Improved Explainability through Uncertainty Estimation in Automatic Target Recognition of SAR Images

Abstract

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

Explainable, Physics-Aware, Trustworthy Artificial Intelligence: A paradigm shift for synthetic aperture radar

“…Bayesian deep neural network, instead, is able to calibrate the output score and measure the uncertainty of the prediction. Some recent studies applied Bayesian deep learning for SAR sea-ice segmentation [60]- [62], as well as target discrimination [63]. The generated uncertainty map can serve as a guideline for the experts in annotation and improve trust between users and the model.…”