Four-component scattering model for polarimetric SAR image decomposition

By Yoshio Yamaguchi, T. Moriyama, Motoi Ishido, Hiroyoshi Yamada. IEEE Transactions on Geoscience and Remote Sensing, 2005

1,205 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/12c2cd93a3a1fa67e03e89bf4bd2206565a5dc90

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Kazuo Ouchi
2. Irena Hajnsek
3. Wentao An
4. Yi Cui
5. Jian Yang
6. K. Papathanassiou
7. Qian Du
8. Yoshio Yamaguchi

In Environmental Engineering

1. Kazuo Ouchi
2. Irena Hajnsek
3. Wentao An
4. Yi Cui
5. Jian Yang
6. K. Papathanassiou
7. Shane R. Cloude

Citations in the corpus

• Three-Component Model-Based Decomposition for Polarimetric SAR Data (2010, methodology): “The principle of this constraint is similar to that used to deal with pixels with negative powers in [ 6 ].”
• Potential of Estimating Soil Moisture Under Vegetation Cover by Means of PolSAR (2009, background): “In [ 26 ], orientation effects of the dipoles forming the volume have been discussed.”
• Land Cover Classification of Palsar Images by Knowledge Based Decision Tree Classifier and Supervised Classifiers Based on SAR Observables (2011, background): “[28] who added forth term called helix scattering for representing off diagonal terms.”
• Analysis of different polarimetric target decomposition methods in forest density classification using C band SAR data (2016, background): “This limitation was overcome by Yamaguchi et al. (2005), who added a fourth term called helix scattering for representing off-diagonal terms.”
• Recent Trend and Advance of Synthetic Aperture Radar with Selected Topics (2013, methodology): “[104] extended the analysis to a four-component-scattering power decomposition by adding helix scattering.”
• More Diverse Means Better: Multimodal Deep Learning Meets Remote-Sensing Imagery Classification (2020, methodology): “2) MS-SAR LCZ data: The LCZ datasets are collected from Sentinel-2 and Sentinel-1 satellites, where the former acquires the MS data with 10 spectral bands and the latter is able to generate the dual-polarimetric SAR data organized as a commonly-used PolSAR covariance matrix (four components) [52].”