Automatic Target Recognition of Cluttered FLIR Imagery Using Multistage Feature Extraction and Feature Repair

摘要

Automatic target recognition using forward-looking infrared imagery is a challenging problem because of the highly unpredictable nature of target thermal signatures. The high variability of target signatures, target obscuration, and clutter in the background results in distortion of target features, which are used by the target detection stage to identify a potential target. Consequently, the target detection stage produces a large number of false alarms. Distorted features h the potential targets also make accurate classification of targets difficult. The proposed technique, in essence attempts to repair the distorted features of~1 the targets to improve the target detection/classification accuracy. The proposed technique completes the feature extraction process in two steps: First, the feature vectors are extracted and classified either as complete or incomplete features using feed-forward neural networks. The incomplete features are then transformed into complete features. These features can then be used to identify/classify the targets.