用Mamba模型提升遥感图像油污检测精度，尤其擅长小目标识别。
OSDMamba: Enhancing Oil Spill Detection from Remote Sensing Images Using Selective State Space Model
- 采用Mamba的选通扫描机制扩大感受野，保留细节。
- 设计非对称解码器与深度监督，提升小样本油污检测能力。
- 在两个公开数据集上分别提升8.9%和11.8%，适合遥感灾害监测。
语义分割常用于遥感图像中的油污检测（OSD），但标注样本少和类别不平衡严重限制了检测精度。现有基于卷积神经网络（CNN）的方法因感受野有限且难以捕捉全局上下文信息，难以检测小面积油污。本研究探索状态空间模型（SSMs）特别是Mamba在视觉任务中的潜力，提出首个专为油污检测设计的Mamba架构——OSDMamba。该模型利用Mamba的选通扫描机制有效扩展感受野并保留关键细节。此外，设计包含ConvSSM和深度监督的非对称解码器，强化多尺度特征融合，提升对少数类样本的敏感性。实验结果表明，OSDMamba在两个公开数据集上分别取得8.9%和11.8%的性能提升，达到当前最优水平。
原文摘要 · Abstract (English)
Semantic segmentation is commonly used for Oil Spill Detection (OSD) in remote sensing images. However, the limited availability of labelled oil spill samples and class imbalance present significant challenges that can reduce detection accuracy. Furthermore, most existing methods, which rely on convolutional neural networks (CNNs), struggle to detect small oil spill areas due to their limited receptive fields and inability to effectively capture global contextual information. This study explores the potential of State-Space Models (SSMs), particularly Mamba, to overcome these limitations, building on their recent success in vision applications. We propose OSDMamba, the first Mamba-based architecture specifically designed for oil spill detection. OSDMamba leverages Mamba's selective scanning mechanism to effectively expand the model's receptive field while preserving critical details. Moreover, we designed an asymmetric decoder incorporating ConvSSM and deep supervision to strengthen multi-scale feature fusion, thereby enhancing the model's sensitivity to minority class samples. Experimental results show that the proposed OSDMamba achieves state-of-the-art performance, yielding improvements of 8.9% and 11.8% in OSD across two publicly available datasets.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。