Cloude-Pottier Decomposition Research Articles

Comparison of three machine learning algorithms for retrieving soil moisture information from Sentinel-1A SAR data in northwest Shandong plain, China

Soil moisture (SM) plays a critical role in the growth and management of grain in semi-humid regions. However, little is known about how to integrate satellite data with machine learning to accurately retrieve SM information in these areas. This study compares the capability of three machine learning algorithms, Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN), to extract SM information over the Northwest Shandong Plain using multi-phase dual-polarized Sentinel-1A satellite data. The backscattering coefficients were obtained through standard intensity and phase processing to calculate the SAR indices, and several characteristic parameters were extracted as impact factors using the Cloude-Pottier decomposition. The importance of these factors was analyzed, while the performance of each machine learning algorithm was comprehensively evaluated using the K-fold cross-validation method. The best-performing model was utilized to retrieve the spatio-temporal changes in SM in the study area. The findings indicate the following: (1) The first eigenvalue has the greatest impact on retrieval accuracy, followed by entropy, where the intensity component of Shannon's entropy is more important than its polarization component; (2) The addition of more impact factors does not bring a continuous improvement in model performance, but the optimal factor combinations differ for different machine learning retrieval models; (3) The RF model trained using the IM12 combination demonstrates better performance than SVM and ANN in retrieving SM information, with a coefficient of determination (R2) of 0.55 and a root mean square error of 6.12 vol% on the validation set. The level of SM in the Yellow River National Wetland Park is higher than that of the surrounding areas, with substantial seasonal changes. Precipitation, temperature, and vegetation significantly influence the regional variations in SM at the macroscopic level.

Enhanced PGA for Dual-Polarized ISAR Imaging by Exploiting Cloude-Pottier Decomposition

Enhanced PGA for Dual-Polarized ISAR Imaging by Exploiting Cloude-Pottier Decomposition

A Polarimetric Decomposition and Copula Quantile Regression Approach for Soil Moisture Estimation From Radarsat-2 Data Over Vegetated Areas

This paper proposes a novel framework for probabilistic estimation of surface soil moisture (SSM) based on polarimetric decomposition and copula quantile regression, mainly focusing on solving the low correlation between synthetic aperture radar (SAR) backscattering coefficients and SSM in corn-covered areas. Cloude-Pottier decomposition and adaptive non-negative eigenvalue decomposition can extract more polarization parameters, explaining the implicit information in polarization data from different theoretical levels. Polarization parameters and the backscattering coefficients for different polarizations constitute predictor variable parameters for estimating the SSM. The dimensionality of the predictor variable parameters is reduced by supervised principal component analysis (SPCA) to derive the first principal component. SPCA ensures a high correlation between the first principal component and the SSM. Finally, the Archimedes copula function simply and effectively constructs the nonlinear relationship between SSM and the first principal component to complete the quantile regression estimation of SSM. Results show that the root-mean-square error (RMSE) range of SSM estimation is 0.039-0.078 cm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{3}$</tex-math></inline-formula> /cm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{3}$</tex-math></inline-formula> and the correlation coefficient (R) is 0.401-0.761. In addition, copula quantile regression constructs an uncertainty range for the SSM estimate, which can be used to judge the reliability of the estimate.

An insight into the sensitivity of fully polarimetric SAR data to biomass of pearl millet crop

The development of polarimetric decomposition techniques improves the capability to understand the complex scattering mechanisms occurring in the dryland crops. The present study is focused on assessing the sensitivity of coherent, incoherent, model and Eigen vector-based decomposition techniques from multitemporal Radarsat-2 polarimetric dataset to scattering mechanisms occurring in the critical growth stages of pearl millet crop. Yamaguchi volume component along with its temporal response was observed to be sensitive and reliable due to its dynamic biomass characterization. Overestimation of volume component and earlier saturation of biomass from 4 kgm−2 were observed limitations in Freeman 2 & 3 component decomposition when dealing with higher vegetation crops. The decrease in volume scattering over increasing biomass proved Van Zyl response contrary to the established understanding. Panicle stage was identified as the critical stage for discriminating pearl millet from similar structured maize and sorghum crops. In this stage, alpha angle generated from Cloude-Pottier decomposition almost reached 550 indicating the increase in contribution of dihedral scattering due to vertical prominent panicles in the pearl millet fields. The various Touzi parameters (α1, Ψ1, φ1 and τ1) were observed to have sensitivity towards changes in crop canopy structures, which will require further research to verify. The proposed Δ(F3-Y4) was the most suitable Volume Difference Index (VDI) for characterizing biomass of erectophile crops as the dynamic biomass response was found even beyond 8 kgm−2.

Investigation of Polarimetric Decomposition for Arctic Summer Sea Ice Classification Using Gaofen-3 Fully Polarimetric SAR Data

The aim of this article was to investigate the potential of polarimetric decomposition of Chinese Gaofen-3 (GF-3) C-band fully polarimetric synthetic aperture radar (PolSAR) data for Arctic sea ice classification during summer season. Five different polarimetric decomposition approaches, including the Cloude-Pottier decomposition (Cloude), the Freeman three-component decomposition (Freeman3), the Freeman three-component decomposition using the extended Bragg model (Freeman3X), the Yamaguchi three-component decomposition (Yamaguchi3), and the nonnegative eigenvalue decomposition (NNED) were analyzed using 35 scenes of GF-3 PolSAR data collected over the Fram Strait, Arctic from June 14–18, 2017. Polarimetric features extracted from these five methods were evaluated and utilized to train random forest classifiers to classify open water (calm water and rough water) and sea ice types (melted ice, unmelted ice, and deformed ice). The results show that NNED could ensure physically valid decomposed powers while the other three model-based decompositions had negative values. In terms of sea ice classification, NNED had the highest feature importance scores and achieved an overall accuracy and Kappa coefficient of about 86.18% and 0.82, respectively. Inclusion of radar incidence angle as a feature in the classifier could slightly improve the classification accuracy by about 3%. The influence of incidence angle on sea ice classification accuracy was also investigated and it was found that high incidence angles (39°–46°) were superior to low incidence angles (21°–27°) due to the overall higher accuracies.

Fusion of SAR Interferometry and Polarimetry Methods for Landslide Reactivation Study, the Bureya River (Russia) Event Case Study

In this paper, we demonstrate the estimation capabilities of landslide reactivation based on various SAR (Synthetic Aperture Radar) methods: Cloude-Pottier decomposition of Sentinel-1 dual polarimetry data, MT-InSAR (Multi-temporal Interferometric Synthetic Aperture Radar) techniques, and cloud computing of backscattering time series. The object of the study is the landslide in the east of Russia that took place on 11 December 2018 on the Bureya River. H-α-A polarimetric decomposition of C-band radar images not detected significant transformations of scattering mechanisms for the surface of the rupture, whereas L-band radar data show changes in scattering mechanisms before and after the main landslide. The assessment of ground displacements along the surface of the rupture in the 2019–2021 snowless periods was carried out using MT-InSAR methods. These displacements were 40 mm/year along the line of sight. The SBAS-InSAR results have allowed us to reveal displacements of great area in 2020 and 2021 snowless periods that were 30–40 mm/year along the line-of-sight. In general, the results obtained by MT-InSAR methods showed, on the one hand, the continuation of displacements along the surface of the rupture and on the other hand, some stabilization of the rate of landslide processes.

Monitoring crop growth using a canopy structure dynamic model and time series of synthetic aperture radar (SAR) data

ABSTRACT Normalized Difference Vegetation Index (NDVI) time series data are used by agricultural agencies for many essential operational crop monitoring programmes. But optical sensors often miss key growth stages due to cloud cover interference, impacting the performance of operational activities. Although the synergistic use of optical and Synthetic Aperture Radar (SAR) imagery can provide time series data, any SAR-optical integration necessitates building a relationship between these two data sources. The objective of this study was to use a semi-empirical Canopy Structure Dynamics Model (CSDM), Growing Degree Days (GDD), and SAR parameters calibrated to optical NDVI to derive daily estimates of canola crop condition over an entire growing season. RADARSAT-2 Fine Quad-pol and RapidEye images were collected over three years for a study site in western Canada. Object-based image analysis was applied to study the relationship between the optical and SAR time series data. Significant correlations were documented between a number of SAR parameters and optical NDVI, specifically a ratio of backscatter intensities (HH-HV)/(HH+HV), a ratio of volume to surface scattering extracted from the Freeman Durden decomposition, and Entropy from the Cloude-Pottier decomposition. Correlations (r-values) between these SAR parameters and optical NDVI ranged from 0.63 to 0.84 for the three years of data. Based on this analysis, a simple statistical model was used to relate SAR parameters to optical NDVI, creating a SAR-calibrated NDVI (SARcal-NDVI). A CSDM was fit to the SARcal-NDVI for each canola field, constructing a temporal vegetation index curve which captured canopy development from emergence to senescence. Coefficients of determination (R2) were 0.87 0.86 and 0.82 for entropy, the volume-surface scattering ratio, and the ratio of backscatter intensities (HH-HV)/(HH+HV), respectively, demonstrating a good model fit. The CSDM describes well the temporal evolution of SARcal-NDVI. Using the CSDM, SARcal-NDVI and GDD, the canola condition can be estimated for any given day in the growing season. In fact when the CSDM was used to estimate SARcal-NDVI for the exact days of RapidEye acquisitions, correlations with optically derived NDVI were high. The strongest correlations with RapidEye NDVI were reported for the volume-surface scattering ratio (R2 of 0.69 and RMSE of 0.15). The SARcal-NDVI estimated from the CSDM was also physically meaningful. Field-based biomass was significantly correlated (R2 of 0.79) with the SARcal-NDVI calculated using the volume-surface scattering ratio. Although further research is needed to extend this method to other crops, these results demonstrate that SAR data can be used to estimate vegetation conditions and when coupled with a CSDM, integrated into current monitoring operations based on optical NDVI. As a next step, the research team will be assessing SARcal-NDVI in a national operational programme which reports on crop yields using modelling with optical-based NDVI.

Detection of Actionable Oil using RADARSAT-2 and Simulated RADARSAT Constellation Mission Data

Abstract 688970 For oil spill response, one of the key parameters is detection of actionable oil. Actionable oil (AO), which tends to be thick, emulsified oil, refers to oil that can be cleaned-up versus non-actionable oil (sheen) which cannot be readily cleaned-up. Previous studies by MDA of a controlled oil spills in the North Sea have shown the capability of RADARSAT-2 quad polarized data to detect AO using the entropy parameter (H) derived from the Cloude-Pottier decomposition. H → 0 for oil-free water and H → 1 in the presence of an oil slick. To further test the detection of AO, RADARSAT-2, ASTER, WorldView imagery and in situ measurement were acquired April 25, 2017 at the MC20 site off Louisiana. Five oil thickness classes ranging from 1 micron to 100 microns were derived from a maximum likelihood classifier based on the ASTER and WorldView images and in situ samples. For oil-free water, the average H was 0.13 and 0.62 for the slick. There was good correlation between the variability of H and the oil thickness classes. Specifically, larger H was correlated with oil thickness in the 50 – 200 micron range and smaller H was correlated with oil in the 1 micro range. Not surprising there was overlap in H for area were the oil thickness was ~ 1 micron and the area deemed to be slick-free. The results indicate that actionable oil can be discriminated from non-actionable oil based on the relative difference of H. Although these results are encouraging, one of the operational limitations is based on the use of the relatively small swath-width (25 km – 50 km) of the RADARSAT-2 quad polarized mode. The impact of the swath-width can be mitigated with data from the RADARSAT Constellation Mission (RCM). The RCM has a compact polarimetry (CP) mode that provides more polarimetric information than dual polarized modes, less than quad polarized modes, but is available for swath widths up to 500 km. Analysis of simulated SC50 RCM data (50 m resolution, 350 km swath width) derived from the aforementioned RADARSAT-2 image shows similar oil-slick variability that was observed in the RADARSAT-2 image and hence the capability to detect AO.

Dryland Crop Classification Combining Multitype Features and Multitemporal Quad-Polarimetric RADARSAT-2 Imagery in Hebei Plain, China.

The accuracy of dryland crop classification using satellite-based synthetic aperture radar (SAR) data is often unsatisfactory owing to the similar dielectric properties that exist between the crops and their surroundings. The main objective of this study was to improve the accuracy of dryland crop (maize and cotton) classification by combining multitype features and multitemporal polarimetric SAR (PolSAR) images in Hebei plain, China. Three quad-polarimetric RADARSAT-2 scenes were acquired between July and September 2018, from which 117 features were extracted using the Cloude–Pottier, Freeman–Durden, Yamaguchi, and multiple-component polarization decomposition methods, together with two polarization matrices (i.e., the coherency matrix and the covariance matrix). Random forest (RF) and support vector machine (SVM) algorithms were used for classification of dryland crops and other land-cover types in this study. The accuracy of dryland crop classification using various single features and their combinations was compared for different imagery acquisition dates, and the performance of the two algorithms was evaluated quantitatively. The importance of all investigated features was assessed using the RF algorithm to optimize the features used and the imagery acquisition date for dryland crop classification. Results showed that the accuracy of dryland crop classification increases with evolution of the phenological period. In comparison with SVM, the RF algorithm showed better performance for dryland crop classification when using full polarimetric RADARSAT-2 data. Dryland crop classification accuracy was not improved substantially when using only backscattering intensity features or polarization decomposition parameters extracted from a single-date image. Satisfactory classification accuracy was achieved using 11 optimized features (derived from the Cloude–Pottier decomposition and the coherency matrix) from 2 RADARSAT-2 images (acquisition dates corresponding to the middle and late stages of dryland crop growth). This study provides an important reference for timely and accurate classification of dryland crop in Hebei plain, China.

Application of Radar Polarimetry to Monitor Changes in Backscattering Mechanisms in Landslide Zones Using the Example of the Collapse of the Bureya River Bank

Possibilities of using radar polarimetry methods for identifying landslide zones are analyzed. The transformation of the dominant mechanism of signal scattering by the reflecting surface was used as a key feature of landslide zones. The polarimetric data from the PALSAR-2 radar of the ALOS-2 satellite are processed using the Freeman–Durden and Cloude–Pottier decompositions at four test sites selected in the region of the landslide caused by the collapse of the bank of the Bureya River. It is found that the results of decompositions are consistent with each other; however, in some areas there are significant differences due to the specific features of the basic model assumptions. It is shown that, before the descent of the landslide masses, three main mechanisms of radar signal scattering existed in the analyzed region: single surface, volumetric, and double scattering. After the collapse, this area was dominated by single scattering characteristic of the reflective surface with large-scale irregularities free of vegetation, due to which the landslide descent zone can be confidently recognized. The significant potential of using radar polarimetry for remote diagnostics of the consequences of landslide phenomena has been demonstrated.

Unsupervised classification for PolSAR images based on multi-level feature extraction

ABSTRACT With the development of remote sensing systems, the scale of the imaging data grows rapidly, which highly requires appropriate adaptability for interpretation algorithms. Focusing on this trend, an unsupervised classification algorithm for polarimetric synthetic aperture radar (PolSAR) images is proposed based on multi-level feature extraction. The algorithm firstly generates an initial classification map by multi-level feature extraction, and then introduces Wishart classifier into the iterative classification to refine the initial. At the first level, the PolSAR image is classified into four categories by combining entropy and anisotropy features that are extracted from Cloude-Pottier decomposition. From the scattering mechanisms, the second-level classification is conducted with the surface, double-bounce and volume scattering power obtained from three-component decompression. Accordingly, the PolSAR image is further divided into 13 categories. Finally, to discriminate objects with similar polarimetric characteristics but different scattering power, the total scattering power is adopted to classify the PolSAR image into 26 categories at the third level. Experiments on some real PolSAR images acquired by AIRSAR system demonstrate the effectiveness of the proposed method both qualitatively and quantitatively.

On the Use of Neumann Decomposition for Crop Classification Using Multi-Temporal RADARSAT-2 Polarimetric SAR Data

In previous studies, parameters derived from polarimetric target decompositions have proven as very effective features for crop classification with single/multi-temporal polarimetric synthetic aperture radar (PolSAR) data. In particular, a classical eigenvalue-eigenvector-based decomposition approach named after Cloude–Pottier decomposition (or “H/A/α”) has been frequently used to construct classification approaches. A model-based decomposition approach proposed by Neumann some years ago provides two parameters with very similar physical meanings to polarimetric scattering entropy H and the alpha angle α in Cloude–Pottier decomposition. However, the main aim of the Neumann decomposition is to describe the morphological characteristics of vegetation. Therefore, it is worth investigating the performance of Neumann decomposition on crop classification, since vegetation is the principal type of targets in agricultural scenes. In this paper, a multi-temporal supervised classification method based on Neumann decomposition and Random Forest Classifier (named “ND-RF”) is proposed. The three parameters from Neumann decomposition, computed along the time series of data, are used as classification features. Finally, the Random Forest Classifier is applied for supervised classification. For comparison, an analogue classification scheme is constructed by replacing the Neumann decomposition with the Cloude–Pottier decomposition, hence named CP-RF. For validation, a time series of 11 polarimetric RADARSAT-2 SAR images acquired over an agricultural site in London, Ontario, Canada in 2015 is employed. Totally, 10 multi-temporal combinations of datasets were tested by adding images one by one sequentially along the SAR observation time. The results show that the ND-RF method generally produces better classification performance than the CP-RF method, with the largest improvement of over 12% in overall accuracy. Further tests show that the two parameters similar to entropy and alpha angle produce classification results close to those of CP-RF, whereas the third parameter in the Neumann decomposition is more effective in improving the classification accuracy with respect to the Cloude–Pottier decomposition.

Using scattering decomposition to improve the estimation of the topography-induced polarization orientation angle

ABSTRACTPolarization orientation angle (POA) shifts produced by topography variations lead to rotating polarization around the radar beam direction. In addition to slope topography, depolarization induced by volume scattering and orientation of dihedral target impact POA which is used for deorientation and, in some cases, topography measurement. Unlike deorientation, to provide reliable topography measurements, it is essential to accurately estimate shifts in POA caused by topography variations, not volume scattering or oriented dihedral. The existing POA estimation methods approximately provide topography POA using an averaged POA. In this letter, a modified POA estimation method was proposed to achieve a high consistency with topography. The proposed method initially reconstructed some sub-scatterers using eigenvalue/eigenvector Cloude–Pottier decomposition. In the second step, the type of scattering mechanisms was determined using Cloude decomposition. Then, it compensated for the POA of the dihedral and the volume-reconstructed sub-scatterers using the POA of each of them obtained with the help of Huynen’s desying operation. Finally, topography POA was obtained from the new coherency matrix. The experimental results were evaluated by the digital elevation model (DEM)-derived POA. Results demonstrated that the proposed method L- and P-band derived POA are and better than the averaged POA in the existing method.

PolSAR Image Classification Based on Discriminative Clustering

This paper presents a novel unsupervised image classification method for Polarimetric Synthetic Aperture Radar (PolSAR) data. The proposed method is based on a discriminative clustering framework that explicitly relies on a discriminative supervised classification technique to perform unsupervised clustering. To implement this idea, an energy function is designed for unsupervised PolSAR image classification by combining a supervised Softmax Regression (SR) model with a Markov Random Field (MRF) smoothness constraint. In this model, both the pixelwise class labels and classifiers are taken as unknown variables to be optimized. Starting from the initialized class labels generated by Cloude-Pottier decomposition and K-Wishart distribution hypothesis, the classifiers and class labels are iteratively optimized by alternately minimizing the energy function with respect to them. Finally, the optimized class labels are taken as the classification result, and the classifiers for different classes are also derived as a side effect. This approach is applied to real PolSAR benchmark data. Extensive experiments justify that the proposed approach can effectively classify the PolSAR image in an unsupervised way and produce higher accuracies than the compared state-of-the-art methods.

Tracking crop phenological development using multi-temporal polarimetric Radarsat-2 data

Information on crop phenological development stages such as emergence, flowering, fruiting, maturing and senescence is essential for crop production surveillance and yield prediction. It has long been related to optical spectral signatures such as the Normalized Difference Vegetation Index (NDVI) or spectral shifts in the red-edge range. In recent years, more efforts have been made to explore the sensitivity of Synthetic Aperture Radar (SAR), particularly polarimetric SAR signatures, to crop biophysical parameters or phenological stages. In this study, phenological metrics of canola (Brassica napus) and spring wheat (Triticum spp.) are related with temporal evolution of polarimetric SAR parameters derived from the C-band RADARSAT-2 full polarimetric SAR data. Both crops are very common in north eastern Ontario, Canada, but have very anatomically different development processes. From multi-temporal RADARSAT-2 data acquired in three consecutive years (2012–2014), significant correlations were observed between a number of SAR polarimetric parameters and the growth parameters of both crops. Strong correlation was observed between plant height and the Alpha angle of the Cloude-Pottier decomposition, with the R2 of 0.91 and 0.66 for canola and wheat, respectively. The R2 increased when the polarimetric parameters were smoothed in the time domain (R2 of 0.98 for canola and 0.88 for wheat). Strong correlation was also observed for the two crops between the effective leaf area index (LAIe) and the Beta angle, and between days-after-seeding (DAS) and a combination of the Alpha and the Beta angles. These findings show that multi-temporal C-band polarimetric SAR parameters could be used for tracking crop phenological development stages.

Unsupervised PolSAR Image Classification Using Discriminative Clustering

This paper presents a novel unsupervised image classification method for polarimetric synthetic aperture radar (PolSAR) data. The proposed method is based on a discriminative clustering framework that explicitly relies on a discriminative supervised classification technique to perform unsupervised clustering. To implement this idea, we design an energy function for unsupervised PolSAR image classification by combining a supervised softmax regression model with a Markov random field smoothness constraint. In this model, both the pixelwise class labels and classifiers are taken as unknown variables to be optimized. Starting from the initialized class labels generated by Cloude–Pottier decomposition and $K$ -Wishart distribution hypothesis, we iteratively optimize the classifiers and class labels by alternately minimizing the energy function with respect to them. Finally, the optimized class labels are taken as the classification result, and the classifiers for different classes are also derived as a side effect. We apply this approach to real PolSAR benchmark data. Extensive experiments justify that our approach can effectively classify the PolSAR image in an unsupervised way and produce higher accuracies than the compared state-of-the-art methods.

Identification of Altered Minerals Based on Synthetic Aperture Radar (SAR) For Mineral Exploration in a Tropical Area

Geological investigation by remote sensing using surface physical properties in tropical regions is challenging. To minimize the effects of atmosphere and vegetation in the obtained optical images, we used the synthetic aperture radar (SAR) images. The phased array L-band synthetic aperture radar (PALSAR) onboard the advanced land observing satellite (ALOS) was selected due to ability of the L-band to penetrate clouds and canopy vegetation. The polarimetric decomposition method based on Cloude–Pottier classification was used as the basis for backscattering analyses. The Ciseuti area in West Java, Indonesia was selected as the study site due to the existence of mining activities, including gold and galena mines. The identification is focused on the spatial distribution of prospected minerals regardless of cloud and vegetation canopy. The classified prospect zones could be extended using the Cloude– Pottier polarimetric decomposition into moderate random entropy and alpha double bounce scattering at argillic alterations, moderate random entropy and alpha surface scattering at intermediate argillic alterations, and highly moderate random entropy and alpha volume diffusion at advanced argillic alterations. The entropy and alpha extracted from ALOS PALSAR data based on Cloude–Pottier decomposition were very useful for identifying alteration zones.

Contributions of C-Band SAR Data and Polarimetric Decompositions to Subarctic Boreal Peatland Mapping

The objective of this paper is to assess the accuracy of C-band synthetic aperture radar (SAR) datasets in mapping peatland types over a region of Canada's subarctic boreal zone. This paper assessed contributions of quad-polarization linear backscatter intensities (σ°HH, σ°HV, σ°VV), image textures, and two polarimetric scattering decompositions: 1) Cloude-Pottier, and 2) Freeman-Durden. Four quad-polarimetric RADADSAT-2 images were studied at incidence angles of 19.4°, 23.1°, 45.8°, and 48.1°. The influence of combining dual-angular information acquired within a short temporal span was also assessed. These C-band SAR data were used to classify peatlands according to isolated flat bogs (bogs), channel fens (fens), raised peat plateaus (plateaus), and forested uplands (uplands) using a supervised support vector machine (SVM) classifier. Numerous classifications were examined to compare the unique contributions of these variables to classification accuracy. Results suggest linear backscatter variables in isolation produce comparable classification results with those of the Freeman-Durden and Cloude-Pottier decompositions. Combining polarimetric decomposition and texture data into classifications with linear backscatter data resulted in only minor (~1-3%) improvement. Combining classifications from small and large incidence angles (dual-angular) significantly improved classification results over those of a single image. Classification accuracy was the highest for isolated bogs and open water surfaces, whereas fens, uplands, and plateaus had lower accuracies. The highest accuracy classification (84% and kappa coefficient of 0.80) used a dual-angular approach, with additional decomposition and texture information. However, it is noted that texture information rarely improved classification results across all tests. This approach identified isolated flat bogs, channel fens, and raised peat plateaus with >76% producer's accuracies.

Estimation of Jones matrix, birefringence and entropy using Cloude-Pottier decomposition in polarization-sensitive optical coherence tomography.

Estimation of polarimetric parameters has been a fundamental issue to assess biological tissues that have form birefringence or polarization scrambling in polarization-sensitive optical coherence tomography (PS-OCT). We present a mathematical framework to provide a maximum likelihood estimation of the target covariance matrix and its incoherent target decomposition to estimate a Jones matrix of a dominant scattering mechanism, called Cloude-Pottier decomposition, thereby deriving the phase retardation and the optic axis of the sample. In addition, we introduce entropy that shows the randomness of the polarization property. Underestimation of the entropy at a low sampling number is mitigated by asymptotic quasi maximum likelihood estimator. A bias of the entropy from random noises is corrected to show only the polarization property inherent in the sample. The theory is validated with experimental measurements of a glass plate and waveplates, and applied to the imaging of a healthy human eye anterior segment as an image filter.

A study on vegetation cover extraction using a Wishart H-α classifier based on fully polarimetric Radarsat-2 data

This article investigates the potential of vegetation cover extraction using fully polarimetric Radarsat-2 data based on the Wishart H-α classification method. In our research, the polarimetric entropy (H) and scattering angle (α) obtained by Cloude–Pottier decomposition technique are used to classify land cover, and the classification results are then used as the initial classification of the Wishart classifier. The classes of the Wishart H-α classification method are associated with vegetation based on the scattering mechanism of vegetation. In our experiment, a fully polarimetric Radarsat-2 image located in the northeast of China, acquired on 4 August 2012, is used to extract the information of vegetation cover, and the corresponding Lansat-7 image is used to verify the classification results. The overall accuracies with different multi-looks window size (3 × 3, 5 × 5, and 7 × 7) are 54.0%, 88.0%, and 73.1%, respectively. The difference of the classification accuracies is influenced by various scattering mechanisms of the lake area. After multi-look processing by a 3 × 3 or 7 × 7 window, the scattering mechanism of the lake should be Bragg scattering rather than fully or partly high-entropy multi-scattering in the Wishart H-α classification. The results show that the water (Bragg scattering) is misclassified as vegetation (high-entropy multi-scattering) by the Wishart H-α classification in C band. The misclassification is caused by the Wishart H-α classifier and the scattering mechanism of the shallow in the study area. Consequently, a modified Wishart H-α classifier that keeps Bragg scattering from high-entropy multi-scattering in each iteration is proposed. Moreover, the analysis on the features of the shallow indicates that the polarimetric entropy increases with the change of roughness. Therefore, the partial lake area presenting high-entropy multi-scattering mechanism is not recognized as vegetation. The final overall accuracies are 81.7%, 88.0%, and 88.0% with the 3 × 3, 5 × 5, and 7 × 7 multi-look window, respectively.