Unsupervised scene-based clustering for underwater structural health monitoring: Enhancing image categorization and feature matching

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Abstract
This study presents a scene-based clustering methodology for underwater structural health monitoring (SHM), categorizing images by intrinsic scene attributes (visibility, color, and turbidity). Shifting focus from object detection to holistic scene understanding enhances automated condition assessment in underwater infrastructure inspection by prefiltering suboptimal images and optimizing downstream tasks such as image matching and alignment. Ensuring algorithms operate on images with consistent lighting, texture, and clarity reduces ambiguities and enhances feature correspondence. It integrates local and global feature extraction, correlation analysis, feature selection, dimensionality reduction, and outlier detection to improve clustering accuracy and pipeline robustness. Clustering uses hierarchical and centroid-based algorithms, evaluated by Silhouette Score, Calinski-Harabasz Index, F1-score, V-measure, Adjusted Mutual Information, and Adjusted Rand Index, applying the Hungarian algorithm for label mapping in pseudo-labeled cases. The methodology is validated on real and synthetic underwater datasets, including a bridge inspection video dataset with abovewater and underwater segments, and controlled laboratory experiments assessing turbidity effects on submerged structural elements. A key contribution is assessing feature matching performance across clustered and unclustered image sets, demonstrating that scene-based clustering significantly enhances keypoint detection, feature correspondence, and inlier ratios while reducing mismatches from illumination and viewpoint variations. Additionally, engineered features outperform pretrained deep learning features in this unsupervised setting, providing more reliable global scene representation and improved clustering and feature matching outcomes. These findings establish the proposed pipeline as a valuable tool for underwater SHM, supporting applications such as 3D reconstruction, infrastructure assessment, and autonomous underwater inspections.
Funding Information
  • Directorate for Engineering (1625039, 2018631)
  • Center for Integrated Asset Management for Multimodal Transportation Infrastructure Systems, Pennsylvania State University

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