# OSDMamba: Enhancing Oil Spill Detection from Remote Sensing Images Using Selective State Space Model

arXiv: https://arxiv.org/abs/2506.18006 · HTML: https://mlsanity.com/paper/2506.18006/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Point clouds, detection and remote sensing, one of 10 directions in State-space sequence models (S4, Mamba).
- Builds on 18 of its 39 references that are in the map.
- First posted on arXiv 22 Jun 2025.

## Placed in

- State-space sequence models (S4, Mamba) / [Point clouds, detection and remote sensing](https://mlsanity.com/ssm/point-clouds-detection-and-remote-sensing/)

## Builds on (most cited first, 18 of 18)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model](https://mlsanity.com/paper/2401.09417/) (2024)
- [Image Transformer](https://mlsanity.com/paper/1802.05751/) (2018)
- [VM-UNet: Vision Mamba UNet for Medical Image Segmentation](https://mlsanity.com/paper/2402.02491/) (2024)
- [SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation](https://mlsanity.com/paper/2209.08575/) (2022)
- [LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation](https://mlsanity.com/paper/1707.03718/) (2017)
- [Swin-UMamba: Mamba-based UNet with ImageNet-based pretraining](https://mlsanity.com/paper/2402.03302/) (2024)
- [Mixed Transformer U-Net For Medical Image Segmentation](https://mlsanity.com/paper/2111.04734/) (2021)
- [Convolutional State Space Models for Long-Range Spatiotemporal Modeling](https://mlsanity.com/paper/2310.19694/) (2023)
- [Offset Curves Loss for Imbalanced Problem in Medical Segmentation](https://mlsanity.com/paper/2012.02463/) (2020)
- [Compositional Oil Spill Detection Based on Object Detector and Adapted Segment Anything Model from SAR Images](https://mlsanity.com/paper/2401.07502/) (2024)
- [Deep learning based on Transformer architecture for power system short-term voltage stability assessment with class imbalance](https://mlsanity.com/paper/2310.11690/) (2023)
- [Oil Spill Segmentation using Deep Encoder-Decoder models](https://mlsanity.com/paper/2305.01386/) (2023)

## Built on by (most cited first, 3 of 3)

- [Beyond Segmentation: An Oil Spill Change Detection Framework Using Synthetic SAR Imagery](https://mlsanity.com/paper/2601.02139/) (2026)
- [OilSAM2: Memory-Augmented SAM2 for Scalable SAR Oil Spill Detection](https://mlsanity.com/paper/2603.10231/) (2026)
- [MambaMPD: A Mamba-Driven Segmentation Framework for Marine Pollution Detection from Remote Sensing Imagery](https://mlsanity.com/paper/2609.15676/) (2026)

## Abstract (arXiv)

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.