We report on observations of low level noise and modulation in natural sky light and in sunlit scenes. Data
were taken with various clear sky and partly cloudy conditions of the sky itself, as a function of altitude and
angular resolution, and of naturally illuminated scenes. Different optical and sensor configurations were
employed to explore the contributions of natural signal fluctuation to the remote sensing of vibrations
through modulation of optical and near infrared diffusely scattered light and temporal imaging of partially
resolved high contrast features. Low noise InGaAs high dynamic range photodiodes and cameras, and
silicon image sensors were used with real time and post-processing to identify the noise floor, and to
establish practical limits on light level and viewing distance in the use of these methods for remote
structural health and vibration monitoring.
We report on model predictions of angular effects of fractional intensity modulation of light that is diffusely scattered from a vibrating surface, and compare these to experimental data for a few common materials. We show the predicted and observed effects of the time dependent properties of the BRDF of the material on detected fractional modulation. We suggest a few practical commercial applications of this type of measurement.
We report on a passive imaging technique to measure physical properties of a vibrating surface using the detection of optical signal modulation in light scattered from that surface. The optical signal modulation arises from a changing surface normal and may be used to produce a surface normal change image without touching the surface and changing its state. The images may be used to extract the surface vibration frequency and mode pattern which are dependent on surface properties of the material, including its flexural modulus and mass density. Comparison of the vibration image with a finite element model may be used to infer properties of the vibrating surface, including boundary conditions. A temporal sequence of optical images of signal modulation may be analyzed to infer spatial damping properties of the surface material. Damping is a measure of energy dissipation within the material. The approach being developed has the advantage of being able to remotely image arbitrary sized structures to determine global or local vibrational properties.
This paper discusses the formulation and implementation of an acceleration approach for the MCScene code, a high
fidelity model for full optical spectrum (UV to LWIR) hyperspectral image (HSI) simulation. The MCScene simulation
is based on a Direct Simulation Monte Carlo approach for modeling 3D atmospheric radiative transport, as well as
spatially inhomogeneous surfaces including surface BRDF effects. The model includes treatment of land and ocean
surfaces, 3D terrain, 3D surface objects, and effects of finite clouds with surface shadowing. This paper will review an
acceleration algorithm that exploits spectral redundancies in hyperspectral images. In this algorithm, the full scene is
determined for a subset of spectral channels, and then this multispectral scene is unmixed into spectral end members and
end member abundance maps. Next, pure end member pixels are determined at their full hyperspectral resolution, and
the full hyperspectral scene is reconstructed from the hyperspectral end member spectra and the multispectral abundance
maps. This algorithm effectively performs a hyperspectral simulation while requiring only the computational time of a
multispectral simulation. The acceleration algorithm will be demonstrated, and errors associated with the algorithm will