Alternatives to Target Entropy and Alpha Angle in SAR Polarimetry

Praks, Jaan; Colin-Koeniguer, Élise; Hallikainen, M.

doi:10.1109/tgrs.2009.2013459

Cited by 69 publications

(45 citation statements)

References 13 publications

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“…The first pair corroborates quite well the theoretical predictions of ( [48], [III.B]). In fact, since…”

Section: Mutual Information Relevance and Redundancy Of Featuressupporting

confidence: 87%

“…From S 2 and S 3 we derived a number of features, inspired by [54] and by [48]: Copolarization power ratio:…”

Section: Polarimetric Featuresmentioning

confidence: 99%

“…The analyses of [46,47] qualitatively examined the plausibility of the different outcomes of manual and automatic classification approaches. Our work has a different focus by contributing first a comprehensive quantification of the information content of Eigen-decomposition based polarimetric features (H/A/α) and other polarimetric features derived from the scattering matrix (see [48]) in X-band and in C-band individually. Afterwards, the two results are compared in the overlapping portions of the C-band and X-band images.…”

Section: Introductionmentioning

confidence: 99%

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Comparing Near Coincident Space Borne C and X Band Fully Polarimetric SAR Data for Arctic Sea Ice Classification

Ressel

Singha

2016

Remote Sensing

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This work compares the polarimetric backscatter behavior of sea ice in spaceborne X-band and C-band Synthetic Aperture Radar (SAR) imagery. Two spatially and temporally coincident pairs of fully polarimetric acquisitions from the TerraSAR-X/TanDEM-X and RADARSAT-2 satellites are investigated. Proposed supervised classification algorithm consists of two steps: The first step comprises a feature extraction, the results of which are ingested into a neural network classifier in the second step. Based on the common coherency and covariance matrix, we extract a number of features and analyze the relevance and redundancy by means of mutual information for the purpose of sea ice classification. Coherency matrix based features which require an eigendecomposition are found to be either of low relevance or redundant to other covariance matrix based features, which makes coherency matrix based features dispensable for the purpose of sea ice classification. Among the most useful features for classification are matrix invariant based features (Geometric Intensity, Scattering Diversity, Surface Scattering Fraction). This analysis reveals analogous results for all four acquisitions, in both X-band and C-band frequencies. The subsequent classification produces similarly promising results for all four acquisitions. In particular, the overlapping image portions exhibit a reasonable congruence of detected ice types.

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“…The first pair corroborates quite well the theoretical predictions of ( [48], [III.B]). In fact, since…”

Section: Mutual Information Relevance and Redundancy Of Featuressupporting

confidence: 87%

“…From S 2 and S 3 we derived a number of features, inspired by [54] and by [48]: Copolarization power ratio:…”

Section: Polarimetric Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparing Near Coincident Space Borne C and X Band Fully Polarimetric SAR Data for Arctic Sea Ice Classification

Ressel

Singha

2016

Remote Sensing

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“…In addition, as pointed out in [12], the parameter α obtained from the Cloude and Pottier decomposition is closely related to retardance R in some special cases, and entropy H can be relevant to the depolarization power of the Mueller matrix. The relationships between these parameters are notable theoretical problems for future works.…”

Section: Performance Demonstrationmentioning

confidence: 88%

Pol-SAR Classification Based on Generalized Polar Decomposition of Mueller Matrix

Wang

Zhang

Turnbull

et al. 2016

IEEE Geosci. Remote Sensing Lett.

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Abstract-In this letter, we investigate an application of a generalized polar decomposition of the Mueller matrix for polarimetric synthetic aperture radar (Pol-SAR) classification. Six rollinvariant parameters (diattenuation, retardance, polarization power, depolarization anisotropy, depolarization power, and transmittance) are selected as features for the classification of scattering types. Experimental results using the AIRSAR data over Flevoland show that, for most field types, the D−R−A Δ − Δ−m 00 set provides the highest classification accuracy, followed by the D−R−P Δ −Δ−m 00 set which also provides better accuracy than the widely used H −α−(δ −γ)−A−span set. The proposed method would be valuable for Pol-SAR interpretation.

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“…The depolarization index is defined as the Euclidian distance of the normalized Mueller matrix to the ideal depolarizer of the nonreciprocal case [Praks09]. D(M) can be expressed in terms of elements of the coherency matrix as follows:…”

Section: Depolarization Indexmentioning

confidence: 99%

Advancements in Estimating Crop Growth Stages Using Radarsat-2 and Terrasar-X Polarimetric Data

Λαμπρόπουλος

Liu

2015

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

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ABSTRACT:This paper uses RADARSAT-2 quad Polarimetric Synthetic Aperture Radar (PolSAR) and TerraSAR-X dual polarimetric SAR data to monitor agriculture crop growth stages. Two RADARSAT-2 Fine Quad Wide (FQW) beam modes FQ2W and FQ10W, each with 5 sets of data and 13 sets of Stripmap TerraSAR-X data were used in the study. Both RADARSAT-2 POLSAR data and TerraSAR-X data were acquired in summer 2012 outside Winnipeg, Manitoba, Canada. The study was carried out to two crop types: canola and wheat, each contains 5 regions of interest from ground truth crop classification map in the image scene. Polarimetric features such as differential reflectivity bands ratio, entropy, anisotropy, alpha angle, lambda, scattering diversity and polarization index were evaluated for two crop types. The results from both RADARSAT-2 and TerraSAR-X data were compared and they demonstrated clear relationships between crop growth stages and polarimetric parameters. It is observed that entropy, lambda and differential reflectivity from both data have similar responses to crop growth stages in their common coverage period. The results were also validated using ground truth information.

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Alternatives to Target Entropy and Alpha Angle in SAR Polarimetry

Cited by 69 publications

References 13 publications

Comparing Near Coincident Space Borne C and X Band Fully Polarimetric SAR Data for Arctic Sea Ice Classification

Comparing Near Coincident Space Borne C and X Band Fully Polarimetric SAR Data for Arctic Sea Ice Classification

Pol-SAR Classification Based on Generalized Polar Decomposition of Mueller Matrix

Advancements in Estimating Crop Growth Stages Using Radarsat-2 and Terrasar-X Polarimetric Data

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