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Using similarity parameters for supervised polarimetric SAR image classification
Junyi Xu, Jian Yang, Yingning Peng, Chao Wang,
Yuei An Liou
太空及遙測研究中心
太空科學與工程學系
研究成果
:
雜誌貢獻
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期刊論文
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同行評審
10
引文 斯高帕斯(Scopus)
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Computer Science
Principal Components
100%
Component Analysis
100%
Image Classification
100%
Synthetic Aperture Radar Images
100%
Supervised Classification
100%
Scattering Matrix
100%
Distance Measure
50%
Synthetic Aperture Radar
50%
Feature Space
50%
classification result
50%
Decomposition Coefficient
50%
Keyphrases
Similarity Parameter
100%
Polarimetric Synthetic Aperture Radar Image
100%
Radar Image Classification
100%
Principal Coordinate Analysis (PCoA)
33%
Scattering Matrix
33%
Supervised Classification
33%
Target Scattering
33%
Distance Measure
16%
Received Power
16%
Classification Results
16%
Helix
16%
Feature Space
16%
Two-vector
16%
Synthetic Aperture Radar Imagery
16%
Cover Type
16%
Scattering Mechanism
16%
Corresponding Features
16%
Inner Product
16%
Decomposition Coefficient
16%
Feature Transform
16%
Polarimetric Synthetic Aperture Radar (PolSAR)
16%
Ground Cover
16%
Earth and Planetary Sciences
Synthetic Aperture Radar
100%
Principal Component Analysis
100%
Image Classification
100%
Supervised Classification
100%
S Matrix Theory
100%
Radar Data
50%
Ground Cover
50%
Engineering
Image Classification
100%
Synthetic Aperture Radar Images
100%
Similarity Parameter
100%
Principal Components
33%
Component Analysis
33%
Scattering Matrix
33%
Received Power
16%
Radar Data
16%
Feature Space
16%
Feature Transform
16%
Decomposition Coefficient
16%
Synthetic Aperture Radar
16%
Physics
Image Classification
100%
Synthetic Aperture Radar Images
100%
Distance Measure
100%
Synthetic Aperture Radar
100%
Mathematics
Matrix (Mathematics)
100%
Principal Component Analysis
100%
Feature Space
50%
Inner Product
50%