MambaMPD: A Mamba-Driven Segmentation Framework for Marine Pollution Detection from Remote Sensing Imagery

• URL: http://arxiv.org/abs/2609.15676v1
• Date: Mon, 14 Sep 2026 14:52:16 GMT
• Title: MambaMPD: A Mamba-Driven Segmentation Framework for Marine Pollution Detection from Remote Sensing Imagery
• Abstract summary: MambaMPD is an enhanced Mamba-based framework incorporating Frequency-Aware Augmentation (FAA) and multi-scale Edge-Guided Attention (EGA)<n>FAA integrates wavelet transforms into the encoder to decompose features into multi-scale frequency subbands, enabling the model to identify small, low-contrast, and irregular pollution patterns.<n>EGA adaptively fuses hierarchical, Laplacian-derived boundary cues with deep semantic representations, refining encoder features before decoding to sharpen boundaries.
• Score: 16.897374410793592
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• MambaADv2: Evolving Duality-enhanced State Space Model for Unsupervised Anomaly Detection [108.36437360254605]
  This paper proposes MambaADv2, a framework tailored for multi-class unsupervised anomaly detection.<n>MambaADv2 comprises a pre-trained encoder and a Mamba-inspired decoder, equipped with Duality-enhanced State Space (DSS) modules across multiple scales.<n>The structure of the Hybrid State Space (HSS) block is tailored by following the SSD-based Mamba lineage and incorporating Mamba3-style position-aware state-space modeling.
  arXiv  Detail & Related papers  (2026-06-22T10:14:06Z)
• MambaDSF: Multi-Scale SSM with Dilated Feature Fusion for Sonar Small Target Detection [8.504127970224703]
  Small targets remain difficult to detect due to insufficient pixel coverage, low acoustic contrast, and scale ambiguity across imaging ranges.<n>This letter proposes Mamba Dilated-Scale Fusion (MambaDSF), a hybrid framework addressing these limitations.<n>MambaDSF achieves 91.5% mAP50 on the UATD forward-looking sonar benchmark with 28.7 million parameters, surpassing all compared detectors.
  arXiv  Detail & Related papers  (2026-05-24T08:14:19Z)
• Semantics and Content Matter: Towards Multi-Prior Hierarchical Mamba for Image Deraining [95.00432497331583]
  Multi-Prior Hierarchical Mamba (MPHM) network for image deraining.<n>MPHM integrates macro-semantic textual priors (CLIP) for task-level semantic guidance and micro-structural visual priors (DINOv2) for scene-aware structural information.<n>Experiments demonstrate MPHM's state-of-the-art performance, achieving a 0.57 dB PSNR gain on the Rain200H dataset.
  arXiv  Detail & Related papers  (2025-11-17T08:08:59Z)
• Multi-Representation Attention Framework for Underwater Bioacoustic Denoising and Recognition [0.924965746838578]
  We introduce a multi-step, attention-guided framework that first segments spectrograms to generate soft masks of biologically relevant energy.<n>Image and mask embeddings are integrated via mid-level fusion, enabling the model to focus on salient spectrogram regions.<n>Using real-world recordings from the Saguenay St. Lawrence Marine Park Research Station in Canada, we demonstrate that segmentation-driven attention and mid-level fusion improve signal discrimination.
  arXiv  Detail & Related papers  (2025-10-29T22:49:15Z)
• WaveSeg: Enhancing Segmentation Precision via High-Frequency Prior and Mamba-Driven Spectrum Decomposition [61.3530659856013]
  We propose a novel decoder architecture, WaveSeg, which jointly optimize feature refinement in spatial and wavelet domains.<n>High-frequency components are first learned from input images as explicit priors to reinforce boundary details.<n>Experiments on standard benchmarks demonstrate that WaveSeg, leveraging wavelet-domain frequency prior with Mamba-based attention, consistently outperforms state-of-the-art approaches.
  arXiv  Detail & Related papers  (2025-10-24T01:41:31Z)
• DEPFusion: Dual-Domain Enhancement and Priority-Guided Mamba Fusion for UAV Multispectral Object Detection [6.4402018224356015]
  A framework named DEPFusion is proposed for UAV multispectral object detection.<n>It consists of Dual-Domain Enhancement (DDE) and Priority-Guided Mamba Fusion (PGMF)<n>Experiments on DroneVehicle and VEDAI datasets demonstrate that DEPFusion achieves good performance with state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-09-09T01:51:57Z)
• Wavelet-Guided Dual-Frequency Encoding for Remote Sensing Change Detection [67.84730634802204]
  Change detection in remote sensing imagery plays a vital role in various engineering applications, such as natural disaster monitoring, urban expansion tracking, and infrastructure management.<n>Most existing methods still rely on spatial-domain modeling, where the limited diversity of feature representations hinders the detection of subtle change regions.<n>We observe that frequency-domain feature modeling particularly in the wavelet domain amplify fine-grained differences in frequency components, enhancing the perception of edge changes that are challenging to capture in the spatial domain.
  arXiv  Detail & Related papers  (2025-08-07T11:14:16Z)
• FaRMamba: Frequency-based learning and Reconstruction aided Mamba for Medical Segmentation [3.5790602918760586]
  Vision Mamba employs one-dimensional causal state-space recurrence to efficiently model global dependencies.<n>Its patch tokenization and 1D serialization disrupt local pixel adjacency and impose a low-pass filtering effect.<n>We propose FaRMamba, a novel extension that explicitly addresses LHICD and 2D-SSD through two complementary modules.
  arXiv  Detail & Related papers  (2025-07-26T20:41:53Z)
• VRS-UIE: Value-Driven Reordering Scanning for Underwater Image Enhancement [104.78586859995333]
  State Space Models (SSMs) have emerged as a promising backbone for vision tasks due to their linear complexity and global receptive field.<n>The predominance of large-portion, homogeneous but useless oceanic backgrounds can dilute the feature representation responses of sparse yet valuable targets.<n>We propose a novel Value-Driven Reordering Scanning framework for Underwater Image Enhancement (UIE)<n>Our framework sets a new state-of-the-art, delivering superior enhancement performance (surpassing WMamba by 0.89 dB on average) by effectively suppressing water bias and preserving structural and color fidelity.
  arXiv  Detail & Related papers  (2025-05-02T12:21:44Z)
• A Hybrid Transformer-Mamba Network for Single Image Deraining [70.64069487982916]
  Existing deraining Transformers employ self-attention mechanisms with fixed-range windows or along channel dimensions. We introduce a novel dual-branch hybrid Transformer-Mamba network, denoted as TransMamba, aimed at effectively capturing long-range rain-related dependencies.
  arXiv  Detail & Related papers  (2024-08-31T10:03:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.