Automatic target recognition (ATR) systems, like human photo-interpreters, rely on a variety of
visual information for detecting, classifying, and identifying manmade objects in aerial imagery.
We describe the integration of a visual learning component into the Image Data Conditioner
(IDC) for target/clutter and other visual classification tasks. The component is based on an
implementation of a model of the visual cortex developed by Serre, Wolf, and Poggio. Visual
learning in an ATR context requires the ability to recognize objects independent of location,
scale, and rotation. Our method uses IDC to extract, rotate, and scale image chips at candidate
target locations. A bootstrap learning method effectively extends the operation of the classifier
beyond the training set and provides a measure of confidence. We show how the classifier can
be used to learn other features that are difficult to compute from imagery such as target
direction, and to assess the performance of the visual learning process itself.