Application of the Matrix-Variate Mellin Transform to Analysis of Polarimetric Radar Images

Anfinsen, Stian Normann; Eltoft, T.

doi:10.1109/tgrs.2010.2103945

Cited by 115 publications

(189 citation statements)

References 37 publications

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“…Shape parameter α can be estimated using the method of matrix log-cumulants, which has been proposed in the literature [39]. However, given that this method is more complex, needing a large amount of computation, and that this calculation is performed for each image object in each iteration of the segmentation, we estimate shape parameter α using the method of Doulgeris, provided in the literature [40].…”

Section: Parameter Estimationmentioning

confidence: 99%

Multi-Feature Segmentation for High-Resolution Polarimetric SAR Data Based on Fractal Net Evolution Approach

Chen

et al. 2017

Remote Sensing

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Segmentation techniques play an important role in understanding high-resolution polarimetric synthetic aperture radar (PolSAR) images. PolSAR image segmentation is widely used as a preprocessing step for subsequent classification, scene interpretation and extraction of surface parameters. However, speckle noise and rich spatial features of heterogeneous regions lead to blurred boundaries of high-resolution PolSAR image segmentation. A novel segmentation algorithm is proposed in this study in order to address the problem and to obtain accurate and precise segmentation results. This method integrates statistical features into a fractal net evolution algorithm (FNEA) framework, and incorporates polarimetric features into a simple linear iterative clustering (SLIC) superpixel generation algorithm. First, spectral heterogeneity in the traditional FNEA is substituted by the G 0 distribution statistical heterogeneity in order to combine the shape and statistical features of PolSAR data. The statistical heterogeneity between two adjacent image objects is measured using a log likelihood function. Second, a modified SLIC algorithm is utilized to generate compact superpixels as the initial samples for the G 0 statistical model, which substitutes the polarimetric distance of the Pauli RGB composition for the CIELAB color distance. The segmentation results were obtained by weighting the G 0 statistical feature and the shape features, based on the FNEA framework. The validity and applicability of the proposed method was verified with extensive experiments on simulated data and three real-world high-resolution PolSAR images from airborne multi-look ESAR, spaceborne single-look RADARSAT-2, and multi-look TerraSAR-X data sets. The experimental results indicate that the proposed method obtains more accurate and precise segmentation results than the other methods for high-resolution PolSAR images.

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Section: Parameter Estimationmentioning

confidence: 99%

Multi-Feature Segmentation for High-Resolution Polarimetric SAR Data Based on Fractal Net Evolution Approach

Chen

et al. 2017

Remote Sensing

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“…MoMLC has been widely used and proven to be an effective estimation technique with respect to the PolSAR product model. The following equation is utilized to estimate ENL [39]: The correct form of the K distribution is decided by the appropriate estimation of the parameters. In this distribution model, the scale matrix Σ is the expectation of the coherency matrix, and the estimated valueΣ can be calculated by the sample average as follows:…”

Section: Polsar K Distributionmentioning

confidence: 99%

“…However, this method ignores the polarimetric information and is unreliable, whereas the direct use of the coherency matrix or covariance matrix obtains good results. Consequently, the method of matrix log-cumulants (MoMLC) [39] is adopted to estimate ENL and shape parameter α in our method. MoMLC has been widely used and proven to be an effective estimation technique with respect to the PolSAR product model.…”

Section: Polsar K Distributionmentioning

confidence: 99%

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Superpixel-Based Classification Using K Distribution and Spatial Context for Polarimetric SAR Images

Chen

Yang

et al. 2016

Remote Sensing

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Classification techniques play an important role in the analysis of polarimetric synthetic aperture radar (PolSAR) images. PolSAR image classification is widely used in the fields of information extraction and scene interpretation or is performed as a preprocessing step for further applications. However, inherent speckle noise of PolSAR images hinders its application on further classification. A novel supervised superpixel-based classification method is proposed in this study to suppress the influence of speckle noise on PolSAR images for the purpose of obtaining accurate and consistent classification results. This method combines statistical information with spatial context information based on the stochastic expectation maximization (SEM) algorithm. First, a modified simple linear iterative clustering (SLIC) algorithm is utilized to generate superpixels as classification elements. Second, class posterior probabilities of superpixels are calculated by a K distribution in iterations of SEM. Then, a neighborhood function is defined to express the spatial relationship among adjacent superpixels quantitatively, and the class posterior probabilities are updated by this predefined neighborhood function in a probabilistic label relaxation (PLR) procedure. The final classification result is obtained by the maximum a posteriori decision rule. A simulated image, a spaceborne RADARSAT-2 image, and an airborne AIRSAR image are used to evaluate the proposed method, and the classification accuracy of our proposed method is 99.28%, 93.16% and 89.70%, respectively. The experimental results indicate that the proposed method obtains more accurate and consistent results than other methods.

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“…2 In remote sensing, it provides more choices to discriminate the targets. 3 In environmental protection, it can detect oil spill over the sea. 4 With the increasing demand for more compact size and higher alignment precision of polarimetric sensor, it attracts a growing attention to integrate wire grid polarizer on the sensor surface.…”

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confidence: 99%

The simulation of localized surface plasmon and surface plasmon polariton in wire grid polarizer integrated on InP substrate for InGaAs sensor

Wang

Shao

et al. 2015

AIP Advances

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We numerically demonstrate the integration of gold wire grid polarizer on InP substrate for InGaAs polarimetric imaging. The effective spectral range of wire grid polarizer has been designed in 0.8-3 μm according to InGaAs response waveband. The dips in TM transmission are observed due to surface plasmon (SPs) significantly damaging polarization performance. To further understand the coupling mechanism between gold wire grid grating and InP, the different contributions of surface plasmon polariton (SPP) and localized surface plasmon (LSP) to the dips are analyzed. Both transmission and reflectance spectra are simulated at different grating periods and duty cycles by finite-different time-domain (FDTD) method. LSP wavelength is located at around 1 μm and sensitive to the specific shape of metal wire. SPP presents higher resonance wavelength closely related to grating period. The simulations of electric field distribution show the same results.

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Application of the Matrix-Variate Mellin Transform to Analysis of Polarimetric Radar Images

Cited by 115 publications

References 37 publications

Multi-Feature Segmentation for High-Resolution Polarimetric SAR Data Based on Fractal Net Evolution Approach

Multi-Feature Segmentation for High-Resolution Polarimetric SAR Data Based on Fractal Net Evolution Approach

Superpixel-Based Classification Using K Distribution and Spatial Context for Polarimetric SAR Images

The simulation of localized surface plasmon and surface plasmon polariton in wire grid polarizer integrated on InP substrate for InGaAs sensor

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