Automated Feature Extraction from Hyperspectral Imagery, Phase I

In response to NASA Topic S7.01, Visual Learning Systems, Inc. (VLS) will develop a novel hyperspectral plug-in toolkit for its award winning Feature Analyst^REG software that will (a) leverage VLS' proven algorithms to provide a new, simple, and long-awaited approach to materials classification from hyperspectral imagery (HSI), and (b) improve state-of-the-art Feature Analyst's automated feature extraction (AFE) capabilities by effectively incorporating detailed spectral information into its extraction process. HSI techniques, such as spectral end-member classification, can provide effective materials classification; however, current methods are slow (or manual), cumbersome, complex for analysts, and are limited to materials classification only. Feature Analyst, on the other hand has a simple workflow of (a) an analyst providing a few examples (e.g., pixels of a certain material) and (b) an advanced software agent classifying the rest of the imagery based on the examples. This simple yet powerful approach will be used as a new paradigm for materials classification. In addition, Feature Analyst uses, along with spectral information, feature characteristics such as spatial association, size, shape, texture, pattern, and shadow in its generic AFE process. Incorporating the best spectral classifier techniques with the best AFE approach promises to greatly increase the usefulness and applicability of HSI More »