C-band Polarimetric Synthetic Aperture Radar Research Articles

River ice breakup classification using dual- (HH&HV) or compact-polarization RADARSAT Constellation Mission data

Ice jams and associated flooding during river ice breakup are seasonal hazards for many northern communities. Synthetic Aperture Radar (SAR) data enable timely monitoring of river ice conditions in inclement weather. River ice types and open water are discriminated during breakup using dual-polarized (HH&HV; DPH) and compact polarimetric (CP) C-band SAR imagery from the RADARSAT Constellation Mission (RCM). Study areas comprise nineteen locations on rivers in northern Ontario, the Northwest Territories, and Alberta, Canada. Rubble ice, sheet ice, and open water areas are sampled in RCM imagery using corroborating evidence from shoreline cameras and oblique airborne photography. Samples are obtained in the incidence angle range 19° to 48°, for open water with various wind speeds and flow conditions, and for rubble ice and sheet ice with varying surface roughness, snow cover, and wetness. Discrimination relies on a primary classification of rubble ice, sheet ice, and open water, and a secondary classification of ice roughness. The primary classification model development uses a recursive-partitioning machine-learning technique. Overall accuracies for the best DPH models are 83.8% to 89.6%, depending on image noise floor. DPH models use both HH and HV polarizations for low noise floor image modes, whereas only HH is used for higher noise floor image modes. The best CP model accuracy is 90.2%, using the RL, RR, and RVRH parameters. Secondary classification associates increasing ice roughness with increasing HH backscatter for DPH data, and with increasing RH backscatter for CP data. The angular dependencies of rubble ice or sheet ice are used to normalize backscatter, which is then divided into roughness categories. The DPH and CP classification models are named IceBC-DP and IceBC-CP, respectively. Qualitative analysis illustrates good overall ice type and open water classification. Confounding situations are mixed pixel issues, whitewater and wind roughening of water, moist snow microwave absorption, and superficial water on sheet ice. The development of robust methods for monitoring ice jam formation with RCM is an operational concern for departments within the Government of Canada and within Provincial and Territorial Governments.

Crop Height Estimation Based on a Novel Semi-Empirical Model Considering Double-Bounce Scattering Using RADARSAT-2 PolSAR Data

Abstract. Obtaining precise and rapid crop height is essential to facilitate agricultural production services, field management, disaster monitoring, and yield assessment. With the capability to penetrate vegetation and record vertical structure information, Polarimetric Synthetic Aperture Radar (PolSAR) holds significant potential for application in vegetation height inversion. The Water Cloud Model (WCM) and its enhanced versions are extensively utilized for estimating crop heights from PolSAR data owing to their physical significance and simplicity. However, the method is not practical for stalk crops due to the neglect of double-bounce scattering considerations. Therefore, according to the growth characteristics of stalk crops, a three-component polarimetric coherent backscattering model considering crop target double-bounce scattering is established by simplifying the Random Volume over Ground (RVoG) coherent scattering model. The empirical coefficient is introduced to simplify the model into a semi-empirical for crop height inversion. The suitability of applying the RVoG-B three-component model for crop height inversion at the early stage in corn fields was assessed using Multi-temporal C-band PolSAR RADARSAT-2 data in three polarimetric channels. The results show that the HV channel exhibits superior potential in inverting the height of corn compared with the HH and the VV channels. The results of corn height inversion demonstrate that the RVoG-B three-component semi-empirical model performs effectively in estimating corn height, with its inversion accuracy having an RMSE ranging from 11.66cm to 24.51cm. This study demonstrates the potential application of the RVoG-B three-component semi-empirical model for inverting crop height at the early stage dominated by double-bounce scattering.

Retrieval of Surface Soil Moisture over Wheat Fields during Growing Season Using C-Band Polarimetric SAR Data

Accurate estimation and regular monitoring of soil moisture is very important for many agricultural, hydrological, or climatological applications. Our objective was to evaluate potential contributions of polarimetry to soil moisture estimation during crop growing cycles using RADARSAT-2 C-band images. The research focused on wheat field data collected during Soil Moisture Active Passive Validation Experiment (SMAPVEX12) conducted in 2012 in Manitoba (Canada). A sensitivity analysis was performed to select the most relevant non-polarimetric and polarimetric variables extracted from RADARSAT-2, and statistical models were developed to estimate soil moisture. In fine, three models were developed and validated: a non-polarimetric model based on cross-polarized backscattering coefficient σHV0; a polarimetric mixed model using six polarimetric and non-polarimetric retained variables after the sensitivity analysis; and a simplified polarimetric mixed model considering only the phase difference (ϕHH−VV) and the co-polarized backscattering coefficient σHH0. The validation reveals significant positive contributions of polarimetry. It shows that the non-polarimetric model has a much larger error (RMSE = 0.098 m3/m3) and explains only 19% of observed soil moisture variation compared to the polarimetric mixed model, which has an error of 0.087 m3/m3, with an explained variance of 44%. The simplified model has the lowest error (0.074 m3/m3) and explains 53.5% of soil moisture variation.

Ship Detection in PolSAR Images Based on a Modified Polarimetric Notch Filter

Ship detection based on synthetic aperture radar (SAR) imagery is one of the key applications for maritime security. Compared with single-channel SAR images, polarimetric SAR (PolSAR) data contains the fully-polarized information, which better facilitates better discriminating between targets, sea clutter, and interference. Therefore, many ship detection methods based on the polarimetric scattering mechanism have been studied. To deal with the false alarms caused by the existence of ghost targets, resulting from azimuth ambiguities and interference from side lobes, a modified polarimetric notch filter (PNF) is proposed for PolSAR ship detection. In the proposed method, the third eigenvalue obtained by the eigenvalue–eigenvector decomposition of the polarimetric covariance matrix is utilized to construct a new feature vector. Then, the target power can be computed to construct the modified PNF detector. On the one hand, the detection rate of ship targets can be enhanced by target-to-clutter contrast. On the other hand, false alarms resulting from azimuth ambiguities and side lobes can be reduced to an extent. Experimental results based on three C-band AIRSAR PolSAR datasets demonstrated the capability of the proposed PNF detector to improve detection performance while reducing false alarms. To be specific, the figure of merit (FoM) of the proposed method is the highest among comparative approaches with results of 80%, 100%, and 100% for the tested datasets, respectively.

Improved General Polarimetric Model-Based Decomposition for Coherency Matrix

A representative general polarimetric model-based decomposition framework was proposed by Chen et al., which implements a simultaneous full-parameter inversion by using complete polarimetric information and solves several limitations in previous decomposition methods. However, there are still shortcomings in Chen’s work. Firstly, only the real part of the parameter β in the generalized surface scattering model is considered. Secondly, inappropriate initial input values may lead to local optima in the nonlinear least squares optimization algorithm. Thirdly, the volume scattering component is underestimated in the volume scattering-dominated scene, but overestimated in buildings with large orientation (LOB) areas. Finally, nonlinear optimization is time-consuming computationally. To overcome those issues, an improved generalized polarimetric model-based decomposition method is proposed in this paper. The imaginary part of the parameter β is incorporated into the decomposition framework of the proposed method. Ingeniously utilizing the internal relationship in the generic equations composed of coherent matrix elements, the model parameters can be inversed by simplifying the nonlinear equations to linear equations. Therefore, compared with Chen’s method, the proposed method does not rely on the initial input values, and improves the computational efficiency. In addition, a hierarchical decomposition scheme is presented to solve the problem of underestimation or overestimation of volume scattering component mentioned above. The performance and advantages of this method are evaluated with L-band and C-band polarimetric synthetic aperture radar (PolSAR) data sets. Comparison studies are carried out with other model-based decomposition methods, demonstrating that the proposed method can further improve decomposition performance, especially in LOB areas.

Polarimetric L-Band ALOS2-PALSAR2 for Discontinuous Permafrost Mapping in Peatland Regions

Recently, it has been shown that the long penetrating polarimetric L-band ALOS is very promising for boreal and subarctic peatland mapping and monitoring. The unique information provided by the Touzi decomposition, and the dominant-scattering-type phase in particular, on peatland subsurface water flow permits an enhanced discrimination of bogs from fens, two peatland classes that can hardly be discriminated using conventional optical remote sensing sensors and C-band polarimetric SAR. In this study, the dominant and medium-scattering phases generated by the Touzi decomposition are investigated for discontinuous permafrost mapping in peatland regions. Polarimetric ALOS2, LiDAR, and field data were collected in the middle of August 2014, at the maximum permafrost thaw conditions, over discontinuous permafrost distributed within wooded palsa bogs and peat plateaus near the Namur Lake (Northern Alberta). The ALOS2 image, which was miscellaneously calibrated with antenna cross talk (−33 dB) much higher than the actual ones, was recalibrated. This led to a reduction of the residual calibration error (down to −43 dB) and permitted a significant improvement of the dominant and medium-scattering-type phase (20∘ to −30∘) over peatlands underlain by discontinuous permafrost. The Touzi decomposition, Cloude–Pottier α-H incoherent target scattering decomposition, and the HH-VV phase difference were investigated, in addition to the conventional multipolarization (HH, HV, and VV) channels, for discontinuous permafrost mapping using the recalibrated ALOS2 image. A LiDAR-based permafrost classification developed by the Alberta Geological Survey (AGS) was used in conjunction with the field data collected during the ALOS2 image acquisition for the validation of the results. It is shown that the dominant- and scattering-type phases are the only polarimetric parameters which can detect peatland subsurface discontinuous permafrost. The medium-scattering-type phase, ϕs2, performs better than the dominant-scattering-type phase, ϕs1, and permits a better detection of subsurface discontinuous permafrost in peatland regions. ϕs2 also allows for a better discrimination of areas underlain by permafrost from the nonpermafrost areas. The medium Huynen maximum polarization return (m2) and the minimum degree of polarization (DoP), pmin, can be used to remove the scattering-type phase ambiguities that might occur in areas with deep permafrost (more than 50 cm in depth). The excellent performance of polarimetric PALSAR2 in term of NESZ (−37 dB) permits the demonstration of the very promising L-band long-penetration SAR capabilities for enhanced detection and mapping of relatively deep (up to 50 cm) discontinuous permafrost in peatland regions.

Semi-Supervised Classification of PolSAR Images Based on Co-Training of CNN and SVM with Limited Labeled Samples.

Recently, convolutional neural networks (CNNs) have shown significant advantages in the tasks of image classification; however, these usually require a large number of labeled samples for training. In practice, it is difficult and costly to obtain sufficient labeled samples of polarimetric synthetic aperture radar (PolSAR) images. To address this problem, we propose a novel semi-supervised classification method for PolSAR images in this paper, using the co-training of CNN and a support vector machine (SVM). In our co-training method, an eight-layer CNN with residual network (ResNet) architecture is designed as the primary classifier, and an SVM is used as the auxiliary classifier. In particular, the SVM is used to enhance the performance of our algorithm in the case of limited labeled samples. In our method, more and more pseudo-labeled samples are iteratively yielded for training through a two-stage co-training of CNN and SVM, which gradually improves the performance of the two classifiers. The trained CNN is employed as the final classifier due to its strong classification capability with enough samples. We carried out experiments on two C-band airborne PolSAR images acquired by the AIRSAR systems and an L-band spaceborne PolSAR image acquired by the GaoFen-3 system. The experimental results demonstrate that the proposed method can effectively integrate the complementary advantages of SVM and CNN, providing overall classification accuracy of more than 97%, 96% and 93% with limited labeled samples (10 samples per class) for the above three images, respectively, which is superior to the state-of-the-art semi-supervised methods for PolSAR image classification.

Fusion of VNIR Optical and C-Band Polarimetric SAR Satellite Data for Accurate Detection of Temporal Changes in Vegetated Areas

In this paper, we propose a processing chain jointly employing Sentinel-1 and Sentinel-2 data, aiming to monitor changes in the status of the vegetation cover by integrating the four 10 m visible and near-infrared (VNIR) bands with the three red-edge (RE) bands of Sentinel-2. The latter approximately span the gap between red and NIR bands (700 nm–800 nm), with bandwidths of 15/20 nm and 20 m pixel spacing. The RE bands are sharpened to 10 m, following the hyper-sharpening protocol, which holds, unlike pansharpening, when the sharpening band is not unique. The resulting 10 m fusion product may be integrated with polarimetric features calculated from the Interferometric Wide (IW) Ground Range Detected (GRD) product of Sentinel-1, available at 10 m pixel spacing, before the fused data are analyzed for change detection. A key point of the proposed scheme is that the fusion of optical and synthetic aperture radar (SAR) data is accomplished at level of change, through modulation of the optical change feature, namely the difference in normalized area over (reflectance) curve (NAOC), calculated from the sharpened RE bands, by the polarimetric SAR change feature, achieved as the temporal ratio of polarimetric features, where the latter is the pixel ratio between the co-polar and the cross-polar channels. Hyper-sharpening of Sentinel-2 RE bands, calculation of NAOC and modulation-based integration of Sentinel-1 polarimetric change features are applied to multitemporal datasets acquired before and after a fire event, over Mount Serra, in Italy. The optical change feature captures variations in the content of chlorophyll. The polarimetric SAR temporal change feature describes depolarization effects and changes in volumetric scattering of canopies. Their fusion shows an increased ability to highlight changes in vegetation status. In a performance comparison achieved by means of receiver operating characteristic (ROC) curves, the proposed change feature-based fusion approach surpasses a traditional area-based approach and the normalized burned ratio (NBR) index, which is widespread in the detection of burnt vegetation.

Snow cover characterization using C-band polarimetric SAR in parts of the Himalaya

Assessment of the snow cover state in the initiation zone of an avalanche is essential for the snow avalanche hazard mapping in the mountainous region. Seasonal snow cover is very dynamic in the Himalaya and changes its state from dry to moist or wet according to temperature during the progression of winter. In this study, a methodology based on decomposition techniques using polarimetric synthetic aperture radar (SAR) data in conjunction with field snow wetness profiles is adapted for characterization of snow cover into dry and moist or wet snow in the Himalayan region. C-band Radarsat-2 full-polarization SAR data is utilized for polarimetric descriptors generation by applying various in-coherent target decomposition techniques, viz., Yamaguchi 4-component, Freeman Durden 3-component and Wishart Unsupervised (H-α). The generated polarimetric descriptors, viz., surface, volume, double-bounce and helix scattering are utilized in the supervised classification and Entropy (H) and mean scattering angle (α) for understanding the underlying scattering process happening within the snowpack. These decomposition models although describes various physical scattering mechanisms but are inadequate to provide quantitative estimates of underlying physical properties (as in our case changes in snowpack wetness). A supervised classification based on support vector machine (SVM) is used to characterize snow cover with changing conditions of snow wetness from dry to moist or wet. Three data sets of snow-accumulation and snow-melt period of winter 2015 are processed and analyzed along with field snow wetness (% by volume) profiles. A total 47 Nos. of snow wetness profiles of the snowpack are collected using snow fork instrument in synchronous with satellite passes at well distributed, identifiable field locations and are used in the training (34 Nos.) and testing (13 Nos.) of the SVM model. Snow being heterogeneous material varies metamorphically within snowpack. Based on changing weather conditions and sun exposure duration, it results into different scattering behavior in microwave region of the electromagnetic spectrum. It has been qualitatively observed from target decomposition results that polarimetric descriptors generated using Yamaguchi 4-component scattering decomposition have better captured the increase in the surface scattering (during February and March imagery, due to wet snow conditions of snow cover) from volume scattering (during January imagery, due to dry or moist snow conditions) from seasonal snowpack over the acquisition period from January to March 2015. Further to understand and comprehend the scattering process in the snowpack metamorphism with time, Wishart unsupervised (H-α) classification has been applied on all Radarsat-2 datasets. It was observed in the Himalayan region that during 29 Jan 2015 under dry snow conditions, anisotropic and multiple scattering processes dominates and during period between 22 Feb 2015 and 18 Mar 2015 under moist or wet snow conditions the dipole and Bragg type scattering are dominating. A confusion matrix has been generated for the accuracy assessment of the SVM based classification results, which shows overall accuracy ∼ 79.6 % (for 29 Jan 2015), 72.4 % (for 22 Feb 2015) and 74.5 % (for 18 Mar 2015), respectively with the testing samples. The C-band polarimetric SAR data shown its capability for snow cover type characterization and can be applied for operational applications in the avalanche zones of the snow bound terrain.

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.

Exploitation of sub-look analysis and polarimetric signatures for ship detection in PolSAR data

ABSTRACT Synthetic aperture radar (SAR) reveals a valuable contribution to ship detection due to its all-weather and all-illumination sensing capabilities. In this paper, a new polarimetric SAR (PolSAR) ship detector is proposed that jointly exploits sub-look scattering properties of point-like scatterers (i.e. ships) and polarimetric information. The detector, which is physically based on the concepts of coherent scatterers and polarimetric signatures, consists of four main steps: first, polarimetric signatures are extracted for each sub-look image; then, the correlation coefficient (CC) between the sub-look images is calculated for varying polarization bases; ship detection is performed by setting a threshold on the resulting CC image and finally, morphological filters are applied to delineate the contour of the detected ships and automatically generate the number of targets detected. The exploitation of polarimetric information through the polarimetric signatures, on one side provides more discriminative information about target and makes the detector more robust and accurate, on the other side, it increases the computation time. The detector’s performance is verified against actual L- and C-band PolSAR datasets. Experimental results demonstrate that the proposed detector outperforms state-of-the-art ones and it results in an area under the receiver operating characteristic (ROC) curve that ranges between 0.93 and 0.95.

Crop Monitoring and Classification Using Polarimetric RADARSAT-2 Time-Series Data Across Growing Season: A Case Study in Southwestern Ontario, Canada

Multitemporal polarimetric synthetic aperture radar (PolSAR) has proven as a very effective technique in agricultural monitoring and crop classification. This study presents a comprehensive evaluation of crop monitoring and classification over an agricultural area in southwestern Ontario, Canada. The time-series RADARSAT-2 C-Band PolSAR images throughout the entire growing season were exploited. A set of 27 representative polarimetric observables categorized into ten groups was selected and analyzed in this research. First, responses and temporal evolutions of each of the polarimetric observables over different crop types were quantitatively analyzed. The results reveal that the backscattering coefficients in cross-pol and Pauli second channel, the backscattering ratio between HV and VV channels (HV/VV), the polarimetric decomposition outputs, the correlation coefficient between HH and VV channelρ ρHHVV, and the radar vegetation index (RVI) show the highest sensitivity to crop growth. Then, the capability of PolSAR time-series data of the same beam mode was also explored for crop classification using the Random Forest (RF) algorithm. The results using single groups of polarimetric observables show that polarimetric decompositions, backscattering coefficients in Pauli and linear polarimetric channels, and correlation coefficients produced the best classification accuracies, with overall accuracies (OAs) higher than 87%. A forward selection procedure to pursue optimal classification accuracy was expanded to different perspectives, enabling an optimal combination of polarimetric observables and/or multitemporal SAR images. The results of optimal classifications show that a few polarimetric observables or a few images on certain critical dates may produce better accuracies than the whole dataset. The best result was achieved using an optimal combination of eight groups of polarimetric observables and six SAR images, with an OA of 94.04%. This suggests that an optimal combination considering both perspectives may be valuable for crop classification, which could serve as a guideline and is transferable for future research.

A D-vine copula quantile regression approach for soil moisture retrieval from dual polarimetric SAR Sentinel-1 over vegetated terrains

Soil moisture retrieval from Synthetic Aperture Radar (SAR) over vegetated terrains requires an isolation of soil and canopy signals from observed backscatter (σ°). This study develops a probabilistic soil moisture retrieval method from dual polarimetric C-band SAR Sentinel-1 (S-1) with uncertainty quantification at distinct vegetation covers (VCs). Both σVV° and σVH° were used to represent ground and volume scattering due to the high respective sensitivity to soil moisture and vegetation dynamics. A novel D-vine copula quantile regression (DVQR) was adopted to provide the soil moisture estimates based on modelling trivariate dependence of σVV°-σVH°-soil moisture anomalies (VV-VH-Mv), with a support from the innovative cosmic-ray soil moisture as ground-truth data. The feasibility of DVQR was underlined for: (1) multivariate nonlinear dependence structure modelling and (2) soil moisture retrieval with associated uncertainty. An inter-dependence analysis, which assesses the correlations among three major variables, indicated that the dependence between each pair of variables decreased as canopy density increases from herbs to forests, mainly due to the σ° attenuated by vegetation effect. The dependence structures simulated from the D-vine copula revealed highly nonlinear and asymmetric shapes with tail dependences occurred in most VCs, which can be well captured by different associated Archimedean copulas. Soil moisture anomaly (Mv) estimated using the DVQR and Multiple linear quantile regression (MLQR) were compared against ground-truth data for both in-sample and out-of-sample predictions. Superior performances of the DVQR in most VCs, with 10% and 16% improved in RMSE at grasslands and broadleaf forests, respectively, demonstrated the robustness of this method for S-1 soil moisture retrieval due to the highly nonlinear dependence structures captured by the D-vine models. Over VCs, better performances were obtained at low-canopy herbaceous regions (grasslands and croplands); whereas extremely dry conditions and complex structures in shrublands and dense forests resulted in inferior performances. A sensitivity analysis was then conducted to evaluate the change in Mv estimation accuracy given distinct VV and VH quantile levels. Result underlines that VV is the primary factor controlling the retrieval accuracy, but the increase in VH level also contributes to higher errors in soil moisture estimation, especially under wet conditions.

Assessment of Leaf Area Index of Rice for a Growing Cycle Using Multi-Temporal C-Band PolSAR Datasets

C-band polarimetric synthetic aperture radar (PolSAR) data has been previously explored for estimating the leaf area index (LAI) of rice. Although the rice-growing cycle was partially covered in most of the studies, details for each phenological phase need to be further characterized. Additionally, the selection and exploration of polarimetric parameters are not comprehensive. This study evaluates the potential of a set of polarimetric parameters derived from multi-temporal RADARSAT-2 datasets for rice LAI estimation. The relationships of rice LAI with backscattering coefficients and polarimetric decomposition parameters have been examined in a complete phenological cycle. Most polarimetric parameters had weak relationships (R2 < 0.30) with LAI at the transplanting, reproductive, and maturity phase. Stronger relationships (R2 > 0.50) were observed at the vegetative phase. HV/VV and RVI FD had significant relationships (R2 > 0.80) with rice LAI for the whole growth period. They were utilized to develop empirical models. The best LAI inversion performance (RMSE = 0.81) was obtained when RVI FD was used. The acceptable error demonstrated the possibility to use the decomposition parameters for rice LAI estimation. The HV/VV-based model had a slightly lower estimation accuracy (RMSE = 1.29) but can be a practical alternative considering the wide availability of dual-polarized datasets.

SIRV-Based High-Resolution PolSAR Image Speckle Suppression via Dual-Domain Filtering

This paper presents a spherically invariant random vector (SIRV)-based dual-domain filter for polarimetric synthetic aperture radar (PolSAR) speckle suppression. The SIRV model is a simple clutter decomposition model designed for heterogeneous scenes that decomposes the clutter into two independent domains: the texture domain and the polarimetric domain (also known as the speckle domain). The dual-domain filter takes full advantage of the SIRV model to decompose the task of PolSAR speckle suppression into polarimetric domain filtering and texture domain filtering. The polarimetric domain filtering involves estimating a stable state of the normalized coherency matrix with similar samples via fixed-point iteration. For this purpose, the nonlocal patch matching distance measure and the cutoff threshold in the SIRV model case are defined to select similar samples. The texture domain filtering involves reconstructing a sparse texture image without the effect of speckle. For this purpose, patch ordering-based SAR image despeckling via transform-domain filtering (SAR-POTDF), which is based on simultaneous sparse coding (SSC), is applied for the texture filtering. After the dual-domain filtering, a speckle-free PolSAR image can be reconstructed by the product of the texture and the normalized coherency matrix. The effectiveness and robustness of the proposed method was demonstrated by experiments undertaken with CETC-38 X-band, DLR F-SAR S-band, and IECAS C-band high-resolution PolSAR images. The DLR E-SAR L-band medium-resolution data were also used for comparison. The results showed that the dual-domain filtering is effective for the speckle suppression of high-resolution PolSAR images with heterogeneous and detailed scenes.

Evaluation of Radarsat-2 quad-pol SAR time-series images for monitoring groundwater irrigation

ABSTRACTGroundwater assists farmers to irrigate crops for fulfilling the crop-water requirement. Indian agriculture system is characterized by three cropping seasons known as Kharif (monsoon), Rabi (post-monsoon) and summer (pre-monsoon). In tropical countries like India, monitoring cropping practices using optical remote sensing during Kharif and Rabi seasons is constraint due to the cloud cover, which can be well addressed by microwave remote sensing. In the proposed research, the strength of C-band polarimetric Synthetic Aperture Radar (SAR) time series images were evaluated to classify groundwater irrigated croplands for the Kharif and Rabi cropping seasons of the year 2013. The present study was performed in the Berambadi experimental watershed of Kabini river basin, southern peninsular India. A total of fifteen polarimetric variables were estimated includes four backscattering coefficients (HH, HV, VH, VV) and eleven polarimetric indices for all Radarsat-2 SAR images. The cumulative temporal sum (seasonal and dual-season) of these parameters was supervised classified using Support Vector Machine (SVM) classifier with intensive ground observation samples. Classification results using the best equation (highest accuracy and kappa) shows that the Kharif, Rabi and irrigated double croplands are respectively 9.58 km2 (20.6%), 16.14 km2 (34.7%) and 6.22 km2 (13.4%) with a kappa coefficient respectively 0.84, 0.74 and 0.94.

Assessment of the X- and C-Band Polarimetric SAR Data for Plastic-Mulched Farmland Classification

We present a classification of plastic-mulched farmland (PMF) and other land cover types using full polarimetric RADARSAT-2 data and dual polarimetric (HH, VV) TerraSAR-X data, acquired from a test site in Hebei, China, where the main land covers include PMF, bare soil, winter wheat, urban areas and water. The main objectives were to evaluate the outcome of using high-resolution TerraSAR-X data for classifying PMF and other land covers and to compare classification accuracies based on different synthetic aperture radar bands and polarization parameters. Initially, different polarimetric indices were calculated, while polarimetric decomposition methods were used to obtain the polarimetric decomposition components. Using these polarimetric components as input, the random forest supervised classification algorithm was applied in the classification experiments. Our results show that in this study full-polarimetric RADARSAT-2 data produced the most accurate overall classification (94.81%), indicating that full polarization is vital to distinguishing PMF from other land cover types. Dual polarimetric data had similar levels of classification error for PMF and bare soil, yielding mapping accuracies of 53.28% and 59.48% (TerraSAR-X), and 59.56% and 57.1% (RADARSAT-2), respectively. We found that Shannon entropy made the greatest contribution to accuracy in all three experiments, suggesting that it has great potential to improve agricultural land use classifications based on remote sensing.

Joint estimation of Plant Area Index (PAI) and wet biomass in wheat and soybean from C-band polarimetric SAR data

Retrieval of the Plant Area Index (PAI) and wet biomass from polarimetric SAR (PolSAR) data is of paramount importance for in-season monitoring of crop growth. Notably, the joint estimation of biophysical parameters might be effective instead of an individual parameter due to their inherent relationships (possibly nonlinear). The semi-empirical water cloud model (WCM) can be suitably utilized to estimate biophysical parameters from PolSAR data. Nevertheless, instability problems could occur during the model inversion process using traditional inversion approaches. Iterative optimization (IO) can have difficulty in finding the global minima while look up table (LUT) searches have a lower generalization capability. These challenges reduce the transferability of IO and LUT search inversions in computational efficiency and seldom account for the inter-correlation among the parameters. Alternatively, a machine learning regression technique with a regularization routine may provide a stable and optimum solution for ill-posed problems related to the inversion of the WCM. In the present work, the crop biophysical parameters viz. PAI and wet biomass are estimated simultaneously using the multi-target Random Forest Regression (MTRFR) technique. The accuracy of the retrieval method is analyzed using the in-situ measurements and quad-pol RADARSAT-2 data acquired during the SMAPVEX16 campaign over Manitoba, Canada. The inversion process is tested with different polarization combinations of SAR data for wheat and soybean. The validation used ground measured biophysical parameters for various crops, indicating promising results with a correlation coefficient (r) in the range of 0.6–0.8. In addition, the relationship between PAI and wet biomass using the multi-target and single output model is also assessed based on in-situ measurements. The results confirm that the inter-correlation between biophysical parameters is well preserved in the MTRFR based joint inversion technique for both wheat and soybean.

Co-polarization channel imbalance phase estimation by corner-reflector-like targets

Polarimetric calibration is a critical step to suppress the potential system distortion before implementing any applications for polarimetric synthetic aperture radar (PolSAR). Among all the distortion elements, the crosstalk and cross-pol channel imbalance are generally estimated by the use of natural media, and the co-pol channel imbalance is traditionally solved by the use of corner reflectors (CRs). However, the deployment of ground CRs is costly and may even be impossible in some areas. Many bright point targets, such as poles, lamps, and corner points of structures, are commonly found in manmade regions. In particular, if the object orientation is parallel or perpendicular to the radar flight direction, some points will present similar polarimetric responses to trihedral or dihedral CRs. These points, which are referred to here as “CR-like targets”, can be treated as a supplement to approximately solve the system distortion elements when CRs are unavailable. In this paper, we propose a novel step-by-step algorithm to determine the CR-like targets and estimate the co-pol channel imbalance phase in uncalibrated PolSAR imagery. Chinese X-band airborne and C-band satellite PolSAR data were used to test the proposed method. Compared with the CR-derived co-pol channel imbalance phase, the solution errors of the CR-like targets were 1.305° and 0.03° for the X- and C-band experiments, respectively. The results of the experiments confirm that the solutions of the CR-like targets are very close to those of ground-deployed CRs, and the proposed method can be considered as an effective way to calibrate PolSAR images when sufficient CR-like point targets are detected in manmade regions.

A quad polarimetric SAR calibration algorithm using rotation symmetry

ABSTRACTPolarimetric calibration, precisely deciphering the polarimetric information hidden in the polarized characteristics of a radar scene, is the core step before applying quad polarimetric synthetic aperture radar (PolSAR) data for ground parameter inversion and classification. The previously published techniques for polarimetric calibration generally use at least one known point calibration target to completely determine the polarimetric distortions (cross-talks and channel imbalances) between the various polarized channels. This paper describes a novel method that solely relies on the image itself with the property of rotation symmetry. The algorithm derives the entire cross-talk and channel imbalance parameters using an iterative operation to circularly modify the observed average covariance matrix, which is independent on the known point calibration targets in the illuminated scene. The proposed method is validated to be reliable and efficient through polarimetric calibration experiments using the airborne C-band and RadarSat-2 quad polarimetric SAR images. The experimental results indicate that the new technique achieves similar calibration precision to the Ainsworth algorithm but without using any known calibration point target.