Quad-pol Data Research Articles

Polarimetric Measures in Biomass Change Prediction Using ALOS-2 PALSAR-2 Data

The use of multiple synthetic aperture radar polarizations can improve biomass estimations compared to using a single polarization. In this study, we compared predictions of aboveground biomass change from ALOS-2 PALSAR-2 backscatter using linear regression based on (1) the cross-polarization channels, (2) the co- and cross-polarizations from fully polarimetric SAR, (3) Freeman–Durden polarimetric decomposition, and (4) the polarimetric radar vegetation index (RVI). Additionally, the impact of forest structure on the sensitivity of the polarimetric backscatter to AGB and AGB change was assessed. The biomass consisted of mainly coniferous trees at the hemi-boreal test site Remningstorp, located in southern Sweden. We found some improvements in the predictions when quad-polarized data (RMSE = 79.4 tons/ha) were used instead of solely cross-polarized data (RMSE = 84.9 tons/ha). However, when using Freeman–Durden decomposition, the prediction accuracy improved further (RMSE = 69.7 tons/ha), and the highest accuracy was obtained with the radar vegetation index (RMSE = 50.4 tons/ha). The corresponding R2 values ranged from 0.45 to 0.82. The bias was less than 1 t/ha for all models. An analysis of forest variables showed that the sensitivity to AGB was reduced for high values of basal-area-weighted mean height, basal area, and stem density when predicting absolute AGB, but the best change prediction model was sensitive to changes larger than the apparent saturation point for AGB state estimates. We conclude that by using fully polarimetric SAR images, forest biomass changes can be estimated more accurately compared to using single- or dual-polarization images. The results were improved the most (in terms of RMSE and R2) by using the Freeman–Durden decomposition model or the RVI, which captured especially the large changes better.

A Channel Imbalance Calibration Scheme with Distributed Targets for Circular Quad-Polarization SAR with Reciprocal Crosstalk

As polarimetric antennas can be isolated through excellent electronic frameworks in circular quad-polarization (CQP) synthetic aperture radar (SAR) systems, cross-polarization (x-pol) and co-polarization (co-pol) channel imbalances are more challenging and essential to calibrate than crosstalk in polarimetric calibration (PolCAL). In uncalibrated CQP SAR images without corner reflectors (CRs), the reciprocity and reflection symmetry assumptions of the distributed targets are commonly used to estimate the x-pol and co-pol channel imbalances, respectively. To suppress the influence of additive noise on determining channel imbalances through distributed targets, high signal-to-noise ratio (SNR) distributed targets should be obtained from the x-pol and co-pol channels of the CQP SAR images: namely, surface-dominated and volume-dominated targets. However, some reflection symmetry assumptions used in the existing calibration literature have poor applicability with volume-dominated targets, resulting in unsatisfactory estimation results for the co-pol channel imbalance phase. In this paper, we assess the priority of the reflection symmetry properties of volume-dominated targets used to calibrate the co-pol channel imbalance phase in CQP SAR data synthesized from linear quad-polarization data of ALOS, GF-3, and RADARSAT-2. In the theoretical part, high-priority reflection symmetry (termed semireflection symmetry) assumptions are confirmed as the most suitable for estimating the co-pol channel imbalance phase, and were selected to develop an algorithm for estimating the co-pol channel imbalance phase. Furthermore, based on the novel method for estimating the co-pol channel imbalance phase, a channel imbalance calibration scheme is proposed for CQP SAR systems with reciprocal crosstalk, including extracting surface-dominated and volume-dominated targets, and estimating and filtering channel imbalances. We demonstrate the effectiveness of our proposed scheme with CRs in simulated CQP SAR images. The experimental results show that the calibration scheme is an effective workflow for estimating channel imbalances in CQP SAR systems with reciprocal crosstalk.

Extension of Scattering Power Decomposition to Dual-Polarization Data for Tropical Forest Monitoring

A new scattering power decomposition method is developed for accurate tropical forest monitoring that utilizes data in dual-polarization mode instead of quad-polarization (POLSAR) data. This improves the forest classification accuracy and helps to realize rapid deforestation detection because dual-polarization data are more frequently acquired than POLSAR data. The proposed method involves constructing scattering power models for dual-polarization data considering the radar scattering scenario of tropical forests (i.e., ground scattering, volume scattering, and helix scattering). Then, a covariance matrix is created for dual-polarization data and is decomposed to obtain three scattering powers. We evaluated the proposed method by using simulated dual-polarization data for the Amazon, Southeast Asia, and Africa. The proposed method showed an excellent forest classification performance with both user’s accuracy and producer’s accuracy at >98% for window sizes greater than 7 × 14 pixels, regardless of the transmission polarization. It also showed a comparable deforestation detection performance to that obtained by POLSAR data analysis. Moreover, the proposed method showed better classification performance than vegetation indices and was found to be robust regardless of the transmission polarization. When applied to actual dual-polarization data from the Amazon, it provided accurate forest map and deforestation detection. The proposed method will serve tropical forest monitoring very effectively not only for future dual-polarization data but also for accumulated data that have not been fully utilized.

Forest plantation species classification using Full-Pol-Time-Averaged SAR scattering powers

Forest plantation species identification is essential for biodiversity conservation, sustainable management, and carbon sequestration. Fully Polarimetric Synthetic Aperture Radar (Full-Pol-SAR) remote sensing data aids as an alternative to time-consuming field inventory and cloud-covered optical data for forest plantation species identification. This study tried to identify forest plantation species using Fully Polarimetric Time-series Averaged SAR (Full-Pol-Time-Averaged SAR) datasets. Due to the availability of time series quad polarization data of TerraSAR-X from December 20, 2014 to March 07, 2015 and near-real-time field with us for the Haldwani forest area, we have utilized these data sets for the proposed work. The temporal effect of phenological changes can be characterized using the Full-Pol-SAR 6-component Scattering power Decomposition (6SD) and is utilized to identify different forest species. Misclassification problem exists in forests with single-date 6SD images, an improved forest species classification approach based on time-averaged 6SD and a supervised Random Forest (RF) classifier is proposed. This study reveals that Teak, Eucalyptus, Poplar, mixed, and non-forest could be distinctly identified and classified in time-averaged 6SD images. The overall classification accuracy of the proposed approach (∼83.72%) was, higher than the single-date Full-Pol-SAR results (mean overall accuracy with standard deviation 68.949 ± 2.096%).

Distributed Scatterer Processing Based on Binary Partition Trees with Multi-Baseline PolInSAR Data

Distributed scatterers (DSs) are necessary to increase point density in multi-temporal InSAR (MT-InSAR) monitoring. The identification of homogeneous pixels (HPs) is the first and key step for DS processing to overcome the low signal-to-noise ratio condition. Since multi-polarization data are good at describing geometrical structures and dielectric properties of ground objects, they have been applied for HP identification. However, polarimetric information is not enough for identifying areas with similar ground objects but different deformation. We propose a novel DS preprocessing algorithm based on polarimetric interferometric homogeneous pixel (PIHP) identification. Firstly, a novel Polarimetric InSAR (PolInSAR) similarity that combines polarimetric intensity, interferometric coherence, and phase is proposed, which is readily available in multi-baseline and multi-polarization data and flexible by controlling weighting factors. Secondly, based on the binary partition tree (BPT) framework, object-orientated multi-scale PIHP identification is achieved, which is suitable for complex deformation scenes. Tested with simulated quad-polarization data, our method shows improvement in phase quality and point density, especially in the deformed areas, compared with the traditional HP identification method based on the polarimetric homogeneity (PolHom) test and the method with ground object type map. Tested with 30 quad-polarization Radarsat-2 images over Kilauea Volcano, the point density of our method is three times higher than that of the PolHom test in vegetation areas. Our method is proven to be more sensitive and mechanically more advanced to homogeneous pixels identification than the traditional ones, which is helpful for phase optimization, spatial enlargement of monitoring points, and stability of the MT-InSAR algorithm.

Potential of ALOS2 Polarimetric Imagery to Support Management of Poplar Plantations in Northern Italy

Poplar is one of the most widespread fast-growing forest species. In Northern Italy, plantations are characterized by large interannual fluctuations, requiring frequent monitoring to inform on wood supply and to manage the stands. The use of radar satellite data is proving useful for forest monitoring, being weather independent and sensitive to the changes in forest canopy structure, but it has been scarcely tested in the case of poplar. Here, L-band ALOS2 (Advanced Land Observing Satellite-2) dual-pol data were tested to detect clear-cut plantations in consecutive years. ALOS2 quad-pol data were used to discriminate among different age classes, a much complex task than detecting poplar plantations extent. Results from different machine learning algorithms indicate that with dual-pol data, poplar forest can be discriminated from clear-cut areas with 80% overall accuracy, similar to what is usually obtained with optical data. With quad-pol data, four age classes were classified with moderate overall accuracy (73%) based on polarimetric decompositions, three 3 age classes with higher accuracy (87%) based on HV band. Sources of error are represented by poplar areas of intermediate age when stems, branches and leaves were not developed enough to detect by scattering mechanisms. This study demonstrates the feasibility of monitoring poplar plantations with satellite radar, which represents a growing source of information thanks to already-planned future satellite missions.

Polarimetric information content of Sentinel-1 for land cover mapping: An experimental case study using quad-pol data synthesized from complementary repeat-pass acquisitions

This work presents a technique to merge two Sentinel-1 image products of complementary polarimetric information (HH/HV and VH/VV) to derive pseudo-polarimetric features, such as polarimetric covariance, but also model-based and eigenvalue-based decompositions and an unsupervised Wishart classification of scattering types. The images were acquired within a 6-day period over Southern Germany and have been processed to mimic an actual quad-pol product. This was analyzed statistically, visually and within several classification processes to get an understanding of how well such a dataset depicts scattering mechanisms and other polarimetric features as inputs for land use and land cover mapping. A systematic comparison with the original dual-polarization product showed an increase in information content and largely feasible polarimetric features. Yet, especially the average Alpha angle was found to be biased and too high for some of the compared surfaces. Despite these inaccuracies, the polarimetric features turned out to improve potential land cover mapping as compared with backscatter intensities and dual-polarization features of the input products alone. Among the most significant variables related to land use and cover reported by an independent dataset, Entropy, the co-polarization ratio and the C22 element of the covariance matrix generated the strongest impact on the class separability, although misclassifications between physically related classes remain. Yet, the findings are encouraging concerning further investigation of the polarimetric potential to combine repeat-pass acquisitions of Sentinel-1 for a better description of more specific types of land cover.

PolSAR Ship Detection Based on a SIFT-like PolSAR Keypoint Detector

The detection of ships on the open sea is an important issue for both military and civilian fields. As an active microwave imaging sensor, synthetic aperture radar (SAR) is a useful device in marine supervision. To extract small and weak ships precisely in the marine areas, polarimetric synthetic aperture radar (PolSAR) data have been used more and more widely. We propose a new PolSAR ship detection method which is based on a keypoint detector, referred to as a PolSAR-SIFT keypoint detector, and a patch variation indicator in this paper. The PolSAR-SIFT keypoint detector proposed in this paper is inspired by the SAR-SIFT keypoint detector. We improve the gradient definition in the SAR-SIFT keypoint detector to adapt to the properties of PolSAR data by defining a new gradient based on the distance measurement of polarimetric covariance matrices. We present the application of PolSAR-SIFT keypoint detector to the detection of ship targets in PolSAR data by combining the PolSAR-SIFT keypoint detector with the patch variation indicator we proposed before. The keypoints extracted by the PolSAR-SIFT keypoint detector are usually located in regions with corner structures, which are likely to be ship regions. Then, the patch variation indicator is used to characterize the context information of the extracted keypoints, and the keypoints located on the sea area are filtered out by setting a constant false alarm rate threshold for the patch variation indicator. Finally, a patch centered on each filtered keypoint is selected. Then, the detection statistics in the patch are calculated. The detection statistics are binarized according to the local threshold set by the detection statistic value of the keypoint to complete the ship detection. Experiments on three data sets obtained from the RADARSAT-2 and AIRSAR quad-polarization data demonstrate that the proposed detector is effective for ship detection.

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.

Polarimetric analysis of L-band DFSAR data of Chandrayaan-2 mission for ice detection in permanently shadowed regions (PSRs) of lunar South polar craters

To confirm the presence of water on the moon, many scientists of the world are making continuous efforts through remote sensing data of different missions. In this direction, the Dual Frequency Synthetic Aperture Radar (DFSAR) sensor of the Chandrayaan-2 mission adds a very important chapter which is the world's first Planetary SAR mission of fully polarimetric capability with L- and S-band. This study utilizes the L-band fully polarimetric DFSAR data of Chandrayaan-2 mission for the PolSAR parameters-based analysis and ice detection in permanently shadowed regions (PSRs) of the lunar South Polar craters. The PSR IDs SP_875930_3125710, SP_837670_3387710, and SP_874930_3578760 of the lunar South Pole were selected for the polarimetric analysis using DFSAR L-band. Based on previous studies ((Li et al., 2018), two out of three PSR Ids (SP_875930_3125710 and SP_874930_3578760) were easy to identify for surface ice. That is why only two PSR IDs were used for polarimetric SAR analysis of DFSAR data for surface ice characterization and detection. The hybrid polarimetric simulation was also performed to the fully polarimetric L-band data to study stokes vectors and associated child parameters for the selected study area. The analysis of polarimetric distortions confirms the persistence of the polarimetric quality of the SAR data and for this, the polarimetric distortion analysis was performed with co-pol and cross-pol channels. Wave dichotomy-based Huynen decomposition and Barnes decomposition models were implemented to the fully polarimetric quad-pol DFSAR data. The eigenvalue-eigenvector-based decomposition model was also implemented to characterize the scattering behavior of the PSRs. A high correlation was obtained between Circular Polarization Ratio (CPR), entropy, and alpha for the 200 hundred points randomly collected from the image. Diversity index also showed a high positive correlation with CPR. The polarimetric quality of the data was evaluated with the scatterplot between the cross-polarimetric channels and it was observed that the L-band quad-pol data of DFSAR satisfies the criteria for PolSAR data of a monostatic SAR system. Analysis of the results obtained from the polarimetric SAR data indicated that the high volumetric scattering and CPR for the PSR ID SP_875930_3125710 may be due to ice clusters within the permanently shadowed region. Polarimetric analysis of the PSR (SP_874930_3578760) at Howarth Crater using L-band DFSAR data shows a low amount of volumetric scattering and a low CPR for most locations in the PSR. The different ranges of CPR and volume scattering for both craters indicate that polarimetric parameters and indices should be studied in conjunction with geomorphological parameters of the lunar surface, for unambiguous identification of surface ice clusters in the PSR.

Using the Pauli Scattering Mechanisms for Analysis of Polarimetric SAR Images from RADARSAT Constellation and TanDEM-X Missions

This work describes techniques for extraction of information in polarimetric images from RADARSAT constellation mission (RCM) and TanDEM-X (TDX) and TerraSAR-X (TSX) satellites. Using the boundary conditions of Maxwell's equations it is shown how the single bounce and double bounce scattering can be extracted from quad-pol and compact polarimetric (CP) SAR. We develop new equations to calculate the Pauli decomposition from simulated CP data using RCM quad-pol data. The RCM CP option is circular transmit, linear receive (CTLR). We find that using quad-pol data performs better than using CP data for Pauli decomposition, especially extraction of the HV component. Estimation of single bounce and double bounce scattering from RCM CP data is feasible and yields more information about scattering properties than the CP components alone. We show how more information can be extracted from bistatic TSX/TDX quad-pol data by estimation of the coherence of the different Pauli components and optimization of the interferometric coherence using ships as detection objects. This is a unique opportunity to use optimized coherence on moving ships for characterizing their structure.

Pseudo Quad-Pol Simulation From Compact Polarimetric SAR Data via a Complex-Valued Dual-Branch Convolutional Neural Network

Compact polarimetry (CP) has attracted much attention in recent years due to its hybrid dual polarization imaging mode. CP synthetic aperture radar (SAR) has a larger swath and can provide more polarimetric information compared with the traditional dual polarization imaging mode (HH/HV or VH/VV). Pseudo quad-polarimetric (quad-pol) data simulation is an important technology in the application of CP data. The goal of pseudo quad-pol data simulation from CP data is to change the form of CP data to the form of quad-pol data without increasing any new information. In this work, a new pseudo quadpol data simulation method from the CP data is proposed. This method combines a complex-valued dual-branch convolutional neural network (CV-DBCNN) to achieve the simulation of the pseudo quad-pol data. It utilizes complex-valued convolutional layers and complex-valued activation function to fully extract the polarimetric information embedded in the complex-valued CP data. For the CV-DBCNN, the branch with 11 kernel size is used to nonlinearly and self-adaptively combine the channel of input data, the branch with 33 kernel size is used to extract the discriminative regional polarimetric features. Furthermore, polarimetric decomposition is utilized to evaluate the scattering mechanisms of the pseudo simulated quad-pol data. Three state-of-the-art methods are utilized for comparison. In comparison with other methods, our proposed reconstruction method based on the CVDBCNN shows its superiority in terms of the pseudo quad-pol data reconstruction and scattering mechanism preservation.

Analysis of the Reconstruction Accuracy of Compact Polarized SAR Pseudo Quad-Pol Data for Use in Ocean Surface Observations

Analysis of the Reconstruction Accuracy of Compact Polarized SAR Pseudo Quad-Pol Data for Use in Ocean Surface Observations

Dark spot detection for characterization of marine surface slicks using UAVSAR quad-pol data

Oil spills are a potential hazard, causing the deaths of millions of aquatic animals and this leaves a calamitous effect on the marine ecosystem. This research focuses on evaluating the potential of polarimetric parameters in discriminating the oil slick from water and also possible thicker/thinner zones within the slick. For this purpose, L-band UAVSAR quad-pol data of the Gulf of Mexico region is exploited. A total number of 19 polarimetric parameters are examined to study their behavior and ability in distinguishing oil slick from water and its own less or more oil accumulated zones. The simulation of compact-pol data from UAVSAR quad-pol data is carried out which has shown good performance in detection and discrimination of oil slick from water. To know the extent of separation between oil and water classes, a statistical separability analysis is carried out. The outcomes of each polarimetric parameter from separability analysis are then quantified with the radial basis function (RBF) supervised Support Vector Machine classifier followed with an accurate estimation of the results. Moreover, a comparison of the achieved and estimated accuracy has shown a significant drop in accuracy values. It has been observed that the highest accuracy is given by LHV compact-pol decomposition and coherency matrix with a classification accuracy of ~ 94.09% and ~ 94.60%, respectively. The proposed methodology has performed well in discriminating the oil slick by utilizing UAVSAR dataset for both quad-pol and compact-pol simulation.

A Phase Optimization Method for DS-InSAR Based on SKP Decomposition From Quad-Polarized Data

A novel distributed scatterer interferometric synthetic aperture radar (DS-InSAR) method is presented in which the sum of Kronecker product (SKP) decomposition method is applied to DS candidates. Unlike existing polarimetric optimization methods, the proposed method considers polarimetric and interferometric coherence information simultaneously, resulting in separation of the polarimetric scattering process for each target and the maximum diversity for the corresponding phase center locations. Physical reliability of the phase optimization solution is thereby ensured. The performance of the novel method is evaluated using 30 quad-polarized C-band Radarsat-2 synthetic aperture radar (SAR) images over Kilauea Volcano, Hawaii. The proposed method provides a higher density of measurement scatterer (MS) points and a higher quality of DS interferometric phase with a temporally stable phase center than single-polarization (HH) method and quad-polarization exhaustive search polarimetric optimization (ESPO) method. Thus, the proposed method shows good performance in phase quality improvement and point density increasement.

Machine Learning for Arctic Sea Ice Physical Properties Estimation Using Dual-Polarimetric SAR Data

This work introduces a novel method that combines machine learning (ML) techniques with dual-polarimetric (dual-pol) synthetic aperture radar (SAR) observations for estimating quad-polarimetric (quad-pol) parameters, which are presumed to contain geophysical sea ice information. In the training phase, the output parameters are generated from quad-pol observations obtained by Radarsat-2 (RS2), and the corresponding input data consist of features obtained from overlapping dual-pol Sentinel-1 (S1) data. Then, two, well-recognized ML methods are studied to learn the functional relationship between the output and input data. These ML approaches are the Gaussian process regression (GPR) and neural network (NN) for regression models. The goal is to use the aforementioned ML techniques to generate Arctic sea ice information from freely available dual-pol observations acquired by S1, which can, in general, only be generated from quad-pol data. Eight overlapping RS2 and S1 scenes were used to train and test the GPR and NN models. Statistical regression performance measures were computed to evaluate the strength of the ML regression methods. Then, two scenes were selected for further evaluation, where overlapping optical images were available as well. This allowed the visual interpretation of the maps estimated by the ML models. Finally, one of the methods was tested on an entire S1 scene to perform prediction on areas outside of the RS2 and S1 overlap. Our results indicate that the studied ML techniques can be utilized to increase the information retrieval capacity of the wide swath dual-pol S1 imagery while embedding physical properties in the methodology.

Oil Spill Four-Class Classification Using UAVSAR Polarimetric Data

Oil pollution of oceans from various sources is a devastating environmental problem and immediate detection of oil spills is crucial. Remote sensing techniques have provided an unprecedented opportunity for early oil spill detection and classification with an easy, quick, and cheap approach. Moreover, Fully Polarimetric Synthetic Aperture Radar (PolSAR) data with unique capabilities and informative features is an immense data source for oil spill detection on large scales. The objective of the present study is to utilize PolSAR data not only for oil spill detection, but also to classify the detected oil spill in the ocean into four classes: thick oil, thin oil, oil/water mixture, and clear water. In this study, numerous polarimetric decomposition parameters and texture features are extracted from the PolSAR image. A two-phase feature selection method, manually selection based on oil and water surface backscattering behavior and an optimization algorithm, has been employed on the extracted features to select the optimum feature set. The selected feature set has been used to classify the PolSAR image into oil and water classes. Moreover, the high sensitivity and discriminative power of the validation PolSAR dataset, UAVSAR L-band quad-pol data, is exploited by classifying the image into four classes. Remarkable acquired classification accuracies of 90.21% and 85.41% and Kappa coefficient of 0.8052 and 0.7905 for two-class and four-class classifications, respectively, demonstrate the robustness and high potential of the proposed methodology for oil spill detection and classification.

BiophyNet: A Regression Network for Joint Estimation of Plant Area Index and Wet Biomass From SAR Data

In this study, we propose a sequence-to-sequence neural network architecture to jointly estimate the plant area index (PAI) and wet biomass of canola and soybean. The PAI and wet biomass have considerable importance for crop growth stage mapping and monitoring. RADARSAT-2 quad-pol data along with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> measurements of canola and soybean obtained from the SMAPVEX16 campaign over Manitoba, Canada, are utilized for evaluating the efficiency and accuracy of the proposed estimation methodology. The analysis indicates promising results for the two crops with a correlation coefficient <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(r)$ </tex-math></inline-formula> in the range of 0.69–0.87. The results also confirm intercorrelation between the PAI and wet biomass for canola and soybean.

Observations of SAR polarimetric parameters of lake and fast sea ice during the early growth phase

This study examines links between two sets of parameters obtained from Radarsat-2 Quad-pol data and geophysical information of lake ice and landfast sea ice in the Resolute Bay area, Nunavut, Canada. The first set includes the three orthogonal backscatter coefficients (σhh0, σvv0, σvh0) in addition to the total backscatter power (SPAN). The second set includes polarimetric decomposition parameters obtained using Cloude-Pottier and Yamaguchi decomposition methods. Data were collected during the early freezing period from 20 September until 28 December 2017. Meteorological and ice climatological data from the weather station of Resolute Bay were used to facilitate this task. Ice thickness and salinity were obtained from established empirical models. Results show that single-bounce and multiple-bounce scattering mechanisms are active in lake ice during the entire freezing period while multiple-bounce scattering is active in sea ice only during the initial freezing. Backscatter parameters can be used to determine the thickness of lake ice up to any depth, while both backscatter and the tested decomposition parameters are related to the thickness of young sea ice (<30 cm thick). Physical explanations are offered in each case. Spatial maps of ice thickness are produced. Polarimetric decomposition parameters demonstrate erratic behavior throughout the development of lake ice. High entropy (>0.6) is found to be associated with comparable single- and multiple-bounce scattering mechanisms, hence can be used to identify new ice. Orthogonal backscatter coefficients and polarimetric decomposition parameters provide different information, yet their combination offers clues to better identify ice conditions and investigate questions about scattering mechanisms.

An assessment of temporal RADARSAT-2 SAR data for crop classification using KPCA based support vector machine

Crop discrimination with synthetic aperture radar (SAR) data primarily depends on the characterization of crop geometry using radar backscatter response. Differences in phenological development of crops lead to dissimilar temporal signatures of backscatter intensities, which may influence the separability of the crop classes. This principle leads to multi-date classification approach. In this work, kernel principal component (KPCA) is adopted for feature selection from multi-date datasets, and the selected features are used for classification using support vector machine (SVM) classifier. The classification is investigated for both the KPCA-based SVM and only SVM approaches using quad-pol C-band RADARSAT-2 data acquired over the test site in Vijayawada, India. KPCA-based SVM classification shows an overall accuracy of 89%, which is better than 82% obtained using the SVM-based classification. The proposed methodology effectively incorporates the temporal crop information during classification.