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MODIFIED U-NET BY ADDING TWIN EXTRACTORS FOR MULTI-SENSOR SATELLITE IMAGES FUSION TO MAP MANGROVE FOREST
Ilham Jamaluddin,
Ying Nong Chen
, Ilham Adi Panuntun
太空及遙測研究中心
資訊工程學系
研究成果
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深入研究「MODIFIED U-NET BY ADDING TWIN EXTRACTORS FOR MULTI-SENSOR SATELLITE IMAGES FUSION TO MAP MANGROVE FOREST」主題。共同形成了獨特的指紋。
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Keyphrases
U-Net
100%
Multi-sensor
100%
Mangrove Forest
100%
Satellite Image Fusion
100%
Extractor
100%
Mangrove
25%
Optical Aperture
25%
Mangrove Mapping
25%
Satellite Images
16%
Synthetic Aperture Radar Image
16%
Sentinel-2
16%
Intersection over Union
8%
Further Analysis
8%
Concatenated
8%
Process Evaluation
8%
Experiment Results
8%
F1 Score
8%
Florida
8%
Optical Satellite Images
8%
Satellite Imagery
8%
Synthetic Aperture Radar
8%
Deep Learning Algorithm
8%
Segmentation Algorithm
8%
Synthetic Aperture Radar Imagery
8%
Sentinel-1
8%
Coastal Zone
8%
Sentinel-1 Data
8%
Visual Interpretation
8%
Radar Satellites
8%
Machine Learning Learning
8%
Deep Machine Learning
8%
Non-mangrove
8%
Inception Module
8%
Mangrove Map
8%
Deep Learning Semantic Segmentation
8%
Rookery
8%
Global Mangrove Watch
8%
Forest Mapping
8%
Fused Features
8%
Physics
Image Fusion
100%
Synthetic Aperture Radar Images
100%
Sentinel-2
100%
Sentinel-1
100%
Satellite Sensor
100%
Deep Learning Method
100%
Synthetic Aperture Radar
100%
Machine Learning
50%
Material Science
Sentinel-2
100%