Improved ATR performance using boosting and transfer learning for adaptation of a target detection network

In this paper, we present preliminary results of infra-red target detection using a target-to-clutter based deep learning network (TCRnet). We augment this network with a separate processing path to render a new Directed Acyclic Graph network (TCRDAG) amenable to transfer learning. This transfer learning is used for network adaptation to new observations. The ROC curve shows significant improvement, particularly at the right side of the ROC curve. We further explore a boosting paradigm to improve the ROC curve for the left side. We then present results on a publicly available MWIR dataset released by NVESD.