An Integrated Approach to Hyperspectral Feature Extraction

This paper presents a novel hyperspectral feature-extraction toolkit that provides a simple, automated, and accurate approach to materials classification from hyperspectral imagery (HSI). The proposed toolkit is built as an extension to the state-of-the-art technology in automated feature extraction, the Feature Analyst software suite. Feature Analyst uses, along with spectral information, feature characteristics such as spatial association, size, shape, texture, pattern, and shadow in its generic feature extraction process. While current HSI techniques, such as spectral endmember classification, can provide effective materials classification, these 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, and (b) an advanced software agent classifying the rest of the imagery; however, Feature Analyst does not have effective pre-processing approaches for handling numerous image bands found in HSI. The approach presented in this paper integrates the best of traditional HSI processing with the Feature Analyst approach to produce a powerful new approach that promises to become the new paradigm for HSI materials classification. Experiments presented in this paper show the new approach is (a) accurate, (b) simple, (c) advanced, and (d) exists as a workflow extension to market leading products, such as ArcGIS and ERDAS IMAGINE.