Monte Carlo Simulation of Radiative Transfer in Atmospheric Environments for Problems Arising from Remote Sensing Measurements

Premuda, Margherita

doi:10.5772/15556

Cited by 2 publications

(1 citation statement)

References 47 publications

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“…However, the complexity and accuracy of samples generated by the model have limited the utility of the simulation result, which makes recovery of scene characteristics and properties challenging. [5][6][7] The Digital Imaging and Remote Sensing Image Generation (DIRSIG) model, is one such a first-principles, physicsbased tool for imagery simulation, and was developed by the Digital Imaging and Remote Sensing Laboratory (DIRS) at Rochester Institute of Technology (RIT). The model can produce passive single-band, multi-spectral or imaging spectroscopy (hyperspectral) imagery from the visible through the thermal infrared regions of the electromagnetic spectrum 8 .…”

Section: Introductionmentioning

confidence: 99%

On validating remote sensing simulations using coincident real data

et al. 2016

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The remote sensing community often requires data simulation, either via spectral/spatial downsampling or through virtual, physics-based models, to assess systems and algorithms. The Digital Imaging and Remote Sensing Image Generation (DIRSIG) model is one such first-principles, physics-based model for simulating imagery for a range of modalities. Complex simulation of vegetation environments subsequently has become possible, as scene rendering technology and software advanced. This in turn has created questions related to the validity of such complex models, with potential multiple scattering, bidirectional distribution function (BRDF), etc. phenomena that could impact results in the case of complex vegetation scenes.We selected three sites, located in the Pacific Southwest domain (Fresno, CA) of the National Ecological Observatory Network (NEON). These sites represent oak savanna, hardwood forests, and conifer-manzanita-mixed forests. We constructed corresponding virtual scenes, using airborne LiDAR and imaging spectroscopy data from NEON, ground-based LiDAR data, and field-collected spectra to characterize the scenes. Imaging spectroscopy data for these virtual sites then were generated using the DIRSIG simulation environment. This simulated imagery was compared to real AVIRIS imagery (15m spatial resolution; 12 pixels/scene) and NEON Airborne Observation Platform (AOP) data (1m spatial resolution; 180 pixels/scene). These tests were performed using a distribution-comparison approach for select spectral statistics, e.g., established the spectra's shape, for each simulated versus real distribution pair. The initial comparison results of the spectral distributions indicated that the shapes of spectra between the virtual and real sites were closely matched.

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