In this paper, a feedback training approach for efficiently dealing with distribution shift in synthetic aperture radar target detection using a Bayesian convolutional neural network is proposed. After training the network on in-distribution data, it is tested on out-of-distribution data. Samples that are classified incorrectly with high certainty are fed back for a second round of training. This results in the reduction of false positives in the out-of-distribution dataset. False positive target detections challenge human attention, sensor resource management, and mission engagement. In these types of applications, a reduction in false positives thus often takes precedence over target detection and classification performance. The classifier is used to discriminate the targets from the clutter and to classify the target type in a single step as opposed to the traditional approach of having a sequential chain of functions for target detection and localisation before the machine learning algorithm. Another aspect of automated synthetic aperture radar detection and recognition problems addressed here is the fact that human users of the output of traditional classification systems are presented with decisions made by “black box” algorithms. Consequently, the decisions are not explainable, even to an expert in the sensor domain. This paper makes use of the concept of explainable artificial intelligence via uncertainty heat maps that are overlaid onto synthetic aperture radar imagery to furnish the user with additional information about classification decisions. These uncertainty heat maps facilitate trust in the machine learning algorithm and are derived from the uncertainty estimates of the classifications from the Bayesian convolutional neural network. These uncertainty overlays further enhance the users’ ability to interpret the reasons why certain decisions were made by the algorithm. Further, it is demonstrated that feeding back the high-certainty, incorrectly classified out-of-distribution data results in an average improvement in detection performance and a reduction in uncertainty for all synthetic aperture radar images processed. Compared to the baseline method, an improvement in recall of 11.8%, and a reduction in the false positive rate of 7.08% were demonstrated using the Feedback-assisted Bayesian Convolutional Neural Network or FaBCNN.

In recent years, there has been significant developments in artificial intelligence (AI), with machine learning (ML) implementations achieving impressive performance in numerous fields. The defence capability of countries can greatly benefit from the use of ML systems for Joint Intelligence, Surveillance, and Reconnaissance (JISR). Currently, there are deficiencies in the time required to analyse large Synthetic Aperture Radar (SAR) scenes in order to gather sufficient intelligence to make tactical decisions.ML systems can assist through Automatic Target Recognition (ATR) using SAR measurements to identify potential targets. However, the advancements in ML systems have resulted in non-transparent models that lack interpretability by the human users of the system and, therefore, disqualifying the use of these algorithms in applications that affect human lives and costly property.Current Deep Machine Learning (DML) implementations applied to ATR are still non-transparent and suffer from over-confident predictions. This study addresses these limitations of DML by investigating the performance of a Bayesian Convolutional Neural Network (BCNN) when applied with the task of ATR using SAR images. In addition, the BCNN is used to perform target detection using data © © U Un ni iv ve er rs si it ty y o of f P Pr re et to or ri ia a © © U Un ni iv ve er rs si it ty y o of f P Pr re et to or ri ia a © © U Un ni iv ve er rs si it ty y o of f P Pr re et to or ri ia a SAR Synthetic Aperture Radar SOC Standard Operating Condition SVMs Support Vector Machine VHF Very High Frequency XAI eXplainable Artificial Intelligence YOLO You Only Look Once © © U Un ni iv ve er rs si it ty y o of f P Pr re et to or ri ia a © © U Un ni iv ve er rs si it ty y o of f P Pr re et to or ri ia a

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