Fundamentals of target classification using deep learning

Abstract

In this paper we examine the application of deep learning for automated target recognition (ATR) using a shallow convolutional neural network (CNN) and infrared images from a public domain data provided by US Army Night Vision Laboratories. This study is motivated by the need for high detection and a low false alarm rate when searching for targets in sensor imagery. The goal of this study was to determine a range of optimal thresholds at which to classify an image as a target using a CNN, and an upper bound of the number of training images required for optimal performance. We used a Difference of Gaussian (DoG) kernel to localize targets by detecting the brightest patches in an image and using these patches as testing data for our network. Our CNN was successful in distinguishing between targets and clutter, and results found by our approach were favorably comparable to ground truth.