OilSAM2: Memory-Augmented SAM2 for Scalable SAR Oil Spill Detection

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• OilSAM2 introduces a hierarchical feature aware multi scale memory bank that models texture, structure, and semantic level representations for cross-image information reuse.method 90%
• OilSAM2 proposes a structure semantic consistent memory update strategy to mitigate memory drift by selectively refreshing memory based on semantic discrepancy and structural variation.method 90%
• OilSAM2 is tailored for unordered SAR oil spill monitoring, addressing the limitation of existing memory-augmented models that assume temporal coherence.technical 85%
• OilSAM2 achieves state-of-the-art segmentation performance on two public SAR oil spill datasets.result 90%
• OilSAM2 delivers stable and accurate results under noisy SAR monitoring scenarios.result 85%
• Existing SAM-based approaches for oil spill detection operate on single images and cannot effectively reuse information across scenes.limitation 85%
• Segmenting oil spills from SAR imagery is challenging due to severe appearance variability, scale heterogeneity, and the absence of temporal continuity in real-world monitoring.technical 90%

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