4.7 Article

LESS: LargE-Scale remote sensing data and image simulation framework over heterogeneous 3D scenes

Journal

REMOTE SENSING OF ENVIRONMENT
Volume 221, Issue -, Pages 695-706

Publisher

ELSEVIER SCIENCE INC
DOI: 10.1016/j.rse.2018.11.036

Keywords

Image simulation; Radiative transfer; Landscape modeling

Funding

  1. NSFC [41571341, 41331171]
  2. National Research Foundation (NRF) Singapore through the Singapore-MIT Alliance for Research and Technology's Centre for Environmental Sensing and Modeling (SMART-CENSAM) interdisciplinary research program
  3. Centre National d'Etudes Spatiales (CNES)

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Three-dimensional (3D) radiative transfer modeling of the transport and interaction of radiation through earth surfaces is challenging due to the complexity of the landscapes as well as the intensive computational cost of 3D radiative transfer simulations. To reduce computation time, current models work with schematic landscapes or with small-scale realistic scenes. The computer graphics community provides the most accurate and efficient models (known as renderers) but they were not designed specifically for performing scientific radiative transfer simulations. In this study, we propose LESS, a new 3D radiative transfer modeling framework. LESS employs a weighted forward photon tracing method to simulate multispectral bidirectional reflectance factor (BRF) or flux-related data (e.g., downwelling radiation) and a backward path tracing method to generate sensor images (e.g., fisheye images) or large-scale (e.g. 1 km(2)) spectral images. The backward path tracing also has been extended to simulate thermal infrared radiation by using an on-the-fly computation of the sunlit and shaded scene components. This framework is achieved through the development of a user-friendly graphic user interface (GUI) and a set of tools to help construct the landscape and set parameters. The accuracy of LESS is evaluated with other models as well as field measurements in terms of directional BREs and pixel-wise simulated image comparisons, which shows very good agreement. LESS has the potential in simulating datasets of realistically reconstructed landscapes. Such simulated datasets can be used as benchmarks for various applications in remote sensing, forestry investigation and photogrammetry.

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