A Modified Four-Component Decomposition Method With Refined Volume Scattering Models

UWScat, a ground-based Ku- and X-band scatterometer, was used to compare forested and non-forested landscapes in a terrestrial snow accumulation environment as part of the NASA SnowEx17 field campaign. Field observations from Trail Valley Creek, Northwest Territories; Tobermory, Ontario; and the Canadian Snow and Ice Experiment (CASIX) campaign in Churchill, Manitoba, were also included. Limited sensitivity to snow was observed at 9.6 GHz, while the forest canopy attenuated the signal from sub-canopy snow at 17.2 GHz. Forested landscapes were distinguishable using the volume scattering component of the Freeman–Durden three-component decomposition model by applying a threshold in which values ≥50% indicated forested landscape. It is suggested that the volume scattering component of the decomposition can be used in current snow water equivalent (SWE) retrieval algorithms in place of the forest cover fraction (FF), which is an optical surrogate for microwave scattering and relies on ancillary data. The performance of the volume scattering component of the decomposition was similar to that of FF when used in a retrieval scheme. The primary benefit of this method is that it provides a current, real-time estimate of the forest state, it automatically accounts for the incidence angle and canopy structure, and it provides coincident information on the forest canopy without the use of ancillary data or modeling, which is especially important in remote regions. Additionally, it enables the estimation of forest canopy transmissivity without ancillary data. This study also demonstrates the use of these frequencies in a forest canopy application, and the use of the Freeman–Durden three-component decomposition on scatterometer observations in a terrestrial snow accumulation environment.

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ALTERNATIVE TO FOUR-COMPONENT DECOMPOSITION FOR POLARIMETRIC SAR

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsannals-iii-7-207-2016 ◽

2016 ◽

Vol III-7 ◽

pp. 207-211

Author(s):

J. X. Zhang ◽

G. M. Huang ◽

J. J. Wei ◽

Z. Zhao

Keyword(s):

Diffuse Scattering ◽

Orientation Angle ◽

Decomposition Methods ◽

New Method ◽

Guizhou Province ◽

Eigenvalue Decomposition ◽

Similarity Parameter ◽

Volume Scattering ◽

Decomposition Scheme ◽

Component Decomposition

There are more unknowns than equations to solve for previous four-component decomposition methods. In this case, the nonnegative power of each scattering mechanism has to be determined with some assumptions and physical power constraints. This paper presents a new decomposition scheme, which models the measured matrix after polarimetric orientation angle (POA) compensation as a linear sum of five scattering mechanisms (i.e., odd-bounce scattering, double-bounce scattering, diffuse scattering, volume scattering, and helix scattering). And the volume scattering power is calculated by a slight modified NNED method, owing to this method considering the external volume scattering model from oblique dihedral structure. After the helix and volume scattering powers have been determined sequentially, the other three scattering powers are estimated by combining the generalized similarity parameter (GSP) and the eigenvalue decomposition. Among them, due to POA compensation, the diffuse scattering induced from a dihedral with a relative orientation of 45º has negligible scattering power. Thus, the new method can be reduced as four-component decomposition automatically. And then the ALOS-2 PolSAR data covering Guiyang City, Guizhou Province, China were used to evaluate the performance of the new method in comparison with some classical decomposition methods (i.e. Y4R, S4R and G4U).

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ALTERNATIVE TO FOUR-COMPONENT DECOMPOSITION FOR POLARIMETRIC SAR

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iii-7-207-2016 ◽

2016 ◽

Vol III-7 ◽

pp. 207-211 ◽

Cited By ~ 1

Author(s):

J. X. Zhang ◽

G. M. Huang ◽

J. J. Wei ◽

Z. Zhao

Keyword(s):

Diffuse Scattering ◽

Orientation Angle ◽

Decomposition Methods ◽

New Method ◽

Guizhou Province ◽

Eigenvalue Decomposition ◽

Similarity Parameter ◽

Volume Scattering ◽

Decomposition Scheme ◽

Component Decomposition

There are more unknowns than equations to solve for previous four-component decomposition methods. In this case, the nonnegative power of each scattering mechanism has to be determined with some assumptions and physical power constraints. This paper presents a new decomposition scheme, which models the measured matrix after polarimetric orientation angle (POA) compensation as a linear sum of five scattering mechanisms (i.e., odd-bounce scattering, double-bounce scattering, diffuse scattering, volume scattering, and helix scattering). And the volume scattering power is calculated by a slight modified NNED method, owing to this method considering the external volume scattering model from oblique dihedral structure. After the helix and volume scattering powers have been determined sequentially, the other three scattering powers are estimated by combining the generalized similarity parameter (GSP) and the eigenvalue decomposition. Among them, due to POA compensation, the diffuse scattering induced from a dihedral with a relative orientation of 45º has negligible scattering power. Thus, the new method can be reduced as four-component decomposition automatically. And then the ALOS-2 PolSAR data covering Guiyang City, Guizhou Province, China were used to evaluate the performance of the new method in comparison with some classical decomposition methods (i.e. Y4R, S4R and G4U).

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An Unsupervised Port Detection Method in Polarimetric SAR Images Based on Three-Component Decomposition and Multi-Scale Thresholding Segmentation

Remote Sensing ◽

10.3390/rs14010205 ◽

2022 ◽

Vol 14 (1) ◽

pp. 205

Author(s):

Chun Liu ◽

Jian Yang ◽

Jiangbin Zheng ◽

Xuan Nie

Keyword(s):

Detection Method ◽

Ground Truth ◽

Large Area ◽

Polarimetric Sar ◽

Sar Images ◽

Volume Scattering ◽

Multi Scale ◽

Component Decomposition ◽

Union Section ◽

Strong Scattering

It is difficult to detect ports in polarimetric SAR images due to the complicated components, morphology, and coastal environment. This paper proposes an unsupervised port detection method by extracting the water of the port based on three-component decomposition and multi-scale thresholding segmentation. Firstly, the polarimetric characteristics of the port water are analyzed using modified three-component decomposition. Secondly, the volume scattering power and the power ratio of the double-bounce scattering power to the volume scattering power (PRDV) are used to extract the port water. Water and land are first separated by a global thresholding segmentation of the volume scattering power, in which the sampling region used for the threshold calculation is automatically selected by a proposed homogeneity measure. The interference water regions in the ports are then separated from the water by segmenting the PRDV using the multi-scale thresholding segmentation method. The regions of interest (ROIs) of the ports are then extracted by determining the connected interference water regions with a large area. Finally, ports are recognized by examining the area ratio of strong scattering pixels to the land in the extracted ROIs. Seven single quad-polarization SAR images acquired by RADARSAT-2 covering the coasts of Dalian, Zhanjiang, Fujian, Tianjin, Lingshui, and Boao in China and Berkeley in America are used to test the proposed method. The experimental results show that all ports are correctly and quickly detected. The false alarm rates are zero, the intersection of union section (IoU) indexes between the detected port and the ground truth can reach 75%, and the average processing time can be less than 100 s.

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