Redevelopment of Kennaugh's target characteristic polarization state theory using the polarization transformation ration formalism for the coherent case

Agrawal, A.P.; Boerner, W.-M.

doi:10.1109/36.20269

Cited by 74 publications

(31 citation statements)

References 7 publications

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“…Target decomposition theorems were first formalized by Huynen but have their origin in the research work of Chandrasekhar on light scattering by small anisotropic particles [24]. At present, two theories of target decomposition can be distinguished: coherent target decomposition (CTD) and incoherent target decomposition (ICTD).…”

Section: Target Decomposition Theoremmentioning

confidence: 99%

“…This leads to association of physical mechanism with each independent component having physical constraints such as the average target being invariant to changes in wave polarization basis [24]. Thus, decomposition technique manipulates the elements of scattering matrix or their second order statistics with the objective to provide more descriptive and discriminative target parameters, which have influential significance in various applications of radar polarimetry [1].…”

Section: Target Decomposition Theoremmentioning

confidence: 99%

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Land Cover Classification of Palsar Images by Knowledge Based Decision Tree Classifier and Supervised Classifiers Based on Sar Observables

Mishra¹,

Singh²,

Yamaguchi³

2011

PIER B

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Abstract-The intent of this paper is to explore the application of information obtained from fully polarimetric data for land cover classification. Various land cover classification techniques are available in the literature, but still uncertainty exists in labeling various clusters to their own classes without using any a priori information. Therefore, the present work is focused on analyzing useful intrinsic information extracted from SAR observables obtained by various decomposition techniques. The eigenvalue decomposition and Pauli decomposition have been carried out to separate classes on the basis of their scattering mechanisms. The various classification techniques (supervised: minimum distance, maximum likelihood, parallelepiped and unsupervised: Wishart) were applied in order to see possible differences among SAR observables in terms of information that they contain and their usefulness in classifying particular land cover type. Another important issue is labeling the clusters, and this work is carried out by decision tree classification that uses knowledge based approach. This classifier is implemented by scrupulous knowledge of data obtained by empirical evidence and their experimental validation. It has been demonstrated quantitatively that standard polarimetric parameters such as polarized backscatter coefficients (linear, circular 48Mishra, Singh, and Yamaguchi and linear 45 • ), co and cross-pol ratios for both linear and circular polarizations can be used as information bearing features for making decision boundaries. This forms the basis of discrimination between various classes in sequential format. The classification approach has been evaluated for fully polarimetric ALOS PALSAR L-band level 1.1 data. The classifier uses these data to classify individual pixel into one of the five categories: water, tall vegetation, short vegetation, urban and bare soil surface. The quantitative results shown by this classifier give classification accuracy of about 86%, which is better than other classification techniques.

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Section: Target Decomposition Theoremmentioning

confidence: 99%

Section: Target Decomposition Theoremmentioning

confidence: 99%

Land Cover Classification of Palsar Images by Knowledge Based Decision Tree Classifier and Supervised Classifiers Based on Sar Observables

Mishra¹,

Singh²,

Yamaguchi³

2011

PIER B

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“…To understand the techniques, the concept of a polarization signature [van Zyl, 1985;Agrawal and Boerner, 1989;Boerner et al, 1998] is required. A polarization signature (which can be copolarization (copol), or cross-polarization (crosspol)) measures the backscattered terrain scatter as a function of two variables: orientation W and ellipticity Z.…”

Section: Measurement Of Slope-induced Orientation Changesmentioning

confidence: 99%

Terrain topography measurement using multipass polarimetric synthetic aperture radar data

Schuler¹,

Lee²,

Ainsworth³

et al. 2000

Radio Science

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Abstract. A method has been investigated for the measurement of topography using airborne fully polarimetric synthetic aperture radar (SAR) data. Terrain slopes in both the range and azimuthal directions have been estimated using multipass flight geometries. Using these slope values, the Poisson equation was then solved to create a Digital Elevation Model (DEM) of the terrain topography. The method measures polarimetric orientation angles which are then converted into terrain slopes in the azimuthal direction. The conversion of these orientation angles into terrain slopes requires additional knowledge of the radar look angle and the range direction terrain slopes. The solution for slopes is, therefore, a problem coupled between the range and azimuthal directions. For specialized multipass flight geometries these orthogonal terrain slopes are solved for, and maps of terrain slopes are produced. In particular, the processing of two-pass orthogonal and two-pass antiparallel (headings © and ©+ 180 ø) NASA -Jet Propulsion Laboratory airborne SAR data sets has been carried out for an area in central California. When orthogonal slopes are derived using either of these data sets, a digital elevation model may be generated. The L band, polarimetric SAR (POLSAR) DEM created by this solution is compared to a coregistered C band, interferometric SAR (IFSAR) DEM. Similar comparisons are made for terrain slopes in the azimuthal -range directions which are generated by the POLSAR and IFSAR elevation data. The polarimetric SAR, operating from an aircraft, or satellite, in a strip-mapping mode, is capable of measuring terrain topography for large areas provided that phase-preserving fully polarimetric data are taken. Polarimetric SAR data are also widely used for studies of crop classification, surface roughness, biomass density, and soil moisture content. All of these studies are adversely affected by scattering changes attributable to topography. The technique investigated here potentially offers a means to correct for these effects by making simultaneous, coregistered estimates of local orthogonal terrain slopes.

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“…Current visualization strategies for polarimetric radar data can be split into two broad categories. Firstly there are those which examine the full co-and cross-polarized response of individual covariance matrices, or averages of these matrices (e.g., [1][2][3]). Secondly, there OPEN ACCESS are techniques which attempt to apply standardized image processing techniques to polarimetric data sets in the form of grey scale intensity images (e.g., [4,5]), RGB composites (e.g., [6,7]) or IHS (Intensity, Hue, Saturation) composites (e.g., [8]).…”

Section: Introductionmentioning

confidence: 99%

An Icon-Based Synoptic Visualization of Fully Polarimetric Radar Data

Turner¹,

Woodhouse

2012

Remote Sensing

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Abstract:The visualization of fully polarimetric radar data is hindered by traditional remote sensing methodologies for displaying data due to the large number of parameters per pixel in such data, and the non-scalar nature of variables such as phase difference. In this paper, a new method is described that uses icons instead of image pixels to represent the image data so that polarimetric properties and geographic context can be visualized together. The icons are parameterized using the alpha-entropy decomposition of polarimetric data. The resulting image allows the following five variables to be displayed simultaneously: unpolarized power, alpha angle, polarimetric entropy, anisotropy and orientation angle. Examples are given for both airborne and laboratory-based imaging.

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Redevelopment of Kennaugh's target characteristic polarization state theory using the polarization transformation ration formalism for the coherent case

Cited by 74 publications

References 7 publications

Land Cover Classification of Palsar Images by Knowledge Based Decision Tree Classifier and Supervised Classifiers Based on Sar Observables

Land Cover Classification of Palsar Images by Knowledge Based Decision Tree Classifier and Supervised Classifiers Based on Sar Observables

Terrain topography measurement using multipass polarimetric synthetic aperture radar data

An Icon-Based Synoptic Visualization of Fully Polarimetric Radar Data

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