Improved explainability through uncertainty estimation in automatic target recognition of SAR images

• Main Author: Blomerus, Nicholas Daniel
• Other Authors: De Villiers, Johan Pieter, Cilliers, Jacques E., Nel, Willie
• Format: Doctoral or Postdoctoral Thesis
• Language: English
• Published: University of Pretoria 2022
• Subjects: Automatic Target Recognition; Synthetic Aperture Radar; Explainable Artificial Intelligence; Bayesian Neural Network; UCTD; DML
• Online Access: https://repository.up.ac.za/handle/2263/86502; https://doi.org/10.25403/UPresearchdata.20382900

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 provided by the Council for Scientific and Industrial Research (CSIR). To improve interpretability, a method is proposed that utilises the epistemic uncertainty of the BCNN detector to visualise high- or low-confidence regions in each of the SAR scenes. The results of this research showed that the performance of the BCNN in the task of ATR using SAR images is comparable to current DML methods from literature. The BCNN achieves a classification accuracy of 93.1 % which is marginally lower than the performance of a similar Convolutional Neural Network of 96.8 %. The BCNN outperformed the CNN when the networks were given out-ofdistribution data. The CNN outputs showed over-confident predictions while the BCNN was able to indicate its lack of confidence by ...