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

Aparna Akula, Harish Kumar Sardana

Abstract: 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.