Training decision trees to guide feature selection for infrared image pre-screening algorithms

Abstract

This research explores a fresh approach to the selection and weighting of classical image features for infrared object detection and target-like clutter rejection. Traditional statistical techniques are used to calculate individual features, while modern supervised machine learning techniques are used to rank-order the predictivevalue of each feature. This paper describes the use of Decision Trees to determine which features have the highest value in prediction of the correct binary target/non-target class. This work is unique in that it is focused on infrared imagery and exploits interpretable machine learning techniques for the selection of hand-crafted features integrated into a pre-screening algorithm.