Decision Fusion of Deep Learning and Shallow Learning for Marine Oil Spill Detection
Published 2022 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Decision Fusion of Deep Learning and Shallow Learning for Marine Oil Spill Detection
Authors
Keywords
-
Journal
Remote Sensing
Volume 14, Issue 3, Pages 666
Publisher
MDPI AG
Online
2022-01-31
DOI
10.3390/rs14030666
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Optimal feature selection based speech emotion recognition using two‐stream deep convolutional neural network
- (2021) Mustaqeem et al. INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
- A novel nonlinear hyperspectral unmixing approach for images of oil spills at sea
- (2020) Ying Li et al. INTERNATIONAL JOURNAL OF REMOTE SENSING
- Accurate extraction of offshore raft aquaculture areas based on a 3D-CNN model
- (2020) Zongchen Jiang et al. INTERNATIONAL JOURNAL OF REMOTE SENSING
- Characterization analysis and identification of common marine oil spill types using hyperspectral remote sensing
- (2020) Junfang Yang et al. INTERNATIONAL JOURNAL OF REMOTE SENSING
- Optical interpretation of oil emulsions in the ocean – Part II: Applications to multi-band coarse-resolution imagery
- (2020) Yingcheng Lu et al. REMOTE SENSING OF ENVIRONMENT
- Advances in Remote Sensing Technology, Machine Learning and Deep Learning for Marine Oil Spill Detection, Prediction and Vulnerability Assessment
- (2020) Shamsudeen Temitope Yekeen et al. Remote Sensing
- Inversion of the Thickness of Crude Oil Film Based on an OG-CNN Model
- (2020) Zongchen Jiang et al. Journal of Marine Science and Engineering
- Oil Film Classification Using Deep Learning-Based Hyperspectral Remote Sensing Technology
- (2019) Xueyuan Zhu et al. ISPRS International Journal of Geo-Information
- Hyperspectral Coastal Wetland Classification Based on a Multiobject Convolutional Neural Network Model and Decision Fusion
- (2019) Yabin Hu et al. IEEE Geoscience and Remote Sensing Letters
- Oil Spill Hyperspectral Remote Sensing Detection Based on DCNN with Multi-Scale Features
- (2019) Jun-Fang Yang et al. JOURNAL OF COASTAL RESEARCH
- Optical interpretation of oil emulsions in the ocean – Part I: Laboratory measurements and proof-of-concept with AVIRIS observations
- (2019) Yingcheng Lu et al. REMOTE SENSING OF ENVIRONMENT
- VPRS-Based Regional Decision Fusion of CNN and MRF Classifications for Very Fine Resolution Remotely Sensed Images
- (2018) Ce Zhang et al. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
- Unique oil spill in East China Sea frustrates scientists
- (2018) Cally Carswell NATURE
- Comparing the Potential of Multispectral and Hyperspectral Data for Monitoring Oil Spill Impact
- (2018) Shruti Khanna et al. SENSORS
- Evaluation of the Ability of Spectral Indices of Hydrocarbons and Seawater for Identifying Oil Slicks Utilizing Hyperspectral Images
- (2018) Dong Zhao et al. Remote Sensing
- A fusion approach to classify hyperspectral oil spill data
- (2018) Jacintha Menezes et al. MULTIMEDIA TOOLS AND APPLICATIONS
- A New Endmember Preprocessing Method for the Hyperspectral Unmixing of Imagery Containing Marine Oil Spills
- (2017) Can Cui et al. ISPRS International Journal of Geo-Information
- Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks
- (2016) Yushi Chen et al. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
- Deep Learning Based Feature Selection for Remote Sensing Scene Classification
- (2015) Qin Zou et al. IEEE Geoscience and Remote Sensing Letters
- Spectral–spatial classification of hyperspectral images using deep convolutional neural networks
- (2015) Jun Yue et al. Remote Sensing Letters
- Deep Convolutional Neural Networks for Hyperspectral Image Classification
- (2015) Wei Hu et al. Journal of Sensors
- Review of oil spill remote sensing
- (2014) Merv Fingas et al. MARINE POLLUTION BULLETIN
- Decision Fusion in Kernel-Induced Spaces for Hyperspectral Image Classification
- (2013) Wei Li et al. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
- Progress in Marine Oil Spill Optical Remote Sensing: Detected Targets, Spectral Response Characteristics, and Theories
- (2013) Yingcheng Lu et al. MARINE GEODESY
- Determining oil slick thickness using hyperspectral remote sensing in the Bohai Sea of China
- (2012) Yingcheng Lu et al. International Journal of Digital Earth
- State of the art satellite and airborne marine oil spill remote sensing: Application to the BP Deepwater Horizon oil spill
- (2012) Ira Leifer et al. REMOTE SENSING OF ENVIRONMENT
- Acoustic Modeling Using Deep Belief Networks
- (2011) Abdel-rahman Mohamed et al. IEEE Transactions on Audio Speech and Language Processing
- Decision-Level Fusion of Spectral Reflectance and Derivative Information for Robust Hyperspectral Land Cover Classification
- (2010) Hemanth Reddy Kalluri et al. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
- Decision Fusion for the Classification of Hyperspectral Data: Outcome of the 2008 GRS-S Data Fusion Contest
- (2009) G. Licciardi et al. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
- Assessing the effect of hydrocarbon oil type and thickness on a remote sensing signal: A sensitivity study based on the optical properties of two different oil types and the HYMAP and Quickbird sensors
- (2009) Magnus Wettle et al. REMOTE SENSING OF ENVIRONMENT
- Classification of hyperspectral remote-sensing data with primal SVM for small-sized training dataset problem
- (2008) Mingmin Chi et al. ADVANCES IN SPACE RESEARCH
- Comparing Statistical and Neural Network Methods Applied to Very High Resolution Satellite Images Showing Changes in Man-Made Structures at Rocky Flats
- (2008) M. Chini et al. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Publish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn MoreBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started