Polarimetric Synthetic Aperture Radar System Research Articles

Fine classification of crops based on an inductive transfer learning method with compact polarimetric SAR images

ABSTRACT Compact polarimetric synthetic aperture radar (CP SAR) reduces fully polarimetric SAR system complexity and expands the imaging swath. Generally, fine classification of crop types relies on many labeled training samples. However, due to the temporal interval of crop phenology and ground environment variations over time, training samples from one dataset usually perform poorly for another. Therefore, in this study, transfer learning is introduced to crop classification to ensure classification accuracy by improving reusability of training samples. A stable and robust inductive transfer learning method, i.e. the Transfer Bagging-based Ensemble Learning (TBEL) algorithm, is proposed. The main idea is to select an adequate number of representative samples from unlabeled datasets to characterize each class in the target domain based on limited labeled samples and construct a classifier set to classify the target domain. This study investigates CP SAR data performance in transfer learning for crop classification. The proposed algorithm in the experimental study is compared with six typical methods (Subspace Alignment (SA), CORrelation ALignment (CORAL), Joint Distribution Adaptation (JDA), Balanced Distribution Adaptation (BDA), Transfer Bagging (TrBagg), and Bagging-based Ensemble Transfer Learning (BETL)). The experimental results show that the crop classification accuracy based on the TBEL algorithm is more stable, with an improved overall classification accuracy of 2–6%. Classifying the same rice harvest stage in the cross-year domain has the highest overall accuracy of 92.2%. Wheat fields in different scenes are also classified. Based on the TBEL algorithm, the overall classification accuracy improves by 1–10% compared with typical methods, with an accuracy of at least 87.6%. Furthermore, by testing the CP mode classification performance over various crops in transfer learning, we find that the circular CP mode performs better than the linear mode in most cases. This conclusion agrees with single-scene applications and was first verified in transfer learning.

Optimizing Soil Moisture Retrieval: Utilizing Compact Polarimetric Features with Advanced Machine Learning Techniques

Soil moisture plays a crucial role in various environmental processes and is essential for agricultural management, hydrological modeling, and climate studies. Synthetic Aperture Radar (SAR) remote sensing presents significant potential for estimating soil moisture due to its ability to operate in all weather conditions and provide day-and-night imaging capabilities. Among the SAR configurations, the Compact Polarimetric (CP) mode has gained increasing interest as it relaxes system constraints, improves coverage, and enhances target information compared to conventional dual polarimetric SAR systems. This paper introduces a novel approach for soil moisture retrieval utilizing machine learning algorithms and CP SAR features. The CP SAR features are derived from a series of RADARSAT Constellation Mission (RCM) CP SAR imagery acquired over Canadian experimental sites equipped with Real-Time In Situ Soil Monitoring for Agriculture (RISMA) stations. This study employs a diverse dataset of compact polarimetric SAR features and corresponding ground truth soil moisture measurements for training and validation purposes. The results of our study achieved a Root Mean Square Error (RMSE) of 6.88% with a coefficient of determination R2 equal to 0.60, which corresponds to a correlation R between true and predicted soil moisture values of 0.75, using optimized Ensemble Learning Regression (ELR) with a decision-tree-based model. These results improved, yielding an RMSE of 5.67% and an R2 equal to 0.73 (R = 0.85), using an optimized Gaussian Process Regression (GPR) model.

A Polarimetric Scattering Characteristics-Guided Adversarial Learning Approach for Unsupervised PolSAR Image Classification

Highly accurate supervised deep learning-based classifiers for polarimetric synthetic aperture radar (PolSAR) images require large amounts of data with manual annotations. Unfortunately, the complex echo imaging mechanism results in a high labeling cost for PolSAR images. Extracting and transferring knowledge to utilize the existing labeled data to the fullest extent is a viable approach in such circumstances. To this end, we are introducing unsupervised deep adversarial domain adaptation (ADA) into PolSAR image classification for the first time. In contrast to the standard learning paradigm, in this study, the deep learning model is trained on labeled data from a source domain and unlabeled data from a related but distinct target domain. The purpose of this is to extract domain-invariant features and generalize them to the target domain. Although the feature transferability of ADA methods can be ensured through adversarial training to align the feature distributions of source and target domains, improving feature discriminability remains a crucial issue. In this paper, we propose a novel polarimetric scattering characteristics-guided adversarial network (PSCAN) for unsupervised PolSAR image classification. Compared with classical ADA methods, we designed an auxiliary task for PSCAN based on the polarimetric scattering characteristics-guided pseudo-label construction. This approach utilizes the rich information contained in the PolSAR data itself, without the need for expensive manual annotations or complex automatic labeling mechanisms. During the training of PSCAN, the auxiliary task receives category semantic information from pseudo-labels and helps promote the discriminability of the learned domain-invariant features, thereby enabling the model to have a better target prediction function. The effectiveness of the proposed method was demonstrated using data captured with different PolSAR systems in the San Francisco and Qingdao areas. Experimental results show that the proposed method can obtain satisfactory unsupervised classification results.

High resolution C-band SAR backscatter response to peatland water table depth and soil moisture: a laboratory experiment

ABSTRACT Carbon storage and active carbon sequestration within peatlands strongly depend on water table depth and soil moisture availability. With increasing efforts to protect and restore peatland ecosystems, the assessment of their hydrological condition is highly necessary but remains challenging. Synthetic aperture radar (SAR) satellite observations likely offer an efficient way to obtain regular information with complete spatial coverage over northern peatlands. Studies have indicated that both radar backscatter amplitude and phase are sensitive to peatland condition. Very recently, Differential Interferometric Synthetic Aperture Radar (DInSAR) has been reported as being capable of monitoring ground deformation patterns at the millimetre scale, which are a response to peatland hydrological condition. To further investigate the promise of SAR for peatland monitoring, a laboratory-based polarimetric C-band SAR system was used to acquire the dynamic radar behaviour of a 4 m (l) ×1 m (w) × 0.25 m (d) reconstructed peatland. A forced 4-month drought was introduced with very-high-resolution imagery taken every 2 hours, capturing details of the vertical backscatter patterning through the peat at the centimetric scale. The results showed a clear coherent response both in radar backscatter amplitude and phase to change in water table level and soil moisture. Similar responses were seen across all polarizations. Phase demonstrated a coherent and deterministic change across the experiment; the average differential phase increase across all polarizations was 118° for 17 cm of water table drawdown. Interpreted as the physical movement of the surface, this corresponded to 8.3 mm of surface subsidence. Both phase and amplitude changes were near-linear with changes in the water table depth; amplitude showed a correspondingly strong concomitant mean decrease of 7 dB across all polarizations during the experiment. The results demonstrate the close sensitivity of radar backscatter to hydrological patterns in a peatland ecosystem. The phase result, in particular, strongly supports the notion that differential phase from satellites can be utilized to measure ground deformation as a proxy for the hydrological state.

Realization of Low Cross-Polarization Level for GF-3 SAR Antenna

In the polarimetric synthetic aperture radar (PolSAR) system, crosstalk error is the key parameter for polarimetric imaging. The source of crosstalk is mostly from the cross-polarization level of the SAR antenna subsystem. To meet the requirements of PolSAR observing modes, a low cross-polarization level under −35 dB is realized for the SAR antenna of the GF-3 satellite. In this paper, we will describe the principles and methods for implementation of low cross-polarization level. The two polarized RF chains are totally independent to ensure high isolation, and a dual-polarized waveguide slot antenna subarray is designed with a low cross-polarization level. Besides, the low taper efficiency of the cross-polarization pattern contributes to reduce the cross-polarization, showing the advantage of the phased array antenna in the PolSAR system. The paper also displays the measured results of cross-polarization for the GF-3 SAR antenna. The study gains a better understanding of the cross-polarization pattern for phased array antenna. The methods utilized here can be extended to other polarimetric SAR systems.

Observations from C-Band SAR Fully Polarimetric Parameters of Mobile Sea Ice Based on Radar Scattering Mechanisms to Support Operational Sea Ice Monitoring

Fully polarimetric (FP) SAR systems offer parameters that describe and quantify the scattering mechanisms from the surface cover. These are usually derived from decomposition of matrices derived from the original scattering matrix from observations at each pixel. Power from scattering mechanisms have potential for retrieval of sea ice information, which cannot be derived using traditional backscatter (magnitude or phase) measured by single- or dual-polarization SAR systems. This study investigates the potential of selected FP parameters that represent the power of three scattering mechanisms, in addition to the total power, in identifying ice types and surface features for operational use. Parameters were obtained from a set of 62 RADARSAT-2 Quad-pol data over Resolute Passage, central Arctic, during the period September-December 2017. A scattering-based color-composite scheme was developed. Analysis of the examined color images was supported by information from regional ice charts and SAR image interpretations from the Canadian Ice Service. Case studies are presented to demonstrate the potential of the proposed color-composite tool. Open water, new ice, multi-year ice and a few surface features including rafted, ridged and smooth/rough surfaces can be identified better in the color images. Physical interpretation of the relative power from the given scattering mechanisms is explained for the relevant ice types and surfaces.

Performance Analysis of Ocean Eddy Detection and Identification by L-Band Compact Polarimetric Synthetic Aperture Radar

The automatic detection and analysis of ocean eddies has become a popular research topic in physical oceanography during the last few decades. Compact polarimetric synthetic aperture radar (CP SAR), an emerging polarimetric SAR system, can simultaneously acquire richer polarization information of the target and achieve large bandwidth observations. It has inherent advantages in ocean observation and is bound to become an ideal data source for ocean eddy observation and research. In this study, we simulated the CP data with L-band ALOS PALSAR fully polarimetric data. We assessed the detection and classification potential of ocean eddies from CP SAR by analyzing 50 CP features for 2 types of ocean eddies (“black”and “white”) based on the Euclidean distance and further carried out eddy detection and eddy information extraction experiments. The results showed that among the 50 CP features, the dihedral component power (Pd), shannon entropy (SEI), double bounce (Dbl), Stokes parameters (g0 and g3), eigenvalue (l1), lambda, RVoG parameter (ms), shannon entropy (SE), surface scattering component (Ps), and σHH all performed better for detecting “white” eddies. Moreover, the H-A combination parameter (1mHA), entropy, shannon entropy (SEP, SEI, and SE), probability (p2), polarization degree (m), anisotropy, probability (p1), double bounce (Dbl), H-A combination parameter (H1mA), circular polarization ratio (CPR), and σVV were better CP features for detecting “black” eddies.

Cross-Pol Pattern Effects of Parabolic Reflector Antennas on the Performance of Spaceborne Quad-Pol SAR Systems

The ambiguity performance related to the image quality of a synthetic aperture radar (SAR) system can be categorized into range ambiguity, azimuth ambiguity, and cross ambiguity depending on the cause of the ambiguity signals. The ambiguity performance is affected by the operational method and antenna radiation patterns of an SAR system. In this paper, the effect of the antenna cross-polarization (cross-pol) pattern on the ambiguity performance of a spaceborne quadrature polarimetric (quad-pol) SAR system using an X-band reflector antenna was analyzed. Two methods, conventional quad-pol SAR and hybrid quad-pol SAR systems, have been proposed as quad-pol SAR operational concepts for system performance analysis. We proposed a process to effectively analyze the effect of the antenna cross-pol pattern and showed this through a comparison of several analysis methods. In addition, the difference in antenna radiation patterns according to the size and structure of reflector antennas was compared, and the effects were described in detail. In conclusion, the larger the antenna and the more complex the structure, the greater the effect of the antenna cross-pol pattern, leading to a similar phenomenon as the two quad-pol SAR systems.

Forest height estimation by means of compact PolInSAR data

Forest monitoring and management have become a necessity to restrain deforestation and human encroachment. In this respect, the polarimetric synthetic aperture radar interferometry (PolInSAR) techniques have demonstrated their capability for this purpose. Recently, a new mode of SAR systems called compact polarimetry (CP) has been proposed, which can overcome some limitations of fully polarimetric (FP) SAR system. This study aims to illustrate the capability of compact PolInSAR (C-PolInSAR) data for forest height estimation. For this purpose, a novel method based on random volume over ground (RVoG) model and PolInSAR decomposition technique is proposed. The main motivation of the proposed method is to directly and simultaneously estimate the ground only and volume only coherences from the C-PolInSAR data without the need for any prior information. The proposed method was verified with the L-band single-baseline PolInSAR data acquired over the Remningstorp test site, southern Sweden. In addition, light detection and ranging (LiDAR) data were used to vertify the results. The experimental results demonstrated that the C-PolInSAR data i.e., pi/4-PolInSAR and circular transmit-linear receive PolInSAR (CTLR-PolInSAR) data could provide a significant accuracy for forest height estimation compared to the fully PolInSAR (F-PolInSAR) data by 4.2 m and 4.49 m improvements, respectively.

Polarimetric calibration of spaceborne and airborne multifrequency SAR data for scattering-based characterization of manmade and natural features

The Polarimetric Synthetic Aperture Radar (PolSAR) systems use electromagnetic radiations of different polarizations in the microwave frequency to collect the scattering information from targets on the Earth. Nevertheless, as with any other electronic device, the PolSAR systems are also not ideal and subjected to distortions. The most important of these distortions are the polarimetric distortions caused due to the channel imbalance, phase bias, and crosstalk between the different polarization channels. For the spaceborne PolSAR systems, the Earth’s ionosphere also contributes to an additional polarimetric distortion known as the Faraday rotation. An effort was made in this study to perform the polarimetric calibration of the Quad-pol and Compact-pol PolSAR datasets acquired using different airborne and spaceborne PolSAR systems to estimate and minimize these polarimetric distortions. The investigation was also done to analyze the impact of these polarimetric distortions on the scattering mechanisms from ground targets and on its dependency on the radar wavelength. The study was done using the UAVSAR L-band Quad-pol dataset, RADARSAT-2 Quad-pol dataset, ALOS-2 PALSAR-2, ISRO's L&S- Band Airborne SAR (LS-ASAR) Quad-pol and Compact-pol datasets, and the RISAT-1 Compact-pol dataset. Calibration of the airborne PolSAR data was carried to understand the level of polarimetric distortions in the LS-ASAR product that is a precursor mission to the spaceborne Dual-Frequency L&S Band NASA-ISRO Synthetic Aperture Radar (NISAR) mission. It is understood that the crosstalk is the dominant polarimetric distortion, which severely affects the PolSAR datasets compared to the other polarimetric distortions, and it is more for the higher wavelength PolSAR systems. The Quegan, Improved Quegan, and Ainsworth algorithms for crosstalk estimation and minimization was performed for the different Quad-pol datasets and it was found that the Improved Quegan algorithm is suitable for removing crosstalk from datasets having high crosstalk and the Ainsworth algorithm is suitable for removing crosstalk from datasets having low crosstalk. The Freeman method of the polarimetric calibration was implemented for the compact-pol datasets and it was able to considerably minimize the polarimetric distortions. The coherency matrix, scattering matrix, model-based decomposition, polarimetric signatures, and roll invariant parameter-based analysis revealed that all the datasets after polarimetric calibration were showing the correct scattering responses expected from the ground targets.

A Method for Estimating and Validating Polarimetric Distortion Parameters Using Corner Reflectors and Its Applicability Analysis

This article presents a method for extracting the relative polarimetric distortion parameter (PDP) values of polarimetric radar systems, including polarimetric synthetic aperture radar (PolSAR). By using one trihedral and two dihedrals and ignoring the high-order terms of the channel crosstalk, the PDPs of a PolSAR system can be estimated efficiently and straightforwardly without the need for iterative procedures or the system reciprocity assumption. Most importantly, the applicability issues are analyzed to obtain accurate PDP values. A sensitivity analysis of the target orientation angle (TOA, a rotation about the radar line of sight) error $\Delta \boldsymbol{\theta }$ is theoretically analyzed and experimentally studied. It appears that the impacts of $\Delta \boldsymbol{\theta }$ of different known targets on the channel imbalances and channel crosstalk estimation can be relatively independent and distinguished using our first-order approximate method. Using the proposed method, the channel imbalances are first estimated using the trihedral and 45° dihedral corner reflectors. A 22.5° dihedral should be involved to solve the sign ambiguity. The TOA error of the 45° dihedrals is not significantly sensitive to the channel imbalance estimation. Then, the channel crosstalk can be estimated using the trihedral and 0° dihedral corner reflectors. The channel crosstalk estimation in this step is relatively sensitive to the TOA error, which should be reduced to an acceptable level. Limitations due to the radar cross-section issues of the corner reflectors and the strength of the original channel crosstalk are also analyzed. Finally, the results of simulations and practical N-SAR experiments show the effectiveness of our proposed algorithm.

Spectral classification of ecological spatial polarization SAR image based on target decomposition algorithm and machine learning

With the development of science and technology, the classification of polarimetric SAR images has become an important part of the research of target recognition and image interpretation. However, for the research method is relatively simple and the accuracy is low, this paper carries out the work from the two aspects of feature extraction and feature classification of the ground object, and analyzes and studies the application and value of the polarimetric SAR system. The basic algorithm of polarization SAR image classification is proposed. A polarimetric SAR image feature classification method based on polarization target decomposition and support vector machine is proposed. Four kinds of scattering features and Freeman decomposition are obtained by Cloude decomposition. The simulation results show that the accuracy of using combined features is about 6.5% higher than that of single features. A polarization classification model based on polarization target decomposition and limit learning method is proposed. The simulation experiment shows ELM learning. The algorithm is indeed much faster than SVM learning. In this paper, a polarimetric SAR image classification method based on improved scattering mechanism coefficients is proposed, and the effectiveness of the polarimetric SAR image classification method based on improved scattering mechanism coefficients is verified. Experimental results show that after feature selection, the method of combining Freeman decomposition and Wishart classifier can get better classification results.

A Three-Hierarchy Evaluation of Polarimetric Performance of GF-3, Compared with ALOS-2/PALSAR-2 and RADARSAT-2.

GaoFen-3 (GF-3) is the first Chinese civilian multi-polarization synthetic aperture radar (SAR) satellite, launched on 10 August of 2016, and put into operation at the end of January 2017. The polarimetric SAR (PolSAR) system of GF-3 is able to provide quad-polarization (quad-pol) images in a variety of geophysical research and applications. However, this ability increases the complexity of maintaining image quality and calibration. As a result, to evaluate the quality of polarimetric data, polarimetric signatures are necessary to guarantee accuracy. Compared with some other operational space-borne PolSAR systems, such as ALOS-2/PALSAR-2 (ALOS-2) and RADARSAT-2, GF-3 has less reported calibration and image quality files, forcing users to validate the quality of polarimetric imagery of GF-3 before quantitative applications. In this study, without the validation data obtained from a calibration infrastructure, an innovative, three-hierarchy strategy was proposed to assess PolSAR data quality, in which the performance of GF-3 data was evaluated with ALOS-2 and RADARSAT-2 data as references. Experimental results suggested that: (1) PolSAR data of GF-3 satisfied backscatter reciprocity, similar with that of RADARSAT-2; (2) most of the GF-3 PolSAR images had no signs of polarimetric distortion affecting decomposition, and the system of GF-3 may have been improved around May 2017; and (3) the classification accuracy of GF-3 varied from 75.0% to 91.4% because of changing image-acquiring situations. In conclusion, the proposed three-hierarchy approach has the ability to evaluate polarimetric performance. It proved that the residual polarimetric distortion of calibrated GF-3 PolSAR data remained at an insignificant level, with reference to that of ALOS-2 and RADARSAT-2, and imposed no significant impact on the polarimetric decomposition components and classification accuracy.

An Online Multiview Learning Algorithm for PolSAR Data Real-Time Classification

Polarimetric synthetic aperture radar (PolSAR) data are sequentially acquired and usually large scale. Fast and accurate classification is particularly important for their applications. By introducing online learning, the PolSAR system can learn a classification model incrementally from a stream of instances, which is of high efficiency for newly arrived samples processing, strong adaptability for a dynamically changing environment, and excellent scalability for rapidly increasing data. In this paper, we propose an Online Multi-view Passive-Aggressive learning algorithm, named OMPA, for PolSAR data real-time classification. The polarimetric, color, and texture features are extracted to characterize PolSAR data, and each type of features corresponds to one view. In order to exploit the consistency and complementary property of these views, we give a new optimization model that ensembles the classifiers of multiple distinct views and enforces the agreement between each predictor and the combined predictor. The corresponding algorithms for both binary and multiclass classification tasks are derived, and the update steps have analytical solutions. In addition, we rigorously derive a bound on the number of prediction mistakes of the method. The proposed OMPA algorithm is evaluated on two real PolSAR datasets for built-up areas extraction and land cover classification, respectively. Experimental results demonstrate that OMPA consistently maintains a smaller mistake rate with low time cost and achieves about 1% and 2% accuracy improvements on the datasets, respectively, compared with the best results of the previously known online single-view and multiview learning methods.

ON THE ESTIMATION OF POLARIMETRIC PARAMETERS FOR OIL SLICK FEATURE DETECTION FROM HYBRID POL AND DERIVED PSEUDO QUAD POL SAR DATA

Abstract. Oil spills in oceans have a significant long term effect on the marine ecosystem and are of prime concern for maritime economy. In order to locate and estimate the oil spread area and for quantitative damage assessment, it is required to continually monitor the affected area on the sea and its surroundings and space based remote sensing makes this technically viable. Synthetic Aperture Radar SAR with its high sensitivity to target dielectric constant, look angle and polarization-dependent target backscatter has become a potential tool for oil-spill observation and maritime monitoring. From conventional single-channel SAR (single-pol, HH or VV) to multi-channel SAR – (Dual/Quad-polarization) and more recently compact polarimetric (Hybrid/Slant Linear) SAR systems have been widely used for oil-spill detection in the seas. Various polarimetric features have been proposed to classify oil spills using full, dual and compact polarimetric SAR. RISAT-1 is a C-band SAR with Circular Transmit and Linear Receive (CTLR) hybrid polarimetric imaging capability.This study is aimed at the polarimetric processing of RISAT-1 hybrid pol single look complex (SLC) data for derivation of the decisive polarimetric parameters which can be used to identify oil spills in oceans and their discrimination from look-alike signatures. In order to understand ocean–oil spill signatures from full-quad pol SAR, pseudo-quad pol covariance matrix is constructed from RISAT-1 hybrid pol using polarimetric scattering models .Then polarimetric processing is carried out over pseudo-quad pol data for oil slick detection. In-house developed software is used for carrying out the above oil-spill study.

Optimized Wishart Network for an Efficient Classification of Multifrequency PolSAR Data

High-resolution wide-area images are required in the diverse field of research ranging from urban planning and disaster prediction to agriculture and geology. Sometimes the image is taken under harsh weather conditions or at night time. Current optical remote sensing technology does not have the capability to acquire images in such conditions. Synthetic aperture radar (SAR) uses microwave signal which has a long-range propagation characteristic that allows us to capture images in difficult weather conditions. In addition to this, some polarimetric SAR (PolSAR) systems are also capable of capturing images using multifrequency bands simultaneously resulting into a multitude of information in comparison to the optical images. In this letter, we propose a single-hidden layer optimized Wishart network (OWN) and extended OWN for classification of the single-frequency and multifrequancy PolSAR data, respectively. Performance evaluation is conducted on a single-frequency as well as multifrequency SAR data obtained by Airborne Synthetic Aperture Radar. We observed that for combining multiple band information, proposed single-hidden layer network outperforms deep learning-based architecture involving multiple hidden layers.

Error Source Analysis and Correction of GF-3 Polarimetric Data

The GaoFen-3 (GF-3) satellite is the first polarimetric synthetic aperture radar (PolSAR) satellite in China. With a designed in-orbit life of 8 years, it will provide large amounts of PolSAR data for ocean monitoring, disaster reduction, and many other applications. The polarimetric data quality is essential for all these applications, so the analysis and calibration of the polarimetric error sources are very important for GF-3. In this study, we established a full-link error model for GF-3 PolSAR system. Based on this model, we comprehensively analyzed the quantitative effects of the main error sources including the composition, figured out characteristics of the phase imbalance introduced by the antenna, and pointed out the error sources which have to be corrected. Furthermore, the polarimetric correction method for GF-3 PolSAR system is proposed. Finally, assisted by several external calibration experiments, polarimetric errors of GF-3 data are efficiently corrected during in-orbit-test phase.

Potential of soil moisture estimation using C-band polarimetric SAR data in arid regions

ABSTRACTSoil moisture plays an important role in various applications such as hydrology, agronomy, climatology and ecology. Over the past few decades, images acquired by PolSAR (Polarimetric Synthetic Aperture Radar) systems have raised much interest in the remote sensing community. However, only a few studies have analysed the potential use of polarimetric parameters for the estimation of soil moisture. Taking Juyanze as the study area, two feature extraction methods, the simple polarimetric intensity and phase processing, and the polarimetric decomposition were used to extract different polarimetric parameters. The response of single, multiple, and combined polarimetric parameters to bare surface soil moisture was investigated based on linear and non-linear regression strategies in using fully polarimetric RADARSAT-2 SAR (Synthetic Aperture Radar) imagery and field measurements. The results indicate that each individual polarimetric parameter at C-band is not very sensitive to surface soil moisture under this investigation. However, with the increasing number of polarimetric parameters, the correlation between soil moisture and polarimetric parameters improves steadily and the root mean square error (RMSE) and mean absolute error (MAE) decreased gradually. Combined polarimetric parameters have little improvement on the correlation between soil moisture and polarimetric parameters. The non-linear regression method can improve the correlation to some extent. However, the preliminary results so far still do not satisfy the accuracy of using the polarimetric parameters to inverse soil moisture. Further investigations are required in the direction of a better characterization and exploitation of polarimetric SAR data for the retrieval of soil moisture in arid regions.

Crop Classification Based on Differential Characteristics of <inline-formula> <tex-math notation="LaTeX">$H/\alpha$ </tex-math> </inline-formula> Scattering Parameters for Multitemporal Quad- and Dual-Polarization SAR Images

Crop-type classification is one of the most significant applications in polarimetric synthetic aperture radar (PolSAR) imagery. As a remote sensing technique, PolSAR has been proved to have the ability to provide high-resolution information of illustrated objects. However, single-temporal PolSAR data are restricted to provide sufficient information for crop identification due to the complicated condition of varying morphology within various growing stages. With an increasing number of spaceborne PolSAR systems launched, a large amount of real PolSAR data are being generated and used to provide great opportunities for multitemporal analysis. The main contribution of this paper is to improve crop classification accuracy with various features of classical $H$ / $\alpha$ parameters. First, in order to deal with dual-PolSAR data, $H$ / $\alpha$ decomposition algorithm for quad-PolSAR is modified to suit to the case of dual polarization. Second, according to the differential scattering characteristics of main crops, a new parameter is innovatively defined to measure the differential characteristics in the $H$ / $\alpha$ classification plane. Third, crop types are discriminated by applying a supervised classification method with the newly defined parameter. Furthermore, the correctness of the parameter is verified with simulated and real Sentinel-1 data as well as AirSAR data. Finally, the performances of the classification method are investigated by the comparison with complex Wishart, Freeman–Wishart, and support vector machine (SVM) classifiers. Hence, the experimental results show that the proposed method and SVM classifier with the newly defined parameter have the ability to improve crop classification accuracy.

Pol-SARAS: A Fully Polarimetric SAR Raw Signal Simulator for Extended Soil Surfaces

We present a new synthetic aperture radar (SAR) raw signal simulator, which is able to simultaneously generate raw signals of different polarimetric channels of a polarimetric SAR system in such a way that a correct covariance matrix is obtained for the final images. Extended natural scenes, dominated by surface scattering, are considered. A fast Fourier-domain approach is used for the generation of raw signals. Presentation of theory is supplemented by meaningful experimental results, including a comparison of simulations with real polarimetric scattering data.