针对无序遥感图像中的油污分割难题，提出可复用跨图信息的增强框架
OilSAM2: Memory-Augmented SAM2 for Scalable SAR Oil Spill Detection
- 构建分层多尺度记忆库，融合纹理、结构与语义特征实现跨图信息复用
- 在两个公开数据集上达到最新性能，噪声环境下仍保持稳定准确分割
- 适合处理无时间连续性的海量遥感油污监测场景，尤其适用于突发污染事件
从合成孔径雷达（SAR）图像中分割油污仍具挑战，主要源于外观变化大、尺度差异显著，且实际监测中缺乏时间连续性。尽管基础模型如Segment Anything（SAM）支持提示驱动分割，但现有基于SAM的方法仅处理单图，难以跨场景复用信息。记忆增强变体（如SAM2）虽引入记忆机制，但假设存在时间连贯性，在面对无序的SAR图像集合时易产生语义漂移。本文提出OilSAM2，一种专为无序SAR油污监测设计的记忆增强分割框架。该方法引入分层特征感知的多尺度记忆库，显式建模纹理、结构与语义层级表征，实现鲁棒的跨图像信息复用。为进一步缓解记忆漂移，提出基于语义差异与结构变异的结构语义一致性记忆更新策略，选择性刷新记忆。在两个公开SAR油污数据集上的实验表明，OilSAM2达到当前最优分割性能，在噪声复杂的监测场景下仍表现稳定可靠。源代码已开源：https://github.com/Chenshuaiyu1120/OILSAM2。
原文摘要 · Abstract (English)
Segmenting oil spills from Synthetic Aperture Radar (SAR) imagery remains challenging due to severe appearance variability, scale heterogeneity, and the absence of temporal continuity in real world monitoring scenarios. While foundation models such as Segment Anything (SAM) enable prompt driven segmentation, existing SAM based approaches operate on single images and cannot effectively reuse information across scenes. Memory augmented variants (e.g., SAM2) further assume temporal coherence, making them prone to semantic drift when applied to unordered SAR image collections. We propose OilSAM2, a memory augmented segmentation framework tailored for unordered SAR oil spill monitoring. OilSAM2 introduces a hierarchical feature aware multi scale memory bank that explicitly models texture, structure, and semantic level representations, enabling robust cross image information reuse. To mitigate memory drift, we further propose a structure semantic consistent memory update strategy that selectively refreshes memory based on semantic discrepancy and structural variation.Experiments on two public SAR oil spill datasets demonstrate that OilSAM2 achieves state of the art segmentation performance, delivering stable and accurate results under noisy SAR monitoring scenarios. The source code is available at https://github.com/Chenshuaiyu1120/OILSAM2.
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