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Oil Spill Detection with Multiscale Conditional Adversarial Networks with Small-Data Training

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-981-13-0893-2_38openalex-snapshot

Detection of Oil Spill Through Fully Convolutional Network

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A New Algorithm for SAR Image Target Recognition Based on an Improved Deep Convolutional Neural Network

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A Novel Semi-Supervised Convolutional Neural Network Method for Synthetic Aperture Radar Image Recognition

inferredcites →https://doi.org/10.1016/j.cageo.2014.07.015openalex-snapshot

Automatic decision support system based on SAR data for oil spill detection

inferredcites →https://doi.org/10.1016/j.jag.2014.01.011openalex-snapshot

Oil spill detection using synthetic aperture radar images and feature selection in shape space

inferredcites →https://doi.org/10.1016/j.marpolbul.2013.05.022openalex-snapshot

Automatic Synthetic Aperture Radar based oil spill detection and performance estimation via a semi-automatic operational service benchmark

inferredcites →https://doi.org/10.1016/j.marpolbul.2014.10.041openalex-snapshot

Utilization of a genetic algorithm for the automatic detection of oil spill from RADARSAT-2 SAR satellite data

inferredcites →https://doi.org/10.1016/j.rse.2013.10.012openalex-snapshot

A comparative study of different classification techniques for marine oil spill identification using RADARSAT-1 imagery

inferredcites →https://doi.org/10.1080/2150704x.2019.1587197openalex-snapshot

Oil spill extraction by X-band marine radar using texture analysis and adaptive thresholding

inferredcites →https://doi.org/10.1080/2150704x.2021.1892852openalex-snapshot

Oil spill extraction from X-band marine radar images by power fitting of radar echoes

inferredcites →https://doi.org/10.1080/2151237x.2007.10129236openalex-snapshot

Adaptive Thresholding using the Integral Image

inferredcites →https://doi.org/10.1109/36.868885openalex-snapshot

Neural networks for oil spill detection using ERS-SAR data

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Visual Saliency Modeling for River Detection in High-Resolution SAR Imagery

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Going deeper with convolutions

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Fully convolutional networks for semantic segmentation

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Deep Residual Learning for Image Recognition

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Image-to-Image Translation with Conditional Adversarial Networks

inferredcites →https://doi.org/10.1109/iccv.2019.00467openalex-snapshot

SinGAN: Learning a Generative Model From a Single Natural Image

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Energy Minimization With One Dot Fuzzy Initialization for Marine Oil Spill Segmentation

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Oil Spill Mapping and Measurement in the Gulf of Mexico With Textural Classifier Neural Network Algorithm (TCNNA)

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Satellite Oil Spill Detection Using Artificial Neural Networks

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An Image-Segmentation-Based Framework to Detect Oil Slicks From Moving Vessels in the Southern African Oceans Using SAR Imagery

inferredcites →https://doi.org/10.1109/jstars.2018.2827996openalex-snapshot

Oil Spill Detection in Synthetic Aperture Radar Images Using Lipschitz-Regularity and Multiscale Techniques

inferredcites →https://doi.org/10.1109/jstars.2018.2833485openalex-snapshot

Segmenting Oil Spills from Blurry Images Based on Alternating Direction Method of Multipliers

inferredcites →https://doi.org/10.1109/jstars.2020.3019418openalex-snapshot

A Badging System for Reproducibility and Replicability in Remote Sensing Research

inferredcites →https://doi.org/10.1109/lgrs.2007.907174openalex-snapshot

Classifiers and Confidence Estimation for Oil Spill Detection in ENVISAT ASAR Images

inferredcites →https://doi.org/10.1109/lgrs.2015.2497716openalex-snapshot

Dual Smoothing for Marine Oil Spill Segmentation

inferredcites →https://doi.org/10.1109/tgrs.2006.887019openalex-snapshot

Oil Spill Detection in Radarsat and Envisat SAR Images

inferredcites →https://doi.org/10.1109/tgrs.2013.2261076openalex-snapshot

Fully Automatic Dark-Spot Detection From SAR Imagery With the Combination of Nonadaptive Weibull Multiplicative Model and Pulse-Coupled Neural Networks

inferredcites →https://doi.org/10.1109/tgrs.2016.2574561openalex-snapshot

Polarimetric Analysis of Compact-Polarimetry SAR Architectures for Sea Oil Slick Observation

inferredcites →https://doi.org/10.1109/tgrs.2017.2690001openalex-snapshot

Analysis of Evolving Oil Spills in Full-Polarimetric and Hybrid-Polarity SAR

inferredcites →https://doi.org/10.1109/tgrs.2018.2803038openalex-snapshot

Oil Spill Segmentation via Adversarial $f$ -Divergence Learning

inferredcites →https://doi.org/10.1109/tgrs.2018.2812619openalex-snapshot

Two-Stage Convolutional Neural Network for Ship and Spill Detection Using SLAR Images

inferredcites →https://doi.org/10.1109/tip.2011.2146190openalex-snapshot

A Level Set Method for Image Segmentation in the Presence of Intensity Inhomogeneities With Application to MRI

inferredcites →https://doi.org/10.1109/tpami.2011.274openalex-snapshot

SAR Image Segmentation Based on Level Set Approach and {\cal G}_A^0 Model

inferredcites →https://doi.org/10.3390/jmse8090653openalex-snapshot

Inversion of the Thickness of Crude Oil Film Based on an OG-CNN Model

inferredcites →https://doi.org/10.3390/rs11121402openalex-snapshot

Semantic Segmentation of SLAR Imagery with Convolutional LSTM Selectional AutoEncoders

inferredcites →https://doi.org/10.3390/rs11151762openalex-snapshot

Oil Spill Identification from Satellite Images Using Deep Neural Networks

inferredcites →https://doi.org/10.3390/rs12203338openalex-snapshot

Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review

inferredcites →https://doi.org/10.3390/s17081837openalex-snapshot

Discrimination of Oil Slicks and Lookalikes in Polarimetric SAR Images Using CNN

inferredcites →https://doi.org/10.3390/s18010091openalex-snapshot

A Review of Oil Spill Remote Sensing

inferredcites →https://doi.org/10.3390/s18030797openalex-snapshot

Segmentation of Oil Spills on Side-Looking Airborne Radar Imagery with Autoencoders

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A Deep-Learning Framework for the Detection of Oil Spills from SAR Data

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Building Surrogate Models by Integrating Limited Simulation Data and Shape Prior Knowledge Via Adaptive Gradient Sampling Strategy

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CBENet: contextual and boundary-enhanced network for oil spill detection via microwave remote sensing

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Oil Spill Detection

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Integrating small data and shape prior knowledge with gradient-enhanced Kriging through adaptive knowledge sampling

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Predicting oil spill diffusion through generative adversarial models

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HSRD-Net: A hybrid RemoteSAM-ResNet with dynamic weight assignment for SAR oil spill detection

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A novel TSM-RBF–driven hybrid detection paradigm for marine oil spills in shipborne radar images

inferred← citeshttps://doi.org/10.1016/j.jag.2022.102695openalex-snapshot

Deep-water oil-spill monitoring and recurrence analysis in the Brazilian territory using Sentinel-1 time series and deep learning

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Multi-source knowledge graph reasoning for ocean oil spill detection from satellite SAR images

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Oil spill detection and classification through deep learning and tailored data augmentation

inferred← citeshttps://doi.org/10.1016/j.jlp.2022.104912openalex-snapshot

Oil spills characteristics, detection, and recovery methods: A systematic risk-based view

inferred← citeshttps://doi.org/10.1016/j.marenvres.2024.106904openalex-snapshot

SAGPNet: A shape-aware and adaptive strip self-attention guided progressive network for SAR marine oil spill detection

inferred← citeshttps://doi.org/10.1016/j.marpolbul.2022.113718openalex-snapshot

Monitoring of oil spill in the offshore zone of the Nile Delta using Sentinel data

inferred← citeshttps://doi.org/10.1016/j.marpolbul.2023.114651openalex-snapshot

Ocean oil spill detection from SAR images based on multi-channel deep learning semantic segmentation

inferred← citeshttps://doi.org/10.1016/j.marpolbul.2023.114981openalex-snapshot

An improved semantic segmentation model based on SVM for marine oil spill detection using SAR image

inferred← citeshttps://doi.org/10.1016/j.marpolbul.2024.116475openalex-snapshot

Automated marine oil spill detection algorithm based on single-image generative adversarial network and YOLO-v8 under small samples

inferred← citeshttps://doi.org/10.1016/j.marpolbul.2024.116549openalex-snapshot

Marine oil spill detection and segmentation in SAR data with two steps Deep Learning framework

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Deep learning-based hyperspectral oil spill detection for marine pollution monitoring in the Gulf of Mexico: A step toward marine pollution monitoring and SDG 14 compliance

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GSSNet: Gated selective scan network for port oil spill pollution detection

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CoRemoteSAM-Oil: A method for marine oil spill pollution detection through cross-domain knowledge transfer and integration

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NRGAN: A Noise-resilient GAN with adaptive feature modulation for SAR image segmentation

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Methodology for oil film identification and feature extraction based on remote sensing data acquisition experiment

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Pan-Arctic winter sea-ice lead mapping at 80-m resolution from Sentinel-1 SAR using LeadNet: product generation and morphological insights

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Deep neural network for oil spill detection using Sentinel-1 data: application to Egyptian coastal regions

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Research on Deep Learning-based Cross-disciplinary Applications

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Oil Spill Detection using Deep Learning and CNN: Polarimetric Scattering Information from S-1 SAR Images

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An Alternating Mamba Selective Mechanism GANs for Oil Spill Semantic Segmentation

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DEGAN: A Deep Learning Model for SAR Image Segmentation in Aquacultural Raft Monitoring

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DMAU-Net: A Novel Semantic Segmentation Model for Oil Spill Detection

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Oil Spill SAR Image Segmentation via Probability Distribution Modeling

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Multitask GANs for Oil Spill Classification and Semantic Segmentation Based on SAR Images

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Clustering of Marine Oil-Spill Extent Using Sentinel-1 Dual-Polarimetric Scattering Spectrum

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SSWT-UUNet++ Submarine Oil Pipeline Spill Detection Approach

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MiSSNet: Memory-Inspired Semantic Segmentation Augmentation Network for Class-Incremental Learning in Remote Sensing Images

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SRCNet: Seminal Image Representation Collaborative Network for Oil Spill Segmentation in SAR Imagery

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TransOilSeg : A Novel SAR Oil Spill Detection Method Addressing Data Limitations and Look-Alike Confusions

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Annotation-Free, High-Fidelity SAR Oil-Spill Image Synthesis via Classification-Guided Diffusion Model

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BAGAN: Semantic Segmentation of Oil Spills in Optical Remote Sensing Images Based on Bottleneck Attention Module and Generative Adversarial Networks

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Detection of offshore oil slicks utilizing a novel joint feature extraction technique that integrates limited hyperspectral image data

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Fuzzy inference and machine learning-based prediction with a small dataset for oil spills in the geological environment

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Deep Learning-Based Hyperspectral Oil Spill Detection for Marine Pollution Monitoring in the Gulf of Mexico: A Step Marine Pollution Monitoring and Sdg 14 Compliance

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A few-shot oil spill segmentation network guided by multi-scale feature similarity modeling

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Applying Fuzzy Inference and Machine Learning Methods for Prediction with a Small Dataset: A Case Study for Predicting the Consequences of Oil Spills on a Ground Environment

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Novel Hybrid Feature Engineering with Optimized BAS Algorithm for Shipborne Radar Marine Oil Spill Detection

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