Raytheon Company

Paul M Ingram, Jr

Joseph C Landry

John J Coogan

Paul D Shocklee

Archie Henry Muse

In one example of a method for remote identifying a non-Lambertian target material, a spectral signature for a target is determined from each of at least two different sets of imagery acquired at different angles, and compared to a predicted signature for a candidate material for each of the at least two different angles. The predicted signatures take into account the known anisotropy of reflectance, and thus also radiance, of the candidate material.

Remote identification of non-lambertian materials

A computer implemented method and apparatus estimate background reflectance, aerosol type and visibility within a multispectral imagery using the measured spectral radiance of one or more calibration targets of known reflectance and the measured radiance of the background of the target. The computer implemented method and apparatus uses predefined aerosol types, characterized by a plurality of known parameters, and the known reflectance of the one or more calibration targets, to select an aerosol that best matches the measured radiances.

In-scene determination of aerosol parameters from imagery

In accordance with the present disclosure, a computer implemented system and method predicts the performance for a remote material identification process under real conditions and uncertainties. The method and system transforms data representing measured reflectance values for candidate materials based on environmental conditions, and uncertainties regarding the environmental conditions and/or calibration of sensors measuring radiance values into the performance predictions for a material identification process operating under those conditions and uncertainties. The performance predictions can be communicated to a designer of, for example, a multi-angle material identification system for use in selecting and setting up the system, or communicated to a consumer of images captured by the material identification system for use in interpreting results of application of the material identification process to real imagery acquired with remote sensors.

Remote material identification process performance prediction tool

A method for estimating the error statistic for retrieved temperature and emissivity of a surface material includes determining the second order analytical error propagation from a measured radiance that differs from the true radiance by additive gaussian noise, which is independent in each band. The radiance error is translated into a diagonal covariance matrix and an analytical estimate results in a determination of the standard deviation and bias of surface temperature. Further, the method for estimating the error statistic utilizes Monte Carlo simulation from a sufficiently large ensemble of radiance spectra for the retrieved surface temperature and emissivity. Temperature and emissivity of the surface material were retrieved using ISSTES algorithm.

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Dallas, TX, United States of America

Paul M Ingram, Jr