Improved scattering-model-based speckle filter in polarimetric SAR data with orientation angle compensation

2011 ◽  
Author(s):  
Lin Liu ◽  
Li-Ming Jiang ◽  
Hong-zhong Li ◽  
Jin-Xing Hu
Keyword(s):  
Orientation Angle ◽  
Polarimetric Sar ◽  
Scattering Model ◽  
Model Based ◽  
Sar Data

Scattering-model-based speckle filtering of polarimetric SAR data

2006 ◽  
Vol 44 (1) ◽  
pp. 176-187 ◽  
Author(s):  
Jong-Sen L ◽  
M.R. Grunes ◽  
D.L. Schuler ◽  
E. Pottier ◽  
L. Ferro-Famil
Keyword(s):  
Polarimetric Sar ◽  
Scattering Model ◽  
Model Based ◽  
Sar Data ◽  
Speckle Filtering

scholarly journals A Model-free Four Component Scattering Power Decomposition for Polarimetric SAR Data

2020 ◽  
Author(s):  
Subhadip Dey ◽  
Avik Bhattacharya ◽  
Alejandro C. Frery ◽  
Carlos López-Martínez
Keyword(s):  
Polarization State ◽  
Orientation Angle ◽  
Degree Of Polarization ◽  
Polarimetric Sar ◽  
Negative Power ◽  
Geometrical Properties ◽  
Clustering Technique ◽  
Model Based ◽  
Model Free ◽  
Sar Data

Target decomposition methods of polarimetric Synthetic Aperture Radar (PolSAR) data explain scattering information from a target. In this regard, several conventional model based methods utilize scattering power components to analyze polarimetric SAR data. However, the typical hierarchical process to enumerate power components uses various branching conditions, leading to several limitations. These techniques assume ad hoc scattering models within a radar resolution cell. Therefore, the use of several models makes the computation of scattering powers ambiguous. Some common issues of model-based decompositions are related to the compensation of the orientation angle about the radar line of sight and the negative power components’ occurrence. We propose a model-free four-component scattering power decomposition that alleviates these issues. In the proposed approach, we use the non-conventional 3D Barakat degree of polarization to obtain the scattered electromagnetic wave’s polarization state. The degree of polarization is used to obtain the even-bounce, odd-bounce, and diffused scattering power components. Along with this, a measure of target scattering asymmetry is also proposed, which is then suitably utilized to obtain the helicity power. All the power components are roll-invariant, nonnegative and unambiguous. In addition to this, we propose an unsupervised clustering technique that preserves the dominance of the scattering power components for different targets. This clustering technique assists in understanding the importance of diverse scattering mechanisms based on target characteristics. The technique adequately captures the clusters’ variations from one target to another according to their physical and geometrical properties. This study utilized two dual-frequency (i.e., C- and L-bands) polarimetric SAR data. These two data sets are used to show the decomposition powers’ effectiveness and the apparent interpretability of the clustering results.

A Generalized Volume Scattering Model-Based Vegetation Index From Polarimetric SAR Data

2019 ◽  
Vol 16 (11) ◽  
pp. 1791-1795 ◽  
Author(s):  
Debanshu Ratha ◽  
Dipankar Mandal ◽  
Vineet Kumar ◽  
Heather Mcnairn ◽  
Avik Bhattacharya ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Polarimetric Sar ◽  
Volume Scattering ◽  
Scattering Model ◽  
Model Based ◽  
Sar Data

scholarly journals A Model-free Four Component Scattering Power Decomposition for Polarimetric SAR Data

2021 ◽  
Author(s):  
Subhadip Dey ◽  
Avik Bhattacharya ◽  
Alejandro Frery ◽  
Carlos López-Martínez
Keyword(s):  
Polarization State ◽  
Orientation Angle ◽  
Degree Of Polarization ◽  
Polarimetric Sar ◽  
Negative Power ◽  
Geometrical Properties ◽  
Clustering Technique ◽  
Model Based ◽  
Model Free ◽  
Sar Data

Target decomposition methods of polarimetric Synthetic Aperture Radar (PolSAR) data explain scattering information from a target. In this regard, several conventional model based methods utilize scattering power components to analyze polarimetric SAR data. However, the typical hierarchical process to enumerate power components uses various branching conditions, leading to several limitations. These techniques assume ad hoc scattering models within a radar resolution cell. Therefore, the use of several models makes the computation of scattering powers ambiguous. Some common issues of model-based decompositions are related to the compensation of the orientation angle about the radar line of sight and the negative power components’ occurrence. We propose a model-free four-component scattering power decomposition that alleviates these issues. In the proposed approach, we use the non-conventional 3D Barakat degree of polarization to obtain the scattered electromagnetic wave’s polarization state. The degree of polarization is used to obtain the even-bounce, odd-bounce, and diffused scattering power components. Along with this, a measure of target scattering asymmetry is also proposed, which is then suitably utilized to obtain the helicity power. All the power components are roll-invariant, nonnegative and unambiguous. In addition to this, we propose an unsupervised clustering technique that preserves the dominance of the scattering power components for different targets. This clustering technique assists in understanding the importance of diverse scattering mechanisms based on target characteristics. The technique adequately captures the clusters’ variations from one target to another according to their physical and geometrical properties. This study utilized two dual-frequency (i.e., C- and L-bands) polarimetric SAR data. These two data sets are used to show the decomposition powers’ effectiveness and the apparent interpretability of the clustering results.

scholarly journals A Model-free Four Component Scattering Power Decomposition for Polarimetric SAR Data

2021 ◽  
Author(s):  
Subhadip Dey ◽  
Avik Bhattacharya ◽  
Alejandro Frery ◽  
Carlos López-Martínez
Keyword(s):  
Polarization State ◽  
Orientation Angle ◽  
Degree Of Polarization ◽  
Polarimetric Sar ◽  
Negative Power ◽  
Geometrical Properties ◽  
Clustering Technique ◽  
Model Based ◽  
Model Free ◽  
Sar Data

Target decomposition methods of polarimetric Synthetic Aperture Radar (PolSAR) data explain scattering information from a target. In this regard, several conventional model based methods utilize scattering power components to analyze polarimetric SAR data. However, the typical hierarchical process to enumerate power components uses various branching conditions, leading to several limitations. These techniques assume ad hoc scattering models within a radar resolution cell. Therefore, the use of several models makes the computation of scattering powers ambiguous. Some common issues of model-based decompositions are related to the compensation of the orientation angle about the radar line of sight and the negative power components’ occurrence. We propose a model-free four-component scattering power decomposition that alleviates these issues. In the proposed approach, we use the non-conventional 3D Barakat degree of polarization to obtain the scattered electromagnetic wave’s polarization state. The degree of polarization is used to obtain the even-bounce, odd-bounce, and diffused scattering power components. Along with this, a measure of target scattering asymmetry is also proposed, which is then suitably utilized to obtain the helicity power. All the power components are roll-invariant, nonnegative and unambiguous. In addition to this, we propose an unsupervised clustering technique that preserves the dominance of the scattering power components for different targets. This clustering technique assists in understanding the importance of diverse scattering mechanisms based on target characteristics. The technique adequately captures the clusters’ variations from one target to another according to their physical and geometrical properties. This study utilized two dual-frequency (i.e., C- and L-bands) polarimetric SAR data. These two data sets are used to show the decomposition powers’ effectiveness and the apparent interpretability of the clustering results.

scholarly journals A Model-free Four Component Scattering Power Decomposition for Polarimetric SAR Data

2020 ◽  
Author(s):  
Subhadip Dey ◽  
Avik Bhattacharya ◽  
Alejandro C. Frery ◽  
Carlos López-Martínez
Keyword(s):  
Polarization State ◽  
Orientation Angle ◽  
Degree Of Polarization ◽  
Polarimetric Sar ◽  
Negative Power ◽  
Geometrical Properties ◽  
Clustering Technique ◽  
Model Based ◽  
Model Free ◽  
Sar Data

Target decomposition methods of polarimetric Synthetic Aperture Radar (PolSAR) data explain scattering information from a target. In this regard, several conventional model based methods utilize scattering power components to analyze polarimetric SAR data. However, the typical hierarchical process to enumerate power components uses various branching conditions, leading to several limitations. These techniques assume ad hoc scattering models within a radar resolution cell. Therefore, the use of several models makes the computation of scattering powers ambiguous. Some common issues of model-based decompositions are related to the compensation of the orientation angle about the radar line of sight and the negative power components’ occurrence. We propose a model-free four-component scattering power decomposition that alleviates these issues. In the proposed approach, we use the non-conventional 3D Barakat degree of polarization to obtain the scattered electromagnetic wave’s polarization state. The degree of polarization is used to obtain the even-bounce, odd-bounce, and diffused scattering power components. Along with this, a measure of target scattering asymmetry is also proposed, which is then suitably utilized to obtain the helicity power. All the power components are roll-invariant, nonnegative and unambiguous. In addition to this, we propose an unsupervised clustering technique that preserves the dominance of the scattering power components for different targets. This clustering technique assists in understanding the importance of diverse scattering mechanisms based on target characteristics. The technique adequately captures the clusters’ variations from one target to another according to their physical and geometrical properties. This study utilized two dual-frequency (i.e., C- and L-bands) polarimetric SAR data. These two data sets are used to show the decomposition powers’ effectiveness and the apparent interpretability of the clustering results.

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