Fused multi-sensor image mining for feature foundation data

Abstract

Presents work on methods and user interfaces developed for interactive mining for feature foundation data (e.g. roads, rivers, orchards, forests) in fused multi-sensor imagery. A suite of client/server-based tools, including the Site Mining Tool and Image Map Interface, enable image analysts (IAs) to mine multi-sensor imagery for feature foundation data and to share trainable search agents, search results and image annotations with other IAs connected via a computer network. We discuss extensions to the fuzzy ARTMAP neural network which enable the Site Mining Tool to report confidence measures for detected search targets and to automatically select the critical features in the input vector which are most relevant for particular searches. Examples of the use of the Site Mining Tool and Image Map Interface are shown for an electo-optical (EO), IR and SAR data set derived from Landsat and Radarsat imagery, as well as multispectral (4-band) and hyperspectral (224-band) data sets. In addition, we present an architecture for the enhancement of hyperspectral fused imagery that utilizes internal category activity maps of a trained fuzzy ARTMAP network to enhance the visualization of targets in the color-fused imagery.