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X-Fake: Juggling Utility Evaluation and Explanation of Simulated SAR Images

Zhongling Huang, Yihan Zhuang, Zipei Zhong, Feng Xu, Gong Cheng, Junwei Han

TL;DR

X-Fake addresses the gap between simulated and real SAR data utility by introducing a trustworthy evaluation-explanation framework that combines a Bayesian probabilistic evaluator with an IntroVAE-based counterfactual explainer. The evaluator measures distribution shift via uncertainties in class and azimuth predictions, while the explainer generates high-resolution counterfactuals to reveal inauthentic details. Across EM-model and GenAI-generated SAR datasets, X-Fake outperforms traditional IQA methods in aligning evaluation with downstream utility and provides interpretable explanations, improving data use for SAR deep learning. This framework offers a practical path to quantify and improve the usefulness of simulated SAR data for real-world applications.

Abstract

SAR image simulation has attracted much attention due to its great potential to supplement the scarce training data for deep learning algorithms. Consequently, evaluating the quality of the simulated SAR image is crucial for practical applications. The current literature primarily uses image quality assessment techniques for evaluation that rely on human observers' perceptions. However, because of the unique imaging mechanism of SAR, these techniques may produce evaluation results that are not entirely valid. The distribution inconsistency between real and simulated data is the main obstacle that influences the utility of simulated SAR images. To this end, we propose a novel trustworthy utility evaluation framework with a counterfactual explanation for simulated SAR images for the first time, denoted as X-Fake. It unifies a probabilistic evaluator and a causal explainer to achieve a trustworthy utility assessment. We construct the evaluator using a probabilistic Bayesian deep model to learn the posterior distribution, conditioned on real data. Quantitatively, the predicted uncertainty of simulated data can reflect the distribution discrepancy. We build the causal explainer with an introspective variational auto-encoder to generate high-resolution counterfactuals. The latent code of IntroVAE is finally optimized with evaluation indicators and prior information to generate the counterfactual explanation, thus revealing the inauthentic details of simulated data explicitly. The proposed framework is validated on four simulated SAR image datasets obtained from electromagnetic models and generative artificial intelligence approaches. The results demonstrate the proposed X-Fake framework outperforms other IQA methods in terms of utility. Furthermore, the results illustrate that the generated counterfactual explanations are trustworthy, and can further improve the data utility in applications.

X-Fake: Juggling Utility Evaluation and Explanation of Simulated SAR Images

TL;DR

X-Fake addresses the gap between simulated and real SAR data utility by introducing a trustworthy evaluation-explanation framework that combines a Bayesian probabilistic evaluator with an IntroVAE-based counterfactual explainer. The evaluator measures distribution shift via uncertainties in class and azimuth predictions, while the explainer generates high-resolution counterfactuals to reveal inauthentic details. Across EM-model and GenAI-generated SAR datasets, X-Fake outperforms traditional IQA methods in aligning evaluation with downstream utility and provides interpretable explanations, improving data use for SAR deep learning. This framework offers a practical path to quantify and improve the usefulness of simulated SAR data for real-world applications.

Abstract

SAR image simulation has attracted much attention due to its great potential to supplement the scarce training data for deep learning algorithms. Consequently, evaluating the quality of the simulated SAR image is crucial for practical applications. The current literature primarily uses image quality assessment techniques for evaluation that rely on human observers' perceptions. However, because of the unique imaging mechanism of SAR, these techniques may produce evaluation results that are not entirely valid. The distribution inconsistency between real and simulated data is the main obstacle that influences the utility of simulated SAR images. To this end, we propose a novel trustworthy utility evaluation framework with a counterfactual explanation for simulated SAR images for the first time, denoted as X-Fake. It unifies a probabilistic evaluator and a causal explainer to achieve a trustworthy utility assessment. We construct the evaluator using a probabilistic Bayesian deep model to learn the posterior distribution, conditioned on real data. Quantitatively, the predicted uncertainty of simulated data can reflect the distribution discrepancy. We build the causal explainer with an introspective variational auto-encoder to generate high-resolution counterfactuals. The latent code of IntroVAE is finally optimized with evaluation indicators and prior information to generate the counterfactual explanation, thus revealing the inauthentic details of simulated data explicitly. The proposed framework is validated on four simulated SAR image datasets obtained from electromagnetic models and generative artificial intelligence approaches. The results demonstrate the proposed X-Fake framework outperforms other IQA methods in terms of utility. Furthermore, the results illustrate that the generated counterfactual explanations are trustworthy, and can further improve the data utility in applications.
Paper Structure (26 sections, 19 equations, 10 figures, 7 tables)

This paper contains 26 sections, 19 equations, 10 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Image Quality Assessment
  4. Utility of Simulated SAR Images
  5. Uncertainty Quantification and Explanation for Trustworthy AI
  6. Method
  7. Overview
  8. Probabilistic Evaluator
  9. Optimization with Bayes by Backprop
  10. Monte Carlo Dropout as a Bayesian Approximation
  11. Uncertainty
  12. IntroVAE Pre-training
  13. Counterfactual Explanation Generation
  14. Experiments
  15. Datasets
  16. ...and 11 more sections

Figures (10)

  • Figure 1: (a) Conventional IQA metrics for simulated SAR image evaluation. (b) The inconsistent distribution is the main obstacle that influences the utility of simulated data. (c) Proposed X-Fake: A trustworthy framework that can evaluate and explain the simulated SAR images in terms of utility, with providing quantitative indicators as well as explicit high-quality explanations.
  • Figure 2: The proposed trustworthy utility assessment framework, X-Fake. (1) Constructing a probabilistic evaluator (P-Eva) with Bayesian deep convolutional neural networks, consisting of label and azimuth angle prediction. (2) Pre-training the IntroVAE model to prepare for high-quality counterfactual generation. (3) Optimizing the latent code to obtain the counterfactual explanation.
  • Figure 3: Overall architecture of the proposed BBB-A-ConvNet-a. The BBB convolution layers are represented as “BBBConv. (number of feature maps) $@$ (filter size).”
  • Figure 4: Some examples of the experimented datasets.
  • Figure 5: We compare four FR-IQA metrics (PSNR korhonen2012peak, SSIM wang2004image, VIF sheikh2006image, FSIM 5705575) and four NR-IQA models (NIQE 6353522, BRISQUE 6272356, NIMA 8352823, DBCNN 8576582) with the proposed Eva-BBB and Eva-MCD. For each simulated SAR image dataset, we sort the samples according to the evaluation metrics and split them into "TOP" and "LAST" evenly. The deep models are trained with "TOP" and "LAST" data, respectively, with classification accuracy and angle loss reported in the results. "Gap" denotes the performance gap between "TOP" and "LAST", where the green values indicate the effectiveness of the evaluation method in terms of utility.
  • ...and 5 more figures