Multispectral imagery (MSI) provides information to support decision making across a growing number of private and
industrial applications. Among them, land mapping, terrain classification and feature extraction rank highly in the
interest of those who analyze the data to produce information, reports, and intelligence products. The 8 nominal band
centers of WorldView-2 allow us to use non-traditional means of measuring the differences which exist in the features,
artifacts, and surface materials in the data, and we can determine the most effective method for processing this
information by exploiting the unique response values within those wavelength channels. The difference in responses
across select bands can be sought using normalized difference index ratios to measure moisture content, indicate
vegetation health, and distinguish natural features from man-made objects. The focus of this effort is to develop an
approach to measure, identify and threshold these differences in order to establish an effective land mapping and feature
extraction process germane to WorldView-2 imagery.
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.