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Improved explainability through uncertainty estimation in automatic target recognition of SAR images
Dissertation (MEng (Electronic Engineering))--University of Pretoria, 2021.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2022
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| _version_ | 1867613488320348160 |
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| access_status_str | Open Access |
| author2 | De Villiers, Johan Pieter |
| author_browse | De Villiers, Johan Pieter |
| author_facet | De Villiers, Johan Pieter |
| collection | Thesis |
| dc_rights_str_mv | © 2022 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Dissertation (MEng (Electronic Engineering))--University of Pretoria, 2021. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/86502 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:36:56.516Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/86502 Improved explainability through uncertainty estimation in automatic target recognition of SAR images De Villiers, Johan Pieter nicholas.blomerus@gmail.com Cilliers, Jacques E. Nel, Willie Blomerus, Nicholas Daniel Automatic Target Recognition Synthetic Aperture Radar Explainable Artificial Intelligence Bayesian Neural Network UCTD Dissertation (MEng (Electronic Engineering))--University of Pretoria, 2021. 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 using the epistemic uncertainty in combination with the predictive variance in its output. Using the dataset from the CSIR, uncertainty heat maps were generated that illustrated regions of highand low-confidence. The regions with the highest uncertainty were located near large collections of trees and areas near shadows. The high-uncertainty incorrect predictions were fed back into the BCNN, and results showed a reduction in overall uncertainty and detection performance. Electrical, Electronic and Computer Engineering MEng (Electronic Engineering) Unrestricted 2022-07-27T13:36:00Z 2022-07-27T13:36:00Z 2022-09-07 2021 Dissertation * https://repository.up.ac.za/handle/2263/86502 10.25403/UPresearchdata.20382900 en © 2022 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | Automatic Target Recognition Synthetic Aperture Radar Explainable Artificial Intelligence Bayesian Neural Network UCTD Improved explainability through uncertainty estimation in automatic target recognition of SAR images |
| title | Improved explainability through uncertainty estimation in automatic target recognition of SAR images |
| title_full | Improved explainability through uncertainty estimation in automatic target recognition of SAR images |
| title_fullStr | Improved explainability through uncertainty estimation in automatic target recognition of SAR images |
| title_full_unstemmed | Improved explainability through uncertainty estimation in automatic target recognition of SAR images |
| title_short | Improved explainability through uncertainty estimation in automatic target recognition of SAR images |
| title_sort | improved explainability through uncertainty estimation in automatic target recognition of sar images |
| topic | Automatic Target Recognition Synthetic Aperture Radar Explainable Artificial Intelligence Bayesian Neural Network UCTD |
| url | https://repository.up.ac.za/handle/2263/86502 |