Presented by Gladimir V. G. Baranoski
Sun, 27 Sep, 12:00 - 16:00 (UTC)
Sun, 27 Sep, 20:00 - 00:00 China Standard Time (UTC +8)
Sun, 27 Sep, 14:00 - 18:00 Central Europe Summer Time (UTC +2)
Sun, 27 Sep, 05:00 - 09:00 Pacific Daylight Time (UTC -7)
Predictive computer models, in conjunction with in situ experiments, are regularly being used by remote sensing researchers to simulate and understand the hyperspectral responses of natural materials (e.g., plants and soils), notably with respect to varying environmental stimuli (e.g., changes in light exposure and water stress). The main purpose of this tutorial is to discuss theoretical and practical issues involved in the development of predictive models of light interactions with these materials, and point out key aspects that need to be addressed to enhance their efficacy. Furthermore, since similar models are used in other scientific domains, such as biophotonics, tissue optics, imaging science and computer graphics, just to name a few, this tutorial also aims to foster the cross-fertilization with related efforts in these fields by identifying common needs and complementary resources. The presentation of this tutorial will be organized into five main sections, which are described as follows.
Section 1. This section provides the required background and terminology to be employed throughout the tutorial. It starts with an overview of the main processes involved in the interactions of light with matter. A concise review of relevant optics formulations and radiometry quantities is also provided. We also examine the key concepts of fidelity and predictability, and highlight the requirements and the benefits resulting from their incorporation in applied life sciences investigations.
Section 2. It has been long recognized that a carefully designed model is of little use without reliable data. More specifically, the effective use of a model requires material characterization data (e.g., size and water content) to be used as input, supporting data (e.g., absorption spectra of material constituents) to be used during the light transport simulations, and measured radiometric data (e.g., hyperspectral reflectance, transmittance and BSSDF (Bidirectional Surface Scattering Distribution Function)) to be used in the evaluation of modeled results. Besides their relative scarcity, most of measured radiometric datasets available in the literature often provide only a scant description of the material samples employed during the measurements, which makes the used of these datasets as references in comparisons with modeled data problematic. When it comes to a material’s constituents in their pure form, such as pigments, data scarcity is aggravated by other practical issues. For example, oftentimes their absorption spectra is estimated either through inversion procedures, which may be biased by the inaccuracies of the inverted model, or does not take into account in vivo and in vitro discrepancies. In this section, we address these issues and highlight recent efforts to mitigate them.
Section 3. For the sake of completeness and correctness, one would like to take into account all of the structural and optical characteristics of a target material during the model design stage. However, even if one is able to fully represent a material in a molecular level, as we outlined above, data may not be available to support such a detailed representation. Hence, researchers need to find an appropriate level of abstraction for the material at hand in order to balance data availability, correctness issues and application requirements. Moreover, no particular modeling design approach is superior in all cases, and regardless of the selected level of abstraction, simplifying assumptions and generalizations are usually employed in the current models due to practical constraints and the inherent complexity of natural materials. In this section, we address these issues and their impact on the efficacy of existing simulation algorithms.
Section 4. In order to claim that a model is predictive, one has to provide evidence of its fidelity, i.e., the degree to which it can reproduce the state and behaviour of a real world material in a measurable manner. This makes the evaluation stage essential to determine the predictive capabilities of a given model. In this section, we discuss different evaluation approaches, with a particular emphasis to quantitative and qualitative comparisons of model predictions with actual measured data and/or experimental observations. Although this approach is bound by data availability, it mitigates the presence of biases in the evaluation process and facilitates the identification of model parameters and algorithms that are amenable to modification and correction. In this section, we also discuss the recurrent trade-off involving the pursuit of fidelity and its impact on the performance of simulation algorithms, along with strategies employed to maximize the fidelity/cost ratio of computer intensive models.
Section 5. The development of predictive light interaction models offers several opportunities for synergistic collaborations between remote sensing and other scientific domains. For instance, predictive models can provide a robust computational platform for the “in silico” investigation of phenomena that cannot be studied through traditional “wet” experimental procedures. Eventually, these investigations can also lead to the model enhancements. In this final section, we employ case studies to examine this iterative process, which can itself contribute to accelerate the hypothesis generation and validation cycles of research in different fields. We also stress the importance of reproducibility, the cornerstone of scientific advances, and address technical and political barriers that one may need to overcome in order to establish fruitful interdisciplinary collaborations.