Version 1
: Received: 9 November 2023 / Approved: 9 November 2023 / Online: 12 December 2023 (08:49:00 CET)
How to cite:
Centazzo, A.; Quattrini, A.; Peternier, A.; Leidi, T.; Pasquali, P. Exploring Effectiveness and Usability of Synthetic Sar Data in Real Classification Scenarios: A Case Study. Preprints2023, 2023110646. https://doi.org/10.20944/preprints202311.0646.v1
Centazzo, A.; Quattrini, A.; Peternier, A.; Leidi, T.; Pasquali, P. Exploring Effectiveness and Usability of Synthetic Sar Data in Real Classification Scenarios: A Case Study. Preprints 2023, 2023110646. https://doi.org/10.20944/preprints202311.0646.v1
Centazzo, A.; Quattrini, A.; Peternier, A.; Leidi, T.; Pasquali, P. Exploring Effectiveness and Usability of Synthetic Sar Data in Real Classification Scenarios: A Case Study. Preprints2023, 2023110646. https://doi.org/10.20944/preprints202311.0646.v1
APA Style
Centazzo, A., Quattrini, A., Peternier, A., Leidi, T., & Pasquali, P. (2023). Exploring Effectiveness and Usability of Synthetic Sar Data in Real Classification Scenarios: A Case Study. Preprints. https://doi.org/10.20944/preprints202311.0646.v1
Chicago/Turabian Style
Centazzo, A., Tiziano Leidi and Paolo Pasquali. 2023 "Exploring Effectiveness and Usability of Synthetic Sar Data in Real Classification Scenarios: A Case Study" Preprints. https://doi.org/10.20944/preprints202311.0646.v1
Abstract
In recent years, Synthetic Aperture Radar (SAR) has received attention in the Automatic Target Recognition (ATR) field due to its ability to acquire images both day and night, combined with the superior resolution offered by the latest sensors. However, despite advances, there remains a deficiency in dataset quality and quantity. This has necessitated the use of simulated data to overcome challenges associated with obtaining large quantities of high-quality images of targets and their subsequent labeling. In this study, we trained a Convolutional Neural Network (CNN) using a simulated dataset, subsequently employing it to classify real images from the MSTAR dataset. Traditional training methodologies often struggle with generalization when confronted with domain disparities between simulated and real data. To mitigate this, we exploited Margin Disparity Discrepancy (MDD), a domain adaptation technique not previously explored in SAR context. Findings from this study showcased a 13% increase in classification accuracy and improved generalization using MDD. Through Explainable AI (XAI) techniques such as t-SNE and other metrics like Kullback Leibler Divergence (KLD), we revealed a more distinct alignment of feature spaces between the two domains after domain adaptation, emphasizing MDD’s role in enhancing model accuracy and generalization.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
