A Novel Context-Adaptive Fusion of Shadow and Highlight Regions for Efficient Sonar Image Classification

2025-06-02

AI Summary

Shadow regions in underwater sonar imagery have been systematically overlooked in classification tasks, leading to insufficient robustness against noise and occlusion. Method: This paper introduces the first context-adaptive classification framework that holistically integrates features from both shadowed and highlight regions. It comprises: (1) a shadow-specific classifier coupled with an adaptive shadow segmentation mechanism; (2) an interpretability-driven, region-aware denoising model; and (3) S3Simulator+, a physics-informed synthetic dataset tailored for mine detection under realistic acoustic noise conditions. Contribution/Results: Experiments demonstrate substantial improvements in classification accuracy and robustness under noisy and occluded scenarios. The framework enhances model interpretability and reliability of autonomous underwater perception, establishing a novel paradigm for sonar image understanding.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Web data generation and simulation
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Sonar imaging is fundamental to underwater exploration, with critical applications in defense, navigation, and marine research. Shadow regions, in particular, provide essential cues for object detection and classification, yet existing studies primarily focus on highlight-based analysis, leaving shadow-based classification underexplored. To bridge this gap, we propose a Context-adaptive sonar image classification framework that leverages advanced image processing techniques to extract and integrate discriminative shadow and highlight features. Our framework introduces a novel shadow-specific classifier and adaptive shadow segmentation, enabling effective classification based on the dominant region. This approach ensures optimal feature representation, improving robustness against noise and occlusions. In addition, we introduce a Region-aware denoising model that enhances sonar image quality by preserving critical structural details while suppressing noise. This model incorporates an explainability-driven optimization strategy, ensuring that denoising is guided by feature importance, thereby improving interpretability and classification reliability. Furthermore, we present S3Simulator+, an extended dataset incorporating naval mine scenarios with physics-informed noise specifically tailored for the underwater sonar domain, fostering the development of robust AI models. By combining novel classification strategies with an enhanced dataset, our work addresses key challenges in sonar image analysis, contributing to the advancement of autonomous underwater perception.

Problem

• Improving sonar image classification using shadow and highlight regions
• Enhancing sonar image quality with region-aware denoising
• Expanding dataset for robust underwater sonar AI models

Innovation

• Context-adaptive fusion of shadow and highlight features
• Region-aware denoising preserving structural details
• S3Simulator+ dataset with physics-informed noise scenarios