Infrared sensors are widely utilized on manned and unmanned systems due to their ability to operate during low light conditions as well as their target discrimination capability. Machine vision algorithms that operate on infrared imagery (e.g. target detection, obstacle detection, target tracking) can significantly increase the effectiveness of platforms and the autonomy of unmanned systems. The classification of regions in infrared imagery provides a valuable input to computer vision algorithms. This paper contains an analysis of features for infrared region discrimination, feature dimensionality reduction, and classification for regions of infrared imagery. A variety of features are considered including those based on texture and a physics based model for atmospheric attenuation. An analysis of the optimal feature set and classifier combination is presented. Performance of the classifier on a database of infrared imagery is provided as well as top level contextual techniques to improve classification performance.
['Whit Blanton', 'Kenneth E. Barner']
Infrared region classification using texture and model-based features