Scattering Mechanism Extraction by a Modified Cloude-Pottier Decomposition for Dual Polarization SAR

Ji, Kefeng; Wu, Yonghui

doi:10.3390/rs70607447

Cited by 76 publications

(57 citation statements)

References 32 publications

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“…Therefore, the selection of optimal threshold values should be based on a trade-off between the over-or under-estimation of burnt area. The high commission errors observed on Sentinel-1A NDαI burnt area results are likely due to the inability of the α-angle from the modified H-α decomposition to effectively separate scattering mechanisms when one like-polarised and one cross-polarised band is used [33]. In fact, the lack of co-polarisation was found to degrade the ability to extract scattering mechanisms with medium-and low-entropy scattering mechanisms being highly confused [33].…”

Section: Discussion Of the Resultsmentioning

confidence: 99%

“…The high commission errors observed on Sentinel-1A NDαI burnt area results are likely due to the inability of the α-angle from the modified H-α decomposition to effectively separate scattering mechanisms when one like-polarised and one cross-polarised band is used [33]. In fact, the lack of co-polarisation was found to degrade the ability to extract scattering mechanisms with medium-and low-entropy scattering mechanisms being highly confused [33]. Furthermore, high-entropy scattering mechanisms were found to be confused by medium-entropy and multiple scattering mechanisms [33].…”

Section: Discussion Of the Resultsmentioning

confidence: 99%

“…In fact, the lack of co-polarisation was found to degrade the ability to extract scattering mechanisms with medium-and low-entropy scattering mechanisms being highly confused [33]. Furthermore, high-entropy scattering mechanisms were found to be confused by medium-entropy and multiple scattering mechanisms [33]. To further investigate the commission errors on dual-polarisation data, the areas associated with high commission errors were compared to the land-cover map of the area [39].…”

Section: Discussion Of the Resultsmentioning

confidence: 99%

“…Therefore, the α-angle can be exploited to extract information on dominant scattering mechanisms in vegetated areas. Although the H-A-α decomposition is generally applied to quad-polarisation data, a modified H-A-α decomposition has also been developed for dual-polarisation data [26,32,33].…”

Section: The Potential For Sar Data Analysis For Burn-scar Detectionmentioning

confidence: 99%

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A Simple Normalized Difference Approach to Burnt Area Mapping Using Multi-Polarisation C-Band SAR

Engelbrecht

Theron

Vhengani³

et al. 2017

Remote Sensing

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Abstract:In fire-prone ecosystems, periodic fires are vital for ecosystem functioning. Fire managers seek to promote the optimal fire regime by managing fire season and frequency requiring detailed information on the extent and date of previous burns. This paper investigates a Normalised Difference α-Angle (NDαI) approach to burn-scar mapping using C-band data. Polarimetric decompositions are used to derive α-angles from pre-burn and post-burn scenes and NDαI is calculated to identify decreases in vegetation between the scenes. The technique was tested in an area affected by a wildfire in January 2016 in the Western Cape, South Africa. The quad-pol H-A-α decomposition was applied to RADARSAT-2 data and the dual-pol H-α decomposition was applied to Sentinel-1A data. The NDαI results were compared to a burn scar extracted from Sentinel-2A data. High overall accuracies of 97.4% (Kappa = 0.72) and 94.8% (Kappa = 0.57) were obtained for RADARSAT-2 and Sentinel-1A, respectively. However, large omission errors were found and correlated strongly with areas of high local incidence angle for both datasets. The combined use of data from different orbits will likely reduce these errors. Furthermore, commission errors were observed, most notably on Sentinel-1A results. These errors may be due to the inability of the dual-pol H-α decomposition to effectively distinguish between scattering mechanisms. Despite these errors, the results revealed that burnt areas could be extracted and were in good agreement with the results from Sentinel-2A. Therefore, the approach can be considered in areas where persistent cloud cover or smoke prevents the extraction of burnt area information using conventional multispectral approaches.

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Section: Discussion Of the Resultsmentioning

confidence: 99%

Section: Discussion Of the Resultsmentioning

confidence: 99%

Section: Discussion Of the Resultsmentioning

confidence: 99%

Section: The Potential For Sar Data Analysis For Burn-scar Detectionmentioning

confidence: 99%

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A Simple Normalized Difference Approach to Burnt Area Mapping Using Multi-Polarisation C-Band SAR

Engelbrecht

Theron

Vhengani³

et al. 2017

Remote Sensing

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“…where α is a parameter of H/α decomposition (Ji et al, 2015). It represents average scattering angle, it can be written as:…”

Section: Model-based Decomposition Of T2mentioning

confidence: 99%

A Novel Two-Component Decomposition for Co-Polar Channels of Gf-3 Quad-Pol Data

Kwok

Zhao

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission Ⅲ, WG Ⅲ/2 KEY WORDS: GF-3 satellite, Model-based decomposition, Two-component decomposition, Co-polar data, PolSAR ABSTRACT:Polarimetric target decomposition theory is the most dynamic and exploratory research area in the field of PolSAR. But most methods of target decomposition are based on fully polarized data (quad pol) and seldom utilize dual-polar data for target decomposition. Given this, we proposed a novel two-component decomposition method for co-polar channels of GF-3 quad-pol data. This method decomposes the data into two scattering contributions: surface, double bounce in dual co-polar channels. To save this underdetermined problem, a criterion for determining the model is proposed. The criterion can be named as second-order averaged scattering angle, which originates from the H/α decomposition. and we also put forward an alternative parameter of it. To validate the effectiveness of proposed decomposition, Liaodong Bay is selected as research area. The area is located in northeastern China, where it grows various wetland resources and appears sea ice phenomenon in winter. and we use the GF-3 quad-pol data as study data, which which is China's first C-band polarimetric synthetic aperture radar (PolSAR) satellite. The dependencies between the features of proposed algorithm and comparison decompositions (Pauli decomposition, An&Yang decomposition, Yamaguchi S4R decomposition) were investigated in the study. Though several aspects of the experimental discussion, we can draw the conclusion: the proposed algorithm may be suitable for special scenes with low vegetation coverage or low vegetation in the non-growing season; proposed decomposition features only using co-polar data are highly correlated with the corresponding comparison decomposition features under quad-polarization data. Moreover, it would be become input of the subsequent classification or parameter inversion.

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