An ocean front detection and tracking algorithm
- URL: http://arxiv.org/abs/2502.15250v4
- Date: Wed, 14 May 2025 09:24:28 GMT
- Title: An ocean front detection and tracking algorithm
- Authors: Yishuo Wang, Feng Zhou, Qicheng Meng, Muping Zhou, Zhijun Hu, Chengqing Zhang, Tianhao Zhao,
- Abstract summary: This paper proposes the Bayesian Front Detection and Tracking framework with Metric Space Analysis.<n>BFDTMSA reduces over-detection by $73%$ compared to histogram-based methods.<n>The open-source release bridges a critical gap in reproducible oceanographic research.
- Score: 4.0604303269549185
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Existing ocean front detection methods--including histogram-based variance analysis, Lyapunov exponent, gradient thresholding, and machine learning--suffer from critical limitations: discontinuous outputs, over-detection, reliance on single-threshold decisions, and lack of open-source implementations. To address these challenges, this paper proposes the Bayesian Front Detection and Tracking framework with Metric Space Analysis (BFDT-MSA). The framework introduces three innovations: (1) a Bayesian decision mechanism that integrates gradient priors and field operators to eliminate manual threshold sensitivity; (2) morphological refinement algorithms for merging fragmented fronts, deleting spurious rings, and thinning frontal zones to pixel-level accuracy; and (3) a novel metric space definition for temporal front tracking, enabling systematic analysis of front evolution. Validated on global SST data (2022--2024), BFDT-MSA reduces over-detection by $73\%$ compared to histogram-based methods while achieving superior intensity ($0.16^\circ$C/km), continuity, and spatiotemporal coherence. The open-source release bridges a critical gap in reproducible oceanographic research.
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