Deep CNN-based Feature Extractor for Target Recognition in Thermal Images

Target recognition in thermal infrared images is challenging due to high variability of target IR signature and competing background IR signature due to a number of environmental and target parameters. Traditional hand-crafted feature extractors are limited by these challenges. Recently, deep learning has shown promising success for a number of computer vision works. In this paper, deep CNN-based feature extraction is explored for target recognition in thermal images. In this study, two pre-trained CNNs, AlexNet and VGG19 are considered. A number of deep CNN-based feature extractors are evaluated by extracting features from different layers of the network. The results indicate the robustness of the deep CNN-based feature extractor. The VGG19_fc6 architecture has demonstrated superior performance with 6% improvement in the classification accuracy against the WignerMSER based state of the art target recognition on two class FLIR thermal infrared dataset.