Within urban environments and other complex illumination conditions, tilted target surface or partial sky occlusions from nearby raised objects can have a significant impact on a target’s radiance observed from a spectral sensor. At the pixel level, these terms can be predicted and corrected for by modeling the impact collocated height data has on the scene radiometry. After properly accounting for these impacts, a Lambertian material’s retrieved spectral reflectivity should be the same at any location or orientation within the scene. This paper proposes a novel approach for using this constraint to iterate on atmospheric aerosol parameters until the difference of retrieved spectral reflectance of two pixels of the same material, but under different illumination conditions, is minimized.
The ARTEMIS hyperspectral sensor will be the first spaceborne hyperspectral sensor with an on-board real-time
processing capability. The ARTEMIS real-time processor utilizes both anomaly and material detection algorithms to
locate materials of potential interest. To satisfy the real-time processing timelines, the collected data must be reduced
from hundreds of bands to around 64 bins, where a bin can be a single band or the average of a set of bands. A signature
optimization study was conducted to compare various binning algorithms through the analysis of both the detection
characteristics and the discrimination performance before and after spectral binning.