. The study presents numerical tests of the BRDF retrieval algorithm for various surface and atmospheric conditions

An intuitive control space for material appearance

Many different techniques for measuring material appearance have been proposed in the last few years. These have produced large public datasets, which have been used for accurate, data-driven appearance modeling. However, although these datasets have allowed us to reach an unprecedented level of realism in visual appearance, editing the captured data remains a challenge. In this paper, we present an intuitive control space for predictable editing of captured BRDF data, which allows for artistic creation of plausible novel material appearances, bypassing the difficulty of acquiring novel samples. We first synthesize novel materials, extending the existing MERL dataset up to 400 mathematically valid BRDFs. We then design a large-scale experiment, gathering 56,000 subjective ratings on the high-level perceptual attributes that best describe our extended dataset of materials. Using these ratings, we build and train networks of radial basis functions to act as functionals mapping the perceptual attributes to an underlying PCA-based representation of BRDFs. We show that our functionals are excellent predictors of the perceived attributes of appearance. Our control space enables many applications, including intuitive material editing of a wide range of visual properties, guidance for gamut mapping, analysis of the correlation between perceptual attributes, or novel appearance similarity metrics. Moreover, our methodology can be used to derive functionals applicable to classic analytic BRDF representations. We release our code and dataset publicly, in order to support and encourage further research in this direction

Perceptual Modeling and Reproduction of Gloss

The reproduction of gloss on displays is generally not based on perception and as a consequence does not guarantee the best visualization of a real material. The reproduction is composed of four different steps: measurement, modeling, rendering, and display. The minimum number of measurements required to approximate a real material is unknown. The error metrics used to approximate measurements with analytical BRDF models are not based on perception, and the best visual approximation is not always obtained. Finally, the gloss perception difference between real objects and objects seen on displays has not sufficiently been studied and might be influencing the observer judgement. This thesis proposes a systematic, scalable, and perceptually based workflow to represent real materials on displays. First, the gloss perception difference between real objects and objects seen on displays was studied. Second, the perceptual performance of the error metrics currently in use was evaluated. Third, a projection into a perceptual gloss space was defined, enabling the computation of a perceptual gloss distance measure. Fourth, the uniformity of the gloss space was improved by defining a new gloss difference equation. Finally, a systematic, scalable, and perceptually based workflow was defined using cost-effective instruments

Modeling polarimetric imaging using DIRSIG

The remote sensing community is beginning to recognize the potential benefit of exploiting polarimetric signatures. The ability to accurately model polarimetric phenomenology in a remote sensing system will assist efforts in system design, algorithm development, phenomenology studies, and analyst training. This dissertation lays the ground work for enhancing the current Digital Imaging and Remote Sens ing Laboratory\u27s Synthetic Image Generation (DIRSIG) model to include polarimetric phenomenology. The current modeling capabilities are discussed along with the theoretical background required to expand upon the current state of the art. Methods for modeling and estimating polarimetric signatures and phenomenology from start to end in a typical remote sensing system are presented. A series of simple simulations were conducted to assess the performance of the new polarimetric capabilities. Analysis was performed to characterize the individual models and the collected performance of the models

Intuitive and Accurate Material Appearance Design and Editing

Creating and editing high-quality materials for photorealistic rendering can be a difficult task due to the diversity and complexity of material appearance. Material design is the process by which artists specify the reflectance properties of a surface, such as its diffuse color and specular roughness. Even with the support of commercial software packages, material design can be a time-consuming trial-and-error task due to the counter-intuitive nature of the complex reflectance models. Moreover, many material design tasks require the physical realization of virtually designed materials as the final step, which makes the process even more challenging due to rendering artifacts and the limitations of fabrication. In this dissertation, we propose a series of studies and novel techniques to improve the intuitiveness and accuracy of material design and editing. Our goal is to understand how humans visually perceive materials, simplify user interaction in the design process and, and improve the accuracy of the physical fabrication of designs. Our first work focuses on understanding the perceptual dimensions for measured material data. We build a perceptual space based on a low-dimensional reflectance manifold that is computed from crowd-sourced data using a multi-dimensional scaling model. Our analysis shows the proposed perceptual space is consistent with the physical interpretation of the measured data. We also put forward a new material editing interface that takes advantage of the proposed perceptual space. We visualize each dimension of the manifold to help users understand how it changes the material appearance. Our second work investigates the relationship between translucency and glossiness in material perception. We conduct two human subject studies to test if subsurface scattering impacts gloss perception and examine how the shape of an object influences this perception. Based on our results, we discuss why it is necessary to include transparent and translucent media for future research in gloss perception and material design. Our third work addresses user interaction in the material design system. We present a novel Augmented Reality (AR) material design prototype, which allows users to visualize their designs against a real environment and lighting. We believe introducing AR technology can make the design process more intuitive and improve the authenticity of the results for both novice and experienced users. To test this assumption, we conduct a user study to compare our prototype with the traditional material design system with gray-scale background and synthetic lighting. The results demonstrate that with the help of AR techniques, users perform better in terms of objectively measured accuracy and time and they are subjectively more satisfied with their results. Finally, our last work turns to the challenge presented by the physical realization of designed materials. We propose a learning-based solution to map the virtually designed appearance to a meso-scale geometry that can be easily fabricated. Essentially, this is a fitting problem, but compared with previous solutions, our method can provide the fabrication recipe with higher reconstruction accuracy for a large fitting gamut. We demonstrate the efficacy of our solution by comparing the reconstructions with existing solutions and comparing fabrication results with the original design. We also provide an application of bi-scale material editing using the proposed method

Empirical Measurement and Model Validation of Infrared Spectra of Contaminated Surfaces

The goal of this thesis was to validate predicted infrared spectra of liquid contaminated surfaces from a micro-scale bi-directional reflectance distribution function (BRDF) model through the use of empirical measurement. Liquid contaminated surfaces generally require more sophisticated radiometric modeling to numerically describe surface properties. The Digital Image and Remote Sensing Image Generation (DIRSIG) model utilizes radiative transfer modeling to generate synthetic imagery for a variety of applications. Aside from DIRSIG, a micro-scale model known as microDIRSIG has been developed as a rigorous ray tracing physics-based model that could predict the BRDF of geometric surfaces that are defined as micron to millimeter resolution facets. The model offers an extension from the conventional BRDF models by allowing contaminants to be added as geometric objects to a micro-facet surface. This model was validated through the use of Fourier transform infrared spectrometer measurements. A total of 18 different substrate and contaminant combinations were measured and compared against modeled outputs. The substrates used in this experiment were wood and aluminum that contained three different paint finishes. The paint finishes included no paint, Krylon ultra-flat black, and Krylon glossy black. A silicon based oil (SF96) was measured out and applied to each surface to create three different contamination cases for each surface. Radiance in the longwave infrared region of the electromagnetic spectrum was measured by a Design and Prototypes (D\&P) Fourier transform infrared spectrometer and a Physical Sciences Inc. Adaptive Infrared Imaging Spectroradiometer (AIRIS). The model outputs were compared against the measurements quantitatively in both the emissivity and radiance domains. A temperature emissivity separation (TES) algorithm had to be applied to the measured radiance spectra for comparison with the microDIRSIG predicted emissivity spectra. The model predicted emissivity spectra was also forward modeled through a DIRSIG simulation for comparisons to the radiance measurements. The results showed a promising agreement for homogeneous surfaces with liquid contamination that could be well characterized geometrically. Limitations arose in substrates that were modeled as homogeneous surfaces, but had spatially varying artifacts due to uncertainties with contaminant and surface interactions. There is high desire for accurate physics based modeling of liquid contaminated surfaces and this validation framework may be extended to include a wider array of samples for more realistic natural surfaces that are often found in real world scenarios

A Qualitative and Quantitative Evaluation of 8 Clear Sky Models

We provide a qualitative and quantitative evaluation of 8 clear sky models used in Computer Graphics. We compare the models with each other as well as with measurements and with a reference model from the physics community. After a short summary of the physics of the problem, we present the measurements and the reference model, and how we "invert" it to get the model parameters. We then give an overview of each CG model, and detail its scope, its algorithmic complexity, and its results using the same parameters as in the reference model. We also compare the models with a perceptual study. Our quantitative results confirm that the less simplifications and approximations are used to solve the physical equations, the more accurate are the results. We conclude with a discussion of the advantages and drawbacks of each model, and how to further improve their accuracy

Estimation of Land Surface Albedo from GCOM-C/SGLI Surface Reflectance

XXIV ISPRS Congress “Imaging today, foreseeing tomorrow, ” Commission III2021 edition, 5–9 July 2021This paper examines algorithms for estimating terrestrial albedo from the products of the Global Change Observation Mission – Climate (GCOM-C)/Second-generation Global Imager (SGLI), which was launched in December 2017 by the Japan Aerospace Exploration Agency. We selected two algorithms: one based on a bidirectional reflectance distribution function (BRDF) model and one based on multi-regression models. The former determines kernel-driven BRDF model parameters from multiple sets of reflectance and estimates the land surface albedo from those parameters. The latter estimates the land surface albedo from a single set of reflectance with multi-regression models. The multi-regression models are derived for an arbitrary geometry from datasets of simulated albedo and multi-angular reflectance. In experiments using in situ multi-temporal data for barren land, deciduous broadleaf forests, and paddy fields, the albedos estimated by the BRDF-based and multi-regression-based algorithms achieve reasonable root-mean-square errors. However, the latter algorithm requires information about the land cover of the pixel of interest, and the variance of its estimated albedo is sensitive to the observation geometry. We therefore conclude that the BRDF-based algorithm is more robust and can be applied to SGLI operational albedo products for various applications, including climate-change research

SeaWiFS technical report series. Volume 10: Modeling of the SeaWiFS solar and lunar observations

Post-launch stability monitoring of the Sea-viewing Wide Field-of-view Sensor (SeaWifs) will include periodic sweeps of both an onboard solar diffuser plate and the moon. The diffuser views will provide short-term checks and the lunar views will monitor long-term trends in the instrument's radiometric stability. Models of the expected sensor response to these observations were created on the SeaWiFS computer at the National Aeronautics and Space Administration's (NASA) Goddard Space Flight Center (GSFC) using the Interactive Data Language (IDL) utility with a graphical user interface (GUI). The solar model uses the area of intersecting circles to simulate the ramping of sensor response while viewing the diffuser. This model is compared with preflight laboratory scans of the solar diffuser. The lunar model reads a high-resolution lunar image as input. The observations of the moon are simulated with a bright target recovery algorithm that includes ramping and ringing functions. Tests using the lunar model indicate that the integrated radiance of the entire lunar surface provides a more stable quantity than the mean of radiances from centralized pixels. The lunar model is compared to ground-based scans by the SeaWiFS instrument of a full moon in December 1992. Quality assurance and trend analyses routines for calibration and for telemetry data are also discussed