Multi-Sensor Scene Modeling Using Statistical Models for Bidirectional Texture Functions

This paper presents a novel approach to multi-sensor statistical modeling of bi-directional texture functions (BTF). Our proposed BTF modeling approach is based on (1) conducting an analytical study that relates a sensor resolution to the size and shape of elements forming material surface, (2) developing a robotic system for laboratory BTF data acquisition, (3) researching an application of the Johnson family of statistical probability distribution functions (PDF) to BTF modeling, (4) selecting optimal feature space for statistical BTF modeling, (5) building a database of parameters for the Johnson family of PDFs that after interpolations forms a high-dimensional statistical BTF model and (6) researching several statistical quality metrics that can be used for verification and validation of the obtained BTF models. The motivation for developing the proposed statistical BTF modeling approach comes from the facts that (a) analytical models have to incorporate randomness of outdoor scene clutter surfaces and (b) models have to be computationally feasible with respect to the complexity of modeled interactions between light and materials. The major advantages of our approach over other approaches are (a) the low computational requirements on BTF modeling (BTF model storage, fast BTF model-based generation), (b) flexibility of the Johnson family of PDFs to cover a wide range of PDF shapes and (c) applicability of the BTF model to a wide range of spectral sensors, e.g., color, multi-spectral or hyperspectral cameras. The prime applications for the proposed BTF model are multi-sensor automatic target recognition (ATR), and scene understanding and simulation.