Synthetic Aperture Radar Polarimetry Research Articles

Hybrid Naïve Bayes Gaussian mixture models and SAR polarimetry based automatic flooded vegetation studies using PALSAR-2 data

Flood mapping using Synthetic Aperture Radar (SAR) data impose limitations in fully distinguishing flood under vegetation due to false double bounce returns from inundated tree trunks along with seasonal heterogeneities devised from changing land cover settings. In addition, rapid mapping of flooded vegetation is challenging during near real time applications. In this paper a fully automatic novel supervised classification approach called polarimetric Naïve Bayes is proposed that combines polarimetric information with series of Gaussian mixture models in Naïve Bayes framework to detect various flooded vegetation classes. It also allows the user to choose class configuration and eliminates creation of Region of Interest (ROI) for supervised training. The proposed approach uses scattering information from pre monsoon PolSAR dataset in training step to create ROIs for buildings and other features. In the next step series of Gaussian Mixtures are used for density estimation for different features in Bayesian multiclass problem. The newly developed classifier applied on 2016 Assam flood event resulted in precise mapping of at least three different vegetation classes under flood such as submerged vegetation, wetlands and floating vegetation. Under optimal class configuration, the approach showed better performance compared to other supervised techniques applied on the same data set such as MLE, Mahalanobis, Minimum Euclidean distance, and SVM classifications in delineating flood, submerged vegetation, wetlands and floating vegetation with Producer’s Accuracy of 98.6%, 81.1%, 94% and 51.5% respectively and combined Overall accuracy of 95.5% for flooded vegetation class. This method also detected multiple vegetation classes with better accuracy compared to similar methods.

DUAL-POLARIMETRIC DECOMPOSITION OF SENTINEL-1 SAR IMAGE AND MACHINE LEARNING MODEL FOR OIL SPILL DETECTION: CASE OF MINDORO OIL SPILL

Abstract. Oil spills represent a significant environmental hazard necessitating timely detection to mitigate their detrimental effects. Synthetic Aperture Radar (SAR) technology serves as a remote sensing (RS)-based tool capable of detecting oil spills under varying weather conditions and at all times of day. SAR polarimetry, which assesses the polarization of the backscattered SAR signal, can effectively discriminate oil spills from other features that may manifest as dark regions in the SAR images. The integration of machine learning algorithms offers significant potential for enhancing the accuracy and efficiency of oil spill detection through SAR polarimetry. In recent years, several studies have introduced machine learning-based methodologies for this purpose, yet a comprehensive evaluation of their real-world performance remains essential. This study aimed to assess the efficacy of a machine learning (ML)-based approach for oil spill detection utilizing features derived from a dual-polarimetric decomposition method applied to Sentinel-1 SAR data. Results show that the machine learning-based approach achieved notable accuracy in oil spill detection reaching a score of 0.569 for intersection over union and 72.50 for f1-score of oil spill areas. Overall, this research underscores the potential of ML techniques as valuable tools for oil spill detection via SAR polarimetry.

Designing of Synthetic Aperture Radar Based Control Algorithms for the Autonomous Vehicles

Abstract— The rise in popularity of self-driving cars can be attributed to advancements in modern technology. The surge in interest in self-driving cars has led to an increase in their development, but this has also brought some challenges. A large part of the solution to these problems is satellite remote sensing and GIS technology. Optical data remote sensing technologies alone have limited potential for long-term forest management sustainability. Active Synthetic Aperture Radar (SAR) remote sensing technology has grown in importance in forestry because of its uniqueness and rapid advancement. For example, SAR has an all-weather capability that is sun light independent, cloud and rain-resistant, and highly penetrating. SAR and optical, SAR and LiDAR, optical and LiDAR remote sensing have all been shown to be useful for accurate forest AGB estimation when compared to single sensor data. These types of sensor data integrations are becoming increasingly common. This is made possible by the fact that the scattering process heavily influences the polarimetric signatures that can be observed. The inclusion of SAR polarimetry improves classification and segmentation quality compared to conventional SAR with a single channel. Decomposition products' outputs have been classified. Keywords— Synthetic Aperture Radar, LiDAR, Autonomous Vehicles,

Application of Microwave Polarimetry to the Characterization of Fiber Misalignment in Composites

In this paper synthetic aperture radar (SAR) polarimetry techniques are applied to detect and characterize fiber misalignment in both carbon fiber sheets and glass fiber reinforced polymer (GFRP) composites. The principle behind SAR polarimetry technique to characterize fiber orientation is described, making use of the fact that carbon and glass fibers are polarizing when irradiated with a microwave signal. The difficulties in using 2D polarimetry techniques to make the 3D orientation measurements, required to characterize out-of-plane fiber misalignment, are discussed as well. Subsequently, the feasibility of a recently-developed 3D SAR polarimetry method for this purpose is demonstrated. Several carbon fiber sheet and glass fiber reinforced polymer (GFRP) samples were manufactured with both in-and out-of-plane fiber misalignment. Polarimetric SAR images of the samples were then produced to show the spatially-varying relative orientation (both in-plane and out-of-plane) of the fibers for each sample. These images can be used to both detect and characterize any fiber misalignment, successfully demonstrating the potential for SAR polarimetry as a tool for the inspection of carbon and glass fiber reinforced composites.

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.

Using a fully polarimetric SAR to detect landslide in complex surroundings: Case study of 2015 Shenzhen landslide

Synthetic aperture radar (SAR) polarimetry has demonstrated high efficiency in the detection of landslides in vegetated mountainous areas. In such places, post-landslide soil layers appear to correspond to the typical surface scattering mechanism, which is significantly different from the volume scattering behaviour of the surrounding vegetation. However, a landslide in the complex surroundings of various landforms, involving naked hillslopes, construction fields, bare farmlands, and other such aspects, may not be accurately identified owing to the occurrence of surface scattering behaviours. In order to detect landslides using SAR polarimetry without the limitation of vegetated mountainous areas, we propose a novel method of combining change detection (CD) and an analytic hierarchy process (AHP) based on the Yamaguchi decomposition (YD) to identify landslides while ensuring fewer false alarms. In particular, CD is applied to a pair of pre- and post-event datasets to determine the regions modified by landslides or human activities, and the AHP is performed over the post-event dataset to identify the suspect landslide region characterised by the surface scattering mechanism. Finally, the two results are fused by a logical operation to identify the actual landslide by removing the non-modified surface scattering regions. A case study of the Shenzhen landslide in complex surroundings was considered to verify the performance of the proposed method (CD-AHP). The results indicate that the method could clearly define the main body of the Shenzhen landslide from the city suburbs with a small number of false alarms. Therefore, this method provides a considerable perspective for landslide detection in complex surroundings using SAR polarimetry.

IDENTIFICATION OF TEMPORARY SURFACE WATER USING SENTINEL-1 SAR DATA, CASE STUDY: SENTANI FLASH FLOODING, INDONESIA

Abstract. Temporary surface water monitoring can provide accurate and reliable information about the spatio-temporal level of surface water. This is very important for various environmental applications, such as flood monitoring. Remote sensing data such as Synthetic Aperture Radar (SAR) is very useful for a large-scale flood monitoring. SAR sensors offer clear advantages by providing their own sources of illumination, thus being able to operate in nearly all-weather/day-night conditions. About 30% disasters which occurred in Indonesia are floods. This hazard has become a recurring disaster that takes place annually. A massive flash flood struck Sentani in the Jayapura Regency in the province of Papua, Indonesia on 16 March 2019, causing 104 deaths. The objective of this work is thus to map temporary surface water (flood) of the Sentani flash flooding event in Indonesia using Sentinel-1 SAR imagery. Sentinel-1 IW GRD and SLC (dual polarimetry) on the event period were used. With two types of Sentinel-1 data, this research produced temporary surface water map using rapid mapping method and SAR polarimetry method. Comparing the results, the similarity of SAR polarimetry method to rapid mapping method is about 39%. Based on reference data, rapid mapping result show better accuracy (82%) than SAR polarimetry method (62%). In addition, processing SLC data needs longer time and higher performance than processing GRD data. Thus, for rapid mapping, it is better to use only Sentinel-1 GRD data.

Enhancing land cover classification for multispectral images using hybrid polarimetry SAR data

ABSTRACT The recent development of hybrid polarimetry synthetic aperture radar (SAR) data with decomposition techniques has improved land cover mapping. The generation of land cover maps using land cover classifications of different scenarios was carried out for Theni district, Tamil Nadu, India using optical and hybrid polarimetry SAR data. The present study focuses on evaluating the capability and contribution of hybrid decomposition techniques from Radar Imaging Satellite-1 (RISAT-1) data to improve the optical image classification accuracy. Hybrid decomposition techniques such as m-delta (m-δ), m-chi (m-χ) and m-alpha (m-α) were extracted using Stokes parameters from circular fine resolution stripmap-1 (cFRS-1) mode of RISAT-1 data. Grey level co-occurrence matrix (GLCM) textural bands were extracted from backscattering images of RISAT-1 and spectral bands of linear imaging self scanning sensor – IV (LISS-IV) image. The hybrid decomposition parameters were used to improve the classification accuracies of SAR and optical images and compared with GLCM textural bands. Support vector machine (SVM) classifier was performed for different scenarios and accuracy assessment was evaluated for all the classified images using confusion matrix with help of reference data. The study observed that the RISAT-1 derived products provided reasonable classification accuracy (54%) and also provided better results when added to spectral bands of LISS-IV (74%). The mean and dissimilarity of GLCM textural bands provided best classification, when added individually with RISAT-1 (66%) and LISS-IV (82%), and combined with RISAT-1 and LISS-IV (80%). The results indicate that the optical sensor performs better for the classification of water bodies, fallow land and settlement, however, plantation and rice crops are better classified when combined with SAR hybrid decomposition parameters. The study also observed that GLCM textural bands may change the pixel values based on the window size. The changed pixel values contributed to improving the classification accuracy by 15%. The selected classification and processing methods resulted in reasonable accuracy in land cover mapping in a hilly area with intermittent plains.

Investigation of C-Band SAR Polarimetry for Mapping a High-Tidal Coastal Environment in Northern Canada

Synthetic Aperture Radar (SAR) has been used in characterizing intertidal zones along northern Canadian coastlines. RADARSAT-2, with its full polarimetric information, has been considered for monitoring these vulnerable ecosystems and helping enhance the navigational safety of these waters. The RADARSAT Constellation Mission (RCM) will ensure data continuity with three identical SAR satellites orbiting together, providing superior revisit capabilities. The three satellites are equipped with multiple configurations, including single-polarization (HH, HV, VV), conventional (HH-HV, VV-VH, and HH-VV), hybrid (i.e., compact) dual polarization, and fully polarimetric (FP) modes. This study investigates the potential of the compact polarimetric (CP) mode for mapping an intertidal zone located at Tasiujaq village on the southwest shore of Ungava Bay, Quebec. Simulated RCM data were generated using FP RADARSAT-2 images collected over the study site in 2016. Commonly used tools for CP analysis include Raney m-delta classification and the hybrid dual polarizations RH-RV (where the transmitter is right-circular and the receivers are horizontal and vertical linear polarizations) and RR-RL (where the transmitter is right circular and the receivers are right-circular and left-circular polarizations). The potential of CP is compared with single, conventional dual-pol, and FP. The Freeman–Durden and Touzi discriminators are used for FP analysis. The random forest classifier is used as a classification approach due to its well-documented performance compared to other classifiers. The results suggest that the hybrid compact (RR-RL and RH-RV) dual polarizations provide encouraging separability capacities with overall accuracies of 61% and 60.7%, respectively, although they do not perform as well as conventional dual-pol HH-HV (64.4%). On the other hand, the CP polarimetric m-delta decomposition generated slightly less accurate classification results with an overall accuracy of approximately 62% compared to the FP Freeman–Durden (67.08%) and Touzi discriminators (71.1%).

Polarimetric SAR Interferometry: A Tutorial for Analyzing System Parameters

Polarimetry synthetic aperture radar (SAR) interferometry (PolInSAR) is a promising technology for globalscale forest-height mapping. Many PolInSAR space missions have been proposed or are under development. To ensure the performance of forest-height estimation, a critical study of the required system and platform parameters for the planned mission must be carried out. This involves theoretical modeling of wave scattering of the forest canopy, SAR imaging-simulation of polarimetric interferometry, implementation of a parameter-inversion algorithm, and errorbudget analysis of forest-height retrieval.

Polarimetric SAR Speckle Reduction by Hybrid Iterative Filtering

Speckle filtering in synthetic aperture radar polarimetry (PolSAR) is essential for the extraction of significant information. In this study, the authors introduced a hybrid iterative filtering scheme. The proposed iterative filter is initialized by a polarimetric filter ensuring a high speckle reduction level. Then, to enhance the spatial details, the iterative filter is applied for few iterations. The key parameter b k which traduced the variability of the pixels has been studied. An expression that takes into account the following important numerical considerations is proposed. 1) The variability of the pixel is measured by coefficient variation (CV) rather than the variance. 2) The statistics are computed using non-local neighborhood instead of local square one. 3) A new normalizing function (i. e. hyperbolic tangent) is implemented. 4) The dynamic of range of b k is increased by multiplying the CVs of the filtered and the originals images. Comparison with various state of the art PolSAR filters demonstrated the effectiveness of the proposed iterative method. Simulated, one-look and multilook real PolSAR data were used for validation.

Entropy Based Target Identification in Synthetic Aperture Radar Polarimetry

Synthetic aperture radar is used for polarimetric target identification. It is most prominent imaging radar. This radar covers the widest ranges of earth crust with high resolution images. It captures images day and night. It is suitable for any seasonal weather conditions. The polarization data contains information, on scattering mechanism related to different objects. The objects are land, ocean, glaceries, snow and dense forest which are natural distributed targets. By the use of scattering mechanism the different objects are classified. Scattering mechanism is measured by scattering elements of the matrix. The full polarization of synthetic aperture radar data classifies the obtained image. This paper analyses an entropy based target identification related to synthetic aperture radar polarimetry. The method is also the outcome of Eigen decomposition analysis. The paper also gives broader view of identification of target using physical property and analytical model. The method is helpful for system level design and scattering process considerations.

Mid-season Crop Classification Using Dual-, Compact-, and Full-Polarization in Preparation for the Radarsat Constellation Mission (RCM)

Despite recent research on the potential of dual- (DP) and full-polarimetry (FP) Synthetic Aperture Radar (SAR) data for crop mapping, the capability of compact polarimetry (CP) SAR data has not yet been thoroughly investigated. This is of particular concern, given the availability of such data from RADARSAT Constellation Mission (RCM) shortly. Previous studies have illustrated potential for accurate crop mapping using DP and FP SAR features, yet what contribution each feature makes to the model accuracy is not well investigated. Accordingly, this study examined the potential of the early- to mid-season (i.e., May to July) RADARSAT-2 SAR images for crop mapping in an agricultural region in Manitoba, Canada. Various classification scenarios were defined based on the extracted features from FP SAR data, as well as simulated DP and CP SAR data at two different noise floors. Both overall and individual class accuracies were compared for multi-temporal, multi-polarization SAR data using the pixel- and object-based random forest (RF) classification schemes. The late July C-band SAR observation was the most useful data for crop mapping, but the accuracy of single-date image classification was insufficient. Polarimetric decomposition features extracted from CP and FP SAR data produced relatively equal or slightly better classification accuracies compared to the SAR backscattering intensity features. The RF variable importance analysis revealed features that were sensitive to depolarization due to the volume scattering are the most important FP and CP SAR data. Synergistic use of all features resulted in a marginal improvement in overall classification accuracies, given that several extracted features were highly correlated. A reduction of highly correlated features based on integrating the Spearman correlation coefficient and the RF variable importance analyses boosted the accuracy of crop classification. In particular, overall accuracies of 88.23%, 82.12%, and 77.35% were achieved using the optimized features of FP, CP, and DP SAR data, respectively, using the object-based RF algorithm.

Retrieval of Oil–Water Mixture Ratio at Ocean Surface Using Compact Polarimetry Synthetic Aperture Radar

The oil–water mixture ratio for oil spills on the ocean surface is an important parameter for volume estimation of oil spills, response strategy for the oil spills, cleanup operations, and remediation planning for the impacts on wildlife. Hybrid-polarized (HP) mode compact polarization (CP) synthetic aperture radar (SAR) imagery will soon be available with the launch of the RADARSAT Constellation Mission. The advantage of the proposed new SAR system is that CP images will have wider swath and shorter revisit time compared to quad-polarization (QP) images, which are presently available from space-borne and air-borne SAR. We present a methodology to retrieve the oil–water mixture ratio at the ocean surface using CP SAR imagery. We emulated the HP mode of CP SAR image using Uninhabited Aerial Vehicle SAR (UAVSAR) L band observations collected on June 23rd 2010 over the site of the Deep Water Horizon drilling rig. The gap between elements ratio of CP SAR covariance matrix and that of QP SAR Sinclair matrix is bridged. Numerical optimization and look up table methods are used to relate the oil–water mixture ratio to elements of the covariance matrix for the HP data backscatter. The mixture ratio estimates determined from the ratio of diagonal elements of the covariance matrix for HP mode CP data are compared with results retrieved from the co-polarization ratio from the original QP SAR observations. Results from the proposed methodology for SAR images captured in the HP mode of CP data are shown to compare favourably to observed in situ data of the mixture ratios.

Full and Simulated Compact Polarimetry SAR Responses to Canadian Wetlands: Separability Analysis and Classification

Detailed information on spatial distribution of wetland classes is crucial for monitoring this important productive ecosystem using advanced remote sensing tools and data. Although the potential of full- and dual-polarimetric (FP and DP) Synthetic Aperture Radar (SAR) data for wetland classification has been well examined, the capability of compact polarimetric (CP) SAR data has not yet been thoroughly investigated. This is of great significance, since the upcoming RADARSAT Constellation Mission (RCM), which will soon be the main source of SAR observations in Canada, will have CP mode as one of its main SAR configurations. This also highlights the necessity to fully exploit such important Earth Observation (EO) data by examining the similarities and dissimilarities between FP and CP SAR data for wetland mapping. Accordingly, this study examines and compares the discrimination capability of extracted features from FP and simulated CP SAR data between pairs of wetland classes. In particular, 13 FP and 22 simulated CP SAR features are extracted from RADARSAT-2 data to determine their discrimination capabilities both qualitatively and quantitatively in three wetland sites, located in Newfoundland and Labrador, Canada. Seven of 13 FP and 15 of 22 CP SAR features are found to be the most discriminant, as they indicate an excellent separability for at least one pair of wetland classes. The overall accuracies of 87.89%, 80.67%, and 84.07% are achieved using the CP SAR data for the three wetland sites (Avalon, Deer Lake, and Gros Morne, respectively) in this study. Although these accuracies are lower than those of FP SAR data, they confirm the potential of CP SAR data for wetland mapping as accuracies exceed 80% in all three sites. The CP SAR data collected by RCM will significantly contribute to the efforts ongoing of conservation strategies for wetlands and monitoring changes, especially on large scales, as they have both wider swath coverage and improved temporal resolution compared to those of RADARSAT-2.

INSAR COHERENCE AND POLARIMETRIC PARAMETERS BASED CHARACTERIZATION OF FLOODED AREA – CASE STUDY OF A NATURAL WORLD HERITAGE SITE KAZIRANGA NATIONAL PARK

Abstract. Flood is a major threat to one of the UNESCO world heritage site of India-The Kaziranga National Park. Every year during the monsoon several hundreds of animals which include globally threatened species like single-horned Indian Rhinoceros of Kaziranga lose their lives due to the flood. The Synthetic Aperture Radar (SAR) can be used to monitoring the flood than the optical remote sensors because of their capability of all-weather and time-independent operability. The microwave L band is most suitable for the flood studies because of its higher penetration capability even through the vegetation. In this study, the advantages of SAR polarimetry and Interferometry of multi-temporal L band dual-pol data of ALOS PALSAR 2 were used to characterize the flooded area and also to monitor the flood extent. The H/ A/ Alpha decomposition gives a better characterization of the flooded area. The separability analysis is done with a different combination of decomposition parameters and the parameters having high-class separability between water and non-water areas are selected. Polarimetric classification using Random forest classifier is done on these selected decomposition parameters to classify the study into water and non-water areas. The classified images of different months before, during and after the flood time is used to quantitatively estimate the flood extent and for time series analysis. The Interferometric SAR coherence images along with the backscatter images are used to generate the RGB composites which also gives times series information on the flood impact.

Semi-empirical ocean surface model for compact-polarimetry mode SAR of RADARSAT Constellation Mission

A semi-empirical model of the ocean surface is developed for the compact-polarimetry (CP) mode of Synthetic Aperture Radar (SAR) which will be a main product of the RADARSAT Constellation Mission (RCM), scheduled for launch in November 2018. The fundamental mechanism for the semi-empirical model is the interactions between sea surface waves and the radar microwave. Sea surface waves are generated by ocean surface winds and may be modulated by precipitation or oceanic processes, such as surface currents, internal waves (IWs), and surfactants (e.g. oil spill, and sea ice). Model results for normalized radar cross sections (NRCSs) induced by ocean winds are validated against a database we developed. The ocean wind vectors were observed by in situ buoys of the National Data Buoy Center (NDBC); the RCM NRCSs are obtained from a “CP simulator”, using composites of RADARSAT-2 quad-pol SAR images. We simulate the dependencies of the NRCSs on wind speeds and incidence angles, for up-wind (wind direction is 0°) and cross-wind (wind direction is 90°) conditions. And we report the potential abilities and limitations of ocean winds monitoring using the RCM CP mode, as well as suggest that the RV-pol should be suitable for hurricane monitoring in the future. As hurricanes containing high winds are generally associated with heavy rain conditions, we discuss the possible mechanisms for precipitation effects related to the RV- and RH- polarizations by simulating two effects on the ocean surface waves. Moreover, we suggest that the RCM CP mode is better able to monitor the ocean surface currents than the fully polarimetry SAR systems (e.g. RADARSAT-2), because of the associated large swath and the possible continuous images of three RCM satellites.

Advances in Radar Remote Sensing of Agricultural Crops: A Review

There are enormous advantages of a review article in the field of emerging technology like radar remote sensing applications in agriculture. This paper aims to report select recent advancements in the field of Synthetic Aperture Radar (SAR) remote sensing of crops. In order to make the paper comprehensive and more meaningful for the readers, an attempt has also been made to include discussion on various technologies of SAR sensors used for remote sensing of agricultural crops viz. basic SAR sensor, SAR interferometry (InSAR), SAR polarimetry (PolSAR) and polarimetric interferometry SAR (PolInSAR). The paper covers all the methodologies used for various agricultural applications like empirically based models, machine learning based models and radiative transfer theorem based models. A thorough literature review of more than 100 research papers indicates that SAR polarimetry can be used effectively for crop inventory and biophysical parameters estimation such are leaf area index, plant water content, and biomass but shown less sensitivity towards plant height as compared to SAR interferometry. Polarimetric SAR Interferometry is preferable for taking advantage of both SAR polarimetry and SAR interferometry. Numerous studies based upon multi-parametric SAR indicate that optimum selection of SAR sensor parameters enhances SAR sensitivity as a whole for various agricultural applications. It has been observed that researchers are widely using three models such are empirical, machine learning and radiative transfer theorem based models. Machine learning based models are identified as a better approach for crop monitoring using radar remote sensing data. It is expected that the review article will not only generate interest amongst the readers to explore and exploit radar remote sensing for various agricultural applications but also provide a ready reference to the researchers working in this field.

Covariance Symmetries Detection in PolInSAR Data

In the last two decades, the use of synthetic aperture radar (SAR) for remote sensing purposes has significantly developed due to improvements in the quality and the availability of the images. Two powerful SAR techniques, namely, polarimetry and interferometry, have further increased the range of applications of the sensed data. Using polarimetry, geometrical properties and geophysical parameters, such as shape, roughness, texture, and moisture content, can be retrieved with considerable accuracy, while interferometric information may be used to extract vertical information with accuracy less than 1 cm. In this paper, the potential of using joint polarimetry and interferometry techniques in SAR data (PolInSAR) for the purpose of SAR image classification is investigated. To achieve this goal, we extend a covariance symmetry detection framework to the PolInSAR scenario. The proposed approach will be shown to be able to exploit the peculiar structures of the covariance matrices of PolInSAR images to discriminate structures within the image. Results using real-SAR data are presented to validate the effectiveness of the proposed approach.

Characterization of vegetation and soil scattering mechanisms across different biomes using P-band SAR polarimetry

Understanding the scattering mechanisms from the ground surface in the presence of different vegetation densities is necessary for the interpretation of P-band Synthetic Aperture Radar (SAR) observations and for the design of geophysical retrieval algorithms. In this study, a quantitative analysis of vegetation and soil scattering mechanisms estimated from the observations of an airborne P-band SAR instrument across nine different biomes in North America is presented. The goal is to apply a hybrid (model- and eigen-based) three component decomposition approach to separate the contributions of surface, double-bounce and vegetation volume scattering across a wide range of biome conditions. The decomposition makes no prior assumptions about vegetation structure. We characterize the dynamics of the decomposition across different North American biomes and assess their characteristic range. Impacts of vegetation cover seasonality and soil surface roughness on the contributions of each scattering mechanism are also investigated. Observations used here are part of the NASA Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) mission and data have been collected between 2013 and 2015.