OSDMamba: Enhancing Oil Spill Detection from Remote Sensing Images Using Selective State Space Model
This addresses the problem of accurate oil spill detection for environmental monitoring, but it is incremental as it adapts an existing model to a specific domain.
The paper tackled oil spill detection in remote sensing images by proposing OSDMamba, a Mamba-based architecture, which achieved state-of-the-art performance with improvements of 8.9% and 11.8% on two datasets.
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.