Polarimetric Coherence Research Articles

Estimation of ionospheric Faraday rotation over ocean areas using L-band spaceborne PolSAR data

ABSTRACT L-band spaceborne polarimetric synthetic aperture radar (PolSAR) data has been well used for the high-resolution retrieval of the ionospheric Faraday rotation (FR). A vast majority of these retrievals were, however, conducted on PolSAR data of lands, with only a very few over oceans. For the global imaging of the Earth’s ionosphere, the retrieval of oceanic ionosphere is indispensable. This paper aims to furnish a deep insight into the FR retrieval on oceans using the widely used Bickel-Bates estimator based on the two cross-circular polarizations Z 12 and Z 21 . The FR estimates (FRE) on oceans are evaluated in detail by comparing with those on lands on four ALOS PALSAR datasets. The achieved oceanic FRE are shown to possess comparable or even better quality than those on lands. It occurs in three of the four scenes that the selected ocean sub-area holds a smaller standard deviation (STD) of FRE (less than 0.7000°) than all the selected land sub-areas. Especially, the quality of FRE is shown to be closely linked to the dispersed degree of the magnitude of the conjugate product of Z 12 and Z 21 via the analysis of coefficient of variation (CV), according to which the unusual occurrence of larger magnitude level corresponding to larger STD of FRE can be explained. Nevertheless, compared with CV, the magnitude of the polarimetric coherence between Z 12 and Z 21 (i.e. γ ) is identified as a better index to measure the quality of FRE. A smaller STD of FRE appears along with a larger γ . Three of the four scenes show that the selected ocean sub-area bears a larger mean of γ (greater than 0.9890) in comparison with all the selected land sub-areas. This work can promote the ionospheric research over oceans and to the correction of ionospheric effects in oceanic L-band spaceborne polarimetric radar data as well.

Nearshore Ship Detection in PolSAR Images by Integrating Superpixel-Level GP-PNF and Refined Polarimetric Decomposition

Nearshore ship detection has significant applications in both the military and civilian domains. Compared to synthetic aperture radar (SAR), polarimetric synthetic aperture radar (PolSAR) provides richer information for analyzing the scattering mechanisms of ships and enables better detection of ship targets. However, ships in nearshore areas tend to be highly concentrated, and ship detection is often affected by adjacent strong scattering, resulting in false alarms or missed detections. While the GP-PNF detector performs well in PolSAR ship detection, it cannot obtain satisfactory results in these scenarios, and it also struggles in the presence of azimuthal ambiguity or strong clutter interference. To address these challenges, we propose a nearshore ship detection method named ECD-PNF by integrating superpixel-level GP-PNF and refined polarimetric decomposition. Firstly, polarimetric superpixel segmentation and sea–land segmentation are performed to reduce the influence of land on ship detection. To estimate the sea clutter more accurately, an automatic censoring (AC) mechanism combined with superpixels is used to select the sea clutter superpixels. By utilizing refined eight-component polarimetric decomposition to improve the scattering vector, the physical interpretability of the detector is enhanced. Additionally, the expression of polarimetric coherence is improved to enhance the target clutter ratio (TCR). Finally, this paper combines the third eigenvalue of eigenvalue–eigenvector decomposition to reduce the impact of azimuthal ambiguity. Three spaceborne PolSAR datasets from Radarsat-2 and GF-3 are adopted in the experiments for comparison. The proposed ECD-PNF method achieves the highest figure of merit (FoM) value of 0.980, 1.000, and 1.000 for three datasets, validating the effectiveness of the proposed method.

A polarimetric projection-based scattering characteristics extraction tool and its application to PolSAR image classification

It is necessary to adequately extract scattering characteristics from polarimetric synthetic aperture radar (PolSAR) data for land-cover classification. Current interpretation technologies, such as target decompositions, polarimetric signatures, and polarimetric coherence patterns, have been widely used to explore polarization information. However, because limited scattering models are used to characterize all ground targets, these methods are occasionally limited in the interpretation of PolSAR data. This may reduce the accuracy of the land-cover classification. In this study, we attempted to extract the scattering characteristics through another novel method. We understand the PolSAR image interpretation as the projection of the scattering model on the measured matrix. Current methods project only a limited series of scattering models onto a measured matrix. To complement current methods of PolSAR image interpretation, we propose a polarimetric projection-based scattering characteristics extraction (PPSCE) method. It projects scattering models consisting of four changing independent polarization parameters onto the measured matrix. These scattering models, associated with the geometric and physical properties of the ground targets, correspond to the entire polarization space. The PPSCE method, meanwhile, extracts polarization information sufficiently. L-band UAVSAR, C-band Radarsat-2, and P-band AIRSAR images of classical experimental areas are used to validate the PPSCE method. The results show that the proposed PPSCE method can present polarization information that is difficult to investigate by the existing methods. In addition, the scattering features derived using the PPSCE method are superior for land-cover classification. Compared to the classification results based on the coherency matrix and Wishart classifier, the proposed method improves the overall classification accuracy by 23.56% for UAVSAR data. Compared to target decompositions and polarization signatures, the overall classification accuracy of the proposed method is increased by 29.32% (RF) and 22.63% (SVM) for UAVSAR data, 7.96% (RF) and 4.36% (RF) for Radarsat-2 and AIRSAR datasets, respectively. Furthermore, the proposed method has significant potential for soil moisture inversion. https://rgdoi.net/10.13140/RG.2.2.14091.26409.

MLE-MPPL: A Maximum Likelihood Estimator for Multipolarimetric Phase Linking in MTInSAR

Multi-Temporal Synthetic Aperture Radar Interferometry (MTInSAR) is an efficient geodetic tool for earth surface displacement measurement, and the polarimetric capability of current and upcoming Synthetic Aperture Radar (SAR) satellites offers a new opportunity to further improve MTInSAR phase series estimation. However, none of existing estimators for multipolarimetric MTInSAR phase series of distributed scatters (DSs) is derived under the minimum root mean square error (RMSE) criterion. In this work, a maximum likelihood estimator for multipolarimetric phase linking (MLE-MPPL) is proposed and the corresponding Cramer-Rao lower bound (CRLB) is also derived by modeling the polarimetric interferometric coherence matrix as the Kronecker product of polarimetric coherence matrix and interferometric coherence matrix. In addition, a new metric called Pol-detR is proposed for performance evaluation of multipolarimetric MTInSAR phase series estimation in practical scenarios where the RMSE is not feasible any more. Experimental results based on both simulated and real data show that the proposed MLE-MPPL achieves the best estimation performance and is more robust against inter-channel interference than existing methods.

Analysis of polarimetric coherence data and derived roll-variant parameters of Sentinel-1 data for discriminating mangrove types in Pichavaram wetlands

The major focus of the present study is the polarimetric coherence, which corresponds to the correlation between two polarized channels of Synthetic Aperture Radar (SAR) datasets. The capability of the polarimetric coherence to detect target scatters utilizing the entire rotation domain rather than the fixed radar line of sight is the fundamental concept applied in this study. The present study attempts to test the potential of these concepts in mangrove types discrimination using the Sentinel-1 dual polarimetric (VV and VH) dataset. The study targets to characterize the Rhizophora and Avicennia mangrove types of the Pichavaram region of the eastern coast of India. The roll variant parameters were estimated using the polarimetric coherence values derived in the rotation domain for non-redundant angles. The seasonal influence on these parameters was prominent and confirmed through wet and dry season analysis using the ombrothermic diagram, which depicts the temperature and rainfall data. The temporal analysis with a monthly frequency confined to the period of 2018 to 2020 and revealed the capability of discriminating the selected mangrove types better in the pre-monsoon period. The results also unveiled the sensitivity of the polarimetric coherence and associated parameters to the abrupt change in climatic factors, especially during the dry to wet season transition. Finally, the results are verified with a statistical analysis performed using Coefficient of Variation (CV) and Scale Factor. The scope of the present study can be extended to the utility of roll variant parameters along with polarimetric decomposition derived parameters for better characterization of target scatterers. KEY POLICY HIGHLIGHTS Polarimetric coherence at a fixed radar line of sight can be extended over a rotation domain to discriminate mangrove types. The seasonal influences can be captured through polarimetric coherence pattern analysis. The combined climate and polarimetric coherence pattern analysis identify the appropriate period for mangrove type discrimination.

Wetlands Classification Using Quad-Polarimetric Synthetic Aperture Radar through Convolutional Neural Networks Based on Polarimetric Features

Wetlands are the “kidneys” of the earth and are crucial to the ecological environment. In this study, we utilized GF-3 quad-polarimetric synthetic aperture radar (QP) images to classify the ground objects (nearshore water, seawater, spartina alterniflora, tamarix, reed, tidal flat, and suaeda salsa) in the Yellow River Delta through convolutional neural networks (CNNs) based on polarimetric features. In this case, four schemes were proposed based on the extracted polarimetric features from the polarization coherency matrix and reflection symmetry decomposition (RSD). Through the well-known CNNs: AlexNet and VGG16 as backbone networks to classify GF-3 QP images. After testing and analysis, 21 total polarimetric features from RSD and the polarization coherency matrix for QP image classification contributed to the highest overall accuracy (OA) of 96.54% and 94.93% on AlexNet and VGG16, respectively. The performance of the polarization coherency matrix and polarimetric power features was similar but better than just using three main diagonals of the polarization coherency matrix. We also conducted noise test experiments. The results indicated that OAs and kappa coefficients decreased in varying degrees after we added 1 to 3 channels of Gaussian random noise, which proved that the polarimetric features are helpful for classification. Thus, higher OAs and kappa coefficients can be acquired when more informative polarimetric features are input CNNs. In addition, the performance of RSD was slightly better than obtained using the polarimetric coherence matrix. Therefore, RSD can help improve the accuracy of polarimetric SAR image classification of wetland objects using CNNs.

Evaluation of Multilooking Size on Single-Baseline PolInSAR Forest Height Inversion

In polarimetric interferometric SAR (PolInSAR) technology, the random volume over ground (RVoG) model establishes the mapping relationship between polarimetric complex coherence and forest biophysical parameters (e.g., forest height). However, due to speckle noise and the finite multilooking effect, the real observed coherence region in the complex unitary circle (CUC) is an ellipse, which is biased by the ideal noise-free coherence region represented as a straight line by the RVoG model. Multilooking processing can reduce speckle noise at the cost of resolution loss. Therefore, this paper analyzes the influence of different multilooking sizes on forest height inversion. Experimental results show that the accuracy of forest height inversion first increases and then decreases with the increase in multilooking size, which means there exists an optimal size for PolInSAR forest estimation. From statistical analysis of the forest height estimation error, inversion accuracy mainly depends on estimation bias rather than estimation variance. This is mainly because, in a homogeneous forest area, a large multilooking size helps to reduce the statistical bias effect; in the textured area, the inversion accuracy benefits from a small multilooking size for avoiding the mixing of multiple types of ground targets.

A phase-decomposition-based polarimetric coherence optimization method

The interference between different scattering components in distributed scatterer (DS) makes it difficult to estimate the phase from DS. Recently, some methods based on eigenvalue decomposition (EVD) are proposed to separate scatterer components from DS, such as component extraction and selection SAR (CAESAR) and phase-decomposition-based persistent scatterer (PS) InSAR (PD-PSInSAR). Meanwhile, many polarimetric radar satellites launched in recent years provide abundant polarization data, which promotes the research of polarimetric coherence optimization (PCO). The capability of PCO to improve the amount and quality of PSs has been verified. In this paper, a phase-decomposition-based polarimetric coherence optimization (PD-PCO) is proposed to explore the potential of PCO to improve the phase quality of DS. Firstly, the contribution of the first principal component on a coherence matrix is obtained by searching for the optimum projection vector. Then, the new scattering coefficient is generated by combining the polarimetric scattering vector and the projection vector. Finally, the interferometric phase of dominant scattering mechanism component is separated from the phase component by EVD. Twenty-eight dual-pol Sentinel-1A SAR images have been adopted to validate the effectiveness of the proposed method. Phase derivative variation and coherence of the proposed method has been compared with other algorithms. The result shows that the quality of phase estimated by the PD-PCO is superior to the CAESAR, PD-PSInSAR, and the mean coherence polarimetric optimization based on equivalent scattering mechanism (ESM-R). The experimental results show that the proposed algorithm successfully improves the phase quality of DSCs.

Polarimetric Analysis of ALOS PALSAR data (POL-SAR) over Test Areas in North-West Bulgaria – Polarimetric Descriptors, Decompositions and Classifications

This study is focused on utilizing full-polarimetric L-band radar data from ALOS PALSAR (JAXA) by means of Polarimetry (POL-SAR), over mountainous test sites in Bulgaria. General aim is to show feasibility of the Polarimetry to describe natural targets, which exhibits various scattering mechanisms in respect to their bio-physical and geometrical properties. Firstly, the importance of Covariance and Coherent matrices is shown, which is followed by calculation of the polarimetric coherences with their particular significance. The mathematical and physical model based decompositions are applied to describe backscattering media from scattering mechanisms. Radar indices resulted from H/A/α-decomposition showed radnomization of scattering mechanisms over forest areas, whilst two major scattering mechanisms are observed mainly in crop lands. Α comparison is made between polarimetric descriptors from acquisitions in different seasonality over mountainous forest and agricultural lands. Polarimetric segmentations and classifications are applied, with 8 (H/A) and 16 (H/A/α) components. Finally, a forest mask is proposed based on relevant polarimetric descriptors. Study showed good utilization and importance of the full-polarimetric L-band SAR data, derived from ALOS PALSAR, in natural targets and forest areas. This report resulted from a course GEO414 -“Polarimetrie”, held at the University of Jena, Lehrstuhl für Fernerkundung, in the framework of ERASMUS+, with the kind support of – Dr. T. Jagdhuber (DLR) and Prof. C. Schmullius.

A Novel Polarimetric PSI Method Using Trace Moment-Based Statistical Properties and Total Power Interferogram Construction

With the launch of various multipolarimetric satellites, many scholars have introduced the persistent scatterer (PS)-oriented polarimetric optimization methods and extended the persistent scatterer interferometry (PSI) method to multipolarimetric data configuration, called polarimetric PSI (PolPSI) technology. Most PolPSI methods mainly take the amplitude dispersion index (ADI) as the optimization criterion and evaluate the temporal amplitude stationarity of each polarimetric channel for finding an optimal one. However, due to the unstable statistical characteristics of the quality indicator, many non-PS pixels are easily mistaken for the PS candidates (PSCs), and the performance of interferometric phase optimization is also limited. To overcome these restrictions, in this article, a novel PolPSI method is proposed based on the following two improved innovations. First, in terms of PSC selection, the trace moment (TM)-based statistical properties of time-series polarimetric coherency matrices are utilized for selecting the scatterers with the temporal polarimetric stationarity. Second, in terms of interferometric phase optimization, all interferometric coherency matrices of multipolarization channels are added up together to construct the total power (TP) interferogram for suppressing the effect of speckle noise and decorrelation. In the experiment, 13 scenes of quad-polarization ALOS PALSAR-1 image are selected to verify the algorithm’s effectiveness. The experimental results demonstrate that the proposed PolPSI method can better improve the deformation monitoring performance in three aspects than both the single-polarimetric HH and traditional exhaustive search polarimetric optimization (ESPO) methods, including phase quality improvement, density of PSs, and computational efficiency.

Composite Sequential Network With POA Attention for PolSAR Image Analysis

The scattering response of polarimetric synthetic aperture radar (PolSAR) data is strongly target orientation-dependent. Formulating the polarimetric matrix as sequential data by rotating the polarimetric matrix along the radar line of sight would provide rich information about land-cover properties. In this work, we propose a composite sequential network (CSN) with polarization orientation angle (POA) attention to model the polarimetric coherency matrix sequence and explore target scattering orientation diversity features. Three major factors strengthen the proposed method for PolSAR image analysis. First, CSN improves the feature comprehensiveness by extending the interpretation mode of PolSAR data from spatial polarization to spatial polarization orientation. In this way, CSN could describe polarimetric response dynamics at different orientations. Second, a two-stream composite network with both real- and complex-valued convolutional long short-term memory (ConvLSTM) network is proposed to process the diagonal and off-diagonal elements of the coherency matrix sequence, respectively. Compared to existing real-/complex-valued networks, the CSN explores the significant phase information of the off-diagonal elements by operations in the complex domain. Meanwhile, CSN prevents padding 0 meaninglessly in the imaginary part of the real-valued diagonal elements. Third, during the sequential modeling of the polarimetric matrix, a POA attention mechanism is proposed. Equipped with POA-sensitive decomposition loss, the CSN attends to substantial POA range derived by targets' physical scattering mechanism and learns features closely related to the scattering mechanism. Extensive experiments and analysis on land-cover classification demonstrate the proposed method's robustness and excellence.

A SAR-GMTI Approach Aided by Online Knowledge With an Airborne Multichannel Quad-Pol Radar System

In the complicated geographical environment, there will be a seriously deleterious effect to the performance of synthetic aperture radar (SAR)-ground moving target indication (SAR-GMTI) system, because it is difficult to obtain the homogeneous training samples to accurately estimate the clutter covariance matrix (CCM) without prior information of the observed scene. To this end, this paper proposes a SAR-GMTI approach aided by online knowledge with an airborne multichannel quadrature-polarimetric (quad-pol) radar system. Generally, this paper can be divided into two parts: online knowledge acquisition and polarization knowledge-aided (Pol-KA) SAR-GMTI processing. Firstly, based on the similarity of pixels from the multichannel and multi-polarization information, a weighed estimation method of polarimetric coherency matrix is proposed, which can overcome the over-smoothing problem and increase the estimation accuracy of coherency matrix. Further, a hybrid weighted local K-means based on geodesic distance (GD-HWLKM) clustering algorithm is proposed to achieve the aim of unsupervised classification. Here, geodesic distance (GD) is exploited to measure the distance between multi-feature region covariance matrixes (MFRCMs) and a hybrid weight from different scales (including local cover class distribution, region and pixel) is calculated to automatically update the cluster centroid, which can make full use of the local spatial information by taking the interclass samples' similarity and the diversity of different classes into consideration. Secondly, with the assistance of the previous polarization SAR (PolSAR) image classification result, a Pol-KA SAR-GMTI method is developed. For each ground cover category, an accurate CCM can be estimated with the independent and identically distributed (IID) training samples. Then, the multichannel clutter suppression and preliminary constant false alarm rate (CFAR) detection are performed. Finally, with an airborne multichannel quad-pol radar system, the experimental results on real measured data demonstrate that the proposed method can efficiently improve the clutter suppression preformation and moving-target detection preformation.

PolSAR Ship Detection with Optimal Polarimetric Rotation Domain Features and SVM

Polarimetric synthetic aperture radar (PolSAR) can obtain fully polarimetric information, which provides chances to better understand target scattering mechanisms. Ship detection is an important application of PolSAR and a number of scattering mechanism-based ship detection approaches have been established. However, the backscattering of manmade targets including ships is sensitive to the relative geometry between target orientation and radar line of sight, which makes ship detection still challenging. This work aims at mitigating this issue by target scattering diversity mining and utilization in polarimetric rotation domain with the interpretation tools of polarimetric coherence and correlation pattern techniques. The core idea is to find an optimal combination of polarimetric rotation domain features which shows the best potential to discriminate ship target and sea clutter pixel candidates. With the Relief method, six polarimetric rotation domain features derived from the polarimetric coherence and correlation patterns are selected. Then, a novel ship detection method is developed thereafter with these optimal features and the support vector machine (SVM) classifier. The underlying physics is that ship detection is equivalent to ship and sea clutter classification after the ocean and land partition. Four kinds of spaceborne PolSAR datasets from Radarsat-2 and GF-3 are used for comparison experiments. The superiority of the proposed detection methodology is clearly demonstrated. The proposed method achieves the highest figure of merit (FoM) of 99.26% and 100% for two Radarsat-2 datasets, and of 95.45% and 99.96% for two GF-3 datasets. Specially, the proposed method shows better performance to detect inshore dense ships and reserve the ship structure.

Woody Above-Ground Biomass Estimation on Abandoned Agriculture Land Using Sentinel-1 and Sentinel-2 Data

Abandoned agricultural land (AAL) is a European problem and phenomenon when agricultural land is gradually overgrown with shrubs and forest. This wood biomass has not yet been systematically inventoried. The aim of this study was to experimentally prove and validate the concept of the satellite-based estimation of woody above-ground biomass (AGB) on AAL in the Western Carpathian region. The analysis is based on Sentinel-1 and -2 satellite data, supported by field research and airborne laser scanning. An improved AGB estimate was achieved using radar and optical multi-temporal data and polarimetric coherence by creating integrated predictive models by multiple regression. Abandonment is represented by two basic AAL classes identified according to overgrowth by shrub formations (AAL1) and tree formations (AAL2). First, an allometric model for AAL1 estimation was derived based on empirical material obtained from blackthorn stands. AAL2 biomass was quantified by different procedures related to (1) mature trees, (2) stumps and (3) young trees. Then, three satellite-based predictive mathematical models for AGB were developed. The best model reached R2 = 0.84 and RMSE = 41.2 t·ha−1 (35.1%), parametrized for an AGB range of 4 to 350 t·ha−1. In addition to 3214 hectares of forest land, we identified 992 hectares of shrub–tree formations on AAL with significantly lower wood AGB than on forest land and with simple shrub composition.

Forest Height Estimation Using MultiBaseline Low-Frequency PolInSAR Data Affected by Temporal Decorrelation

For repeat-pass interferometric systems, temporal decorrelation (TD) is inevitable and cannot be ignored, and can lead to significant bias in the forest height estimation. The TD random volume over ground (TD + RVoG) model has been found to be a reasonable way to describe the scattering process over forest areas. In this letter, based on the TD + RVoG model, a new forest height estimation method is proposed for use with multibaseline polarimetric synthetic aperture radar interferometry (PolInSAR) data. First, the correlation between the ground-to-volume ratios (GVRs) associated with the different polarizations is parameterized according to the geometric interpretation of the RVoG model. An interferometric pair that is assumed to have no TD is then selected based on the eccentricity of the polarimetric coherence region, and the other interferometric pairs are fitted by the TD + RVoG model. E-synthetic aperture radar (E-SAR) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P$ </tex-math></inline-formula> -band PolInSAR data sets affected by TD are used to prove the effectiveness of the proposed method. The experimental results show that the forest height results are improved by 25.90% when compared to the RVoG-based method.

PSRN: Polarimetric Space Reconstruction Network for PolSAR Image Semantic Segmentation

To accurately extract various ground objects from polarimetric synthetic aperture radar (PolSAR) images is a challenging research topic. The deep convolutional neural networks are widely used in SAR segmentation due to their remarkable performance in optical remote sensing images. However, they are still limited by the geometric deformation of the objects, the strong scattering interference among adjacent objects, and the difficulty in distinguishing similar things. A large part of the reason lies in the insufficient utilization and the damage of feature mining for PolSAR data. In this article, focusing on the scattering matrix and the polarimetric coherency matrix, a polarimetric space reconstruction network is proposed. First, to maintain the relatively initial spatial constraints and the complete polarimetric information, the inputs are arranged by a spatial amplification coding method for the polarimetric coherency and scattering matrices. Second, a statistics enhancement module based on scattering characteristics is proposed to mine the differential expression among multiple scattering and polarimetric components, which supplements the local feature representation of convolutional operators. Third, the designed dual self-attention mechanism can capture the amplitude and phase relations of matrix element context adaptively. The proposed method keeps the spatiality of each scattering vector and fully explores the complementary information involved in the fully polarimetric data. Moreover, it accomplishes the accurate land cover classification for PolSAR images, especially the traditional confusing categories, such as water and roads. The experimental results on the E-SAR, AIRSAR, Gaofen-3, and RADARSAT-2 datasets show that the proposed method is effective and promising for PolSAR image segmentation.

Polarimetric SAR image classification using 3D generative adversarial network

In this paper, a new architecture of three-dimensional deep convolutional generative adversarial network(3D-DCGAN) is specially defined to solve the unstable training problem of GAN and make full use of the information involved in polarimetric data. Firstly, a data cube with nine components of polarimetric coherency matrix are directly used as the input features of DCGAN. After that, a 3D convolutional model is designed as the components of generator and discriminator to construct the 3D-DCGAN, which considers the effective feature extraction capability of 3D convolutional neural network(CNN). Finally parameters of the network are fine-tuned to realize the polarimetric SAR image classification. The experiments results show the feasibility and efficiency of the proposed method.

Novel clustering schemes for full and compact polarimetric SAR data: An application for rice phenology characterization

Information on rice phenological stages from Synthetic Aperture Radar (SAR) images is of prime interest for in-season monitoring. Often, prior in-situ measurements of phenology are not available. In such situations, unsupervised clustering of SAR images might help in discriminating phenological stages of a crop throughout its growing period. Among the existing unsupervised clustering techniques using full-polarimetric (FP) SAR images, the eigenvalue-eigenvector based roll-invariant scattering-type parameter, and the scattering entropy parameter are widely used in the literature. In this study, we utilize a unique target scattering-type parameter, which jointly uses the Barakat degree of polarization and the elements of the polarimetric coherency matrix. Likewise, we also utilize an equivalent parameter proposed for compact-polarimetric (CP) SAR data. These scattering-type parameters are analogous to the Cloude-Pottier’s parameter for FP SAR data and the ellipticity parameter for CP SAR data. Besides this, we also introduce new clustering schemes for both FP and CP SAR data for segmenting diverse scattering mechanisms across the phenological stages of rice. In this study, we use the RADARSAT-2 FP and simulated CP SAR data acquired over the Indian test site of Vijayawada under the Joint Experiment for Crop Assessment and Monitoring (JECAM) initiative. The temporal analysis of the scattering-type parameters and the new clustering schemes help us to investigate detailed scattering characteristics from rice across its phenological stages.

Estimation of ice fabric within Whillans Ice Stream using polarimetric phase-sensitive radar sounding

AbstractHere we use polarimetric measurements from an Autonomous phase-sensitive Radio-Echo Sounder (ApRES) to investigate ice fabric within Whillans Ice Stream, West Antarctica. The survey traverse is bounded at one end by the suture zone with the Mercer Ice Stream and at the other end by a basal ‘sticky spot’. Our data analysis employs a phase-based polarimetric coherence method to estimate horizontal ice fabric properties: the fabric orientation and the magnitude of the horizontal fabric asymmetry. We infer an azimuthal rotation in the prevailing horizontalc-axis between the near-surface (z≈ 10–50 m) and deeper ice (z≈ 170–360 m), with the near-surface orientated closer to perpendicular to flow and deeper ice closer to parallel. In the near-surface, the fabric asymmetry increases toward the center of Whillans Ice Stream which is consistent with the surface compression direction. By contrast, the fabric orientation in deeper ice is not aligned with the surface compression direction but is consistent with englacial ice reacting to longitudinal compression associated with basal resistance from the nearby sticky spot.

Physical Scattering Interpretation of POLSAR Coherency Matrix by Using Compound Scattering Phenomenon

The $2\times 2$ relative scattering matrix $[S]$ is characterized by five elements (three amplitudes and two relative phases). A more suitable representation of the data in terms of power expression for target identification exists in the $3\times 3$ Pauli-based polarimetric coherency matrix $[T]$ . The aim of this work is to expand the physical interpretation of $[T]$ elements in terms of a physical scattering mechanism, which has not yet been achieved completely. Compound scattering matrix nature is considered to interpret the elements of $[T]$ . Compound scattering phenomenon, and thereby generated matrices, are verified with measured compound scattering matrices in an anechoic chamber by comparing measured and theoretical polarization signature responses. The behavior of measured polarization signature(s) is consistent with the theoretical/hypothetical polarization signature(s). It is also observed that the combination of two or more dipole-type targets formed the scattering generators and helped to reveal the unknown scattering mechanisms in the coherency matrix. Furthermore, the existence of compound scattering phenomenon is also justified with airborne and spaceborne fully polarimetric synthetic aperture radar (POLSAR) data by implementing recent physical model-based scattering power decomposition methods.