Semantic segmentation and segmentation refinement using machine learning case study: Water turbidity segmentation

Keyphrases

• Deep Learning 100%
• Machine Learning 100%
• Landsat 8 Imagery 100%
• Semantic Segmentation 100%
• Water Turbidity 100%
• Segmentation Refinement 100%
• Image Segmentation 66%
• U-Net 66%
• Processing Methods 33%
• Remote Sensing Image 33%
• Coastal Area 33%
• Generative Adversarial Networks 33%
• Deep Learning Architectures 33%
• Remote Sensing 33%
• Pixel Value 33%
• High Water 33%
• Object Detection 33%
• Maximum Likelihood 33%
• Computer-assisted Image Analysis 33%
• High Turbidity 33%
• Processing Time 33%
• Cluster Region 33%
• Image Recognition 33%
• Complementary Tools 33%
• Region Growing 33%
• Overlapping pixels 33%
• Inference Process 33%
• Prediction Process 33%
• Object Segmentation 33%
• Sensing Field 33%
• Computer Vision Analysis 33%

Earth and Planetary Sciences

• Turbidity 100%
• Machine Learning 100%
• Landsat 8 75%
• Remote Sensing 50%
• Image Analysis 25%
• Coastal Region 25%
• Computer Vision 25%

Engineering

• Deep Learning 100%
• Classical Method 50%
• Limitations 25%
• Image Analysis 25%
• Processing Time 25%
• Computervision 25%
• Processing Method 25%
• Coastal Area 25%
• Pixel Value 25%
• Maximum Likelihood 25%
• Object Recognition 25%
• Image Recognition 25%