MambaMPD：一种基于Mamba的遥感图像海洋污染检测分割框架

中文摘要 AI 辅助

准确的海洋污染检测（MPD）对于保护沿海生态系统和海洋生物多样性至关重要。Vision Mamba模型通过高效捕获长距离依赖和全局上下文，在遥感语义分割中展现出潜力，但其在MPD中的潜力尚未得到充分探索。由于信噪比低、污染模式碎片化以及污染物与周围海域视觉相似导致边界模糊，MPD尤为具有挑战性。为解决这些问题，我们提出MambaMPD，一种增强的基于Mamba的框架，融合两种互补的结构先验：频率感知增强（FAA）和多尺度边缘引导注意力（EGA）。FAA将小波变换集成到编码器中，将特征分解为多尺度频率子带，使模型能够捕获识别小尺寸、低对比度和不规则污染模式所需的低频上下文语义和高频结构细节。EGA自适应地将基于拉普拉斯算子的分层边界线索与深层语义表示融合，在解码前细化编码器特征，以锐化边界并减少视觉混淆、空间碎片化场景中的模糊性。这些模块共同提高了对细微污染信号的敏感性，同时保留了精细的边界结构。采用带有挤压激励注意力和深度监督的U-Net风格解码器，逐步恢复和细化跨尺度的语义和空间信息。在两个基准MPD数据集上的大量实验表明，MambaMPD在实现比竞争方法更高的mIoU的同时，所需计算量远低于基于基础模型的方法。在MADOS上，其F1比OSDMamba提高3.6%；在M4D上，其石油泄漏IoU比TransOilSeg提高6.82%。

英文摘要

Accurate marine pollution detection (MPD) is essential for protecting coastal ecosystems and marine biodiversity. Vision Mamba models have shown promise in remote-sensing semantic segmentation by efficiently capturing long-range dependencies and global context, yet their potential for MPD remains underexplored. MPD is particularly challenging because of low signal-to-noise ratios, fragmented pollution patterns, and indistinct boundaries caused by the visual similarity between pollutants and the surrounding sea. To address these issues, we propose MambaMPD, an enhanced Mamba-based framework incorporating two complementary structural priors: Frequency-Aware Augmentation (FAA) and multi-scale Edge-Guided Attention (EGA). FAA integrates wavelet transforms into the encoder to decompose features into multi-scale frequency subbands, enabling the model to capture low-frequency contextual semantics and high-frequency structural details needed to identify small, low-contrast, and irregular pollution patterns. EGA adaptively fuses hierarchical, Laplacian-derived boundary cues with deep semantic representations, refining encoder features before decoding to sharpen boundaries and reduce ambiguity in visually confusing, spatially fragmented scenes. Together, these modules improve sensitivity to subtle pollution signals while preserving fine boundary structures. A U-Net-style decoder with squeeze-and-excitation attention and deep supervision progressively restores and refines semantic and spatial information across scales. Extensive experiments on two benchmark MPD datasets show that MambaMPD achieves higher mIoU than competing methods while requiring substantially less computation than foundation-model-based approaches. On MADOS, it improves F1 by 3.6% over OSDMamba; on M4D, it raises Oil Spill IoU by 6.82% over TransOilSeg.

发表机构

• University of Exeter（埃克塞特大学）
• China University of Geosciences（中国地质大学）
• China University of Petroleum (East China)（中国石油大学（华东））

机构由 AI 辅助整理，请以论文原文为准。