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Adaptive Residual Transformation for Enhanced Feature-Based OOD Detection in SAR Imagery

Kyung-hwan Lee, Kyung-tae Kim

TL;DR

This work proposes transforming feature-based OOD detection into a class-localized feature-residual-based approach, demonstrating that this method can improve stability across varying unknown targets' distribution conditions and highlights the practical relevance of residual-based OOD detection for SAR applications.

Abstract

Recent advances in deep learning architectures have enabled efficient and accurate classification of pre-trained targets in Synthetic Aperture Radar (SAR) images. Nevertheless, the presence of unknown targets in real battlefield scenarios is unavoidable, resulting in misclassification and reducing the accuracy of the classifier. Over the past decades, various feature-based out-of-distribution (OOD) approaches have been developed to address this issue, yet defining the decision boundary between known and unknown targets remains challenging. Additionally, unlike optical images, detecting unknown targets in SAR imagery is further complicated by high speckle noise, the presence of clutter, and the inherent similarities in back-scattered microwave signals. In this work, we propose transforming feature-based OOD detection into a class-localized feature-residual-based approach, demonstrating that this method can improve stability across varying unknown targets' distribution conditions. Transforming feature-based OOD detection into a residual-based framework offers a more robust reference space for distinguishing between in-distribution (ID) and OOD data, particularly within the unique characteristics of SAR imagery. This adaptive residual transformation method standardizes feature-based inputs into distributional representations, enhancing OOD detection in noisy, low-information images. Our approach demonstrates promising performance in real-world SAR scenarios, effectively adapting to the high levels of noise and clutter inherent in these environments. These findings highlight the practical relevance of residual-based OOD detection for SAR applications and suggest a foundation for further advancements in unknown target detection in complex, operational settings.

Adaptive Residual Transformation for Enhanced Feature-Based OOD Detection in SAR Imagery

TL;DR

This work proposes transforming feature-based OOD detection into a class-localized feature-residual-based approach, demonstrating that this method can improve stability across varying unknown targets' distribution conditions and highlights the practical relevance of residual-based OOD detection for SAR applications.

Abstract

Recent advances in deep learning architectures have enabled efficient and accurate classification of pre-trained targets in Synthetic Aperture Radar (SAR) images. Nevertheless, the presence of unknown targets in real battlefield scenarios is unavoidable, resulting in misclassification and reducing the accuracy of the classifier. Over the past decades, various feature-based out-of-distribution (OOD) approaches have been developed to address this issue, yet defining the decision boundary between known and unknown targets remains challenging. Additionally, unlike optical images, detecting unknown targets in SAR imagery is further complicated by high speckle noise, the presence of clutter, and the inherent similarities in back-scattered microwave signals. In this work, we propose transforming feature-based OOD detection into a class-localized feature-residual-based approach, demonstrating that this method can improve stability across varying unknown targets' distribution conditions. Transforming feature-based OOD detection into a residual-based framework offers a more robust reference space for distinguishing between in-distribution (ID) and OOD data, particularly within the unique characteristics of SAR imagery. This adaptive residual transformation method standardizes feature-based inputs into distributional representations, enhancing OOD detection in noisy, low-information images. Our approach demonstrates promising performance in real-world SAR scenarios, effectively adapting to the high levels of noise and clutter inherent in these environments. These findings highlight the practical relevance of residual-based OOD detection for SAR applications and suggest a foundation for further advancements in unknown target detection in complex, operational settings.

Paper Structure

This paper contains 29 sections, 30 equations, 10 figures, 10 tables.

Table of Contents

  1. Introduction
  2. Motivation
  3. Domain Gaps between Optical and SAR Images
  4. Limitation of Post-Processing OOD Detection Methods
  5. Inside Out: Reversing From Central OOD to Peripheral OOD Clusters
  6. Method
  7. Class-specific Residual between Trained and Test Samples
  8. Construction of Reference Space with Residuals' Characteristics
  9. Out-of-Distribution Methods with Class-specific Residuals
  10. Statistical distance
  11. OpenMax
  12. Mahalanobis Distance
  13. ViM
  14. kNN Distance/Density
  15. Result
  16. ...and 14 more sections

Figures (10)

  • Figure 1: MSTAR Database: Optical images of military targets versus SAR images b1. Optical images of MSTAR database targets and their corresponding SAR images. Top to bottom rows, From left to right: the targets are 2S1, BMP2, BRDM2, BTR60, BTR70, D7, T62, T72, ZIL, and ZSU.
  • Figure 2: Scatter Plot of MSP with Untrained Labels 6$\sim$10
  • Figure 3: Scatter Plot of MSP with Untrained Labels 1$\sim$5
  • Figure 4: Inverting ID and OOD Detection: From Central to Peripheral Detection Strategies
  • Figure 5: Flow Chart of the Process
  • ...and 5 more figures