Fine Quad-Pol Research Articles

Monitoring crop growth using a canopy structure dynamic model and time series of synthetic aperture radar (SAR) data

ABSTRACT Normalized Difference Vegetation Index (NDVI) time series data are used by agricultural agencies for many essential operational crop monitoring programmes. But optical sensors often miss key growth stages due to cloud cover interference, impacting the performance of operational activities. Although the synergistic use of optical and Synthetic Aperture Radar (SAR) imagery can provide time series data, any SAR-optical integration necessitates building a relationship between these two data sources. The objective of this study was to use a semi-empirical Canopy Structure Dynamics Model (CSDM), Growing Degree Days (GDD), and SAR parameters calibrated to optical NDVI to derive daily estimates of canola crop condition over an entire growing season. RADARSAT-2 Fine Quad-pol and RapidEye images were collected over three years for a study site in western Canada. Object-based image analysis was applied to study the relationship between the optical and SAR time series data. Significant correlations were documented between a number of SAR parameters and optical NDVI, specifically a ratio of backscatter intensities (HH-HV)/(HH+HV), a ratio of volume to surface scattering extracted from the Freeman Durden decomposition, and Entropy from the Cloude-Pottier decomposition. Correlations (r-values) between these SAR parameters and optical NDVI ranged from 0.63 to 0.84 for the three years of data. Based on this analysis, a simple statistical model was used to relate SAR parameters to optical NDVI, creating a SAR-calibrated NDVI (SARcal-NDVI). A CSDM was fit to the SARcal-NDVI for each canola field, constructing a temporal vegetation index curve which captured canopy development from emergence to senescence. Coefficients of determination (R2) were 0.87 0.86 and 0.82 for entropy, the volume-surface scattering ratio, and the ratio of backscatter intensities (HH-HV)/(HH+HV), respectively, demonstrating a good model fit. The CSDM describes well the temporal evolution of SARcal-NDVI. Using the CSDM, SARcal-NDVI and GDD, the canola condition can be estimated for any given day in the growing season. In fact when the CSDM was used to estimate SARcal-NDVI for the exact days of RapidEye acquisitions, correlations with optically derived NDVI were high. The strongest correlations with RapidEye NDVI were reported for the volume-surface scattering ratio (R2 of 0.69 and RMSE of 0.15). The SARcal-NDVI estimated from the CSDM was also physically meaningful. Field-based biomass was significantly correlated (R2 of 0.79) with the SARcal-NDVI calculated using the volume-surface scattering ratio. Although further research is needed to extend this method to other crops, these results demonstrate that SAR data can be used to estimate vegetation conditions and when coupled with a CSDM, integrated into current monitoring operations based on optical NDVI. As a next step, the research team will be assessing SARcal-NDVI in a national operational programme which reports on crop yields using modelling with optical-based NDVI.

Investigation of the effect of the incidence angle on land cover classification using fully polarimetric SAR images

ABSTRACT Incidence angle is one of the most important imaging parameters that affect polarimetric SAR (PolSAR) image classification. Several studies have examined the land cover classification capability of PolSAR images with different incidence angles. However, most of these studies provide limited physical insights into the mechanism how the variation of incidence angle affects PolSAR image classification. In the present study, land cover classification was conducted by using RADARSAT-2 Wide Fine Quad-Pol (FQ) images acquired at different incidence angles, namely, FQ8 (27.75°), FQ14 (34.20°), and FQ20 (39.95°). Land cover classification capability was examined for each single-incidence angle image and a multi-incidence angle image (i.e., the combination of single-incidence angle images). The multi-incidence angle image produced better classification results than any of the single-incidence angle images, and the different incidence angles exhibited different superiorities in land cover classification. The effect mechanisms of incidence angle variation on land cover classification were investigated by using the polarimetric decomposition theorem that decomposes radar backscatter into single-bounce scattering, double-bounce scattering and volume scattering. Impinging SAR easily penetrated crops to interact with the soil at a small incidence angle. Therefore, the difference in single-bounce scattering between trees and crops was evident in the FQ8 image, which was determined to be suitable for distinguishing between croplands and forests. The single-bounce scattering from bare lands increased with the decrease in incidence angles, whereas that from water changed slightly with the incidence angle variation. Consequently, the FQ8 image exhibited the largest difference in single-bounce scattering between bare lands and water and produced the fewest confusion between them among all the images. The single- and double-bounce scattering from urban areas and forests increased with the decrease in incidence angles. The increase in single- and double-bounce scattering from urban areas was more significant than that from forests because C-band SAR could not easily penetrate the crown layer of forests to interact with the trunks and ground. Therefore, the FQ8 image showed a slightly better performance than the other images in discriminating between urban areas and forests. Compared with other crops and trees, banana trees caused stronger single- and double-bounce scattering because of their large leaves. As a large incidence angle resulted in a long penetration path of radar waves in the crown layer of vegetation, the FQ20 image enhanced the single- and double-bounce scattering differences between banana trees and other vegetation. Thus, the FQ20 image outperformed the other images in identifying banana trees.

Composite Kernel Method for PolSAR Image Classification Based on Polarimetric-Spatial Information

The composite kernel feature fusion proposed in this paper attempts to solve the problem of classifying polarimetric synthetic aperture radar (PolSAR) images. Here, PolSAR images take into account both polarimetric and spatial information. Various polarimetric signatures are collected to form the polarimetric feature space, and the morphological profile (MP) is used for capturing spatial information and constructing the spatial feature space. The main idea is that the composite kernel method encodes diverse information within a new kernel matrix and tunes the contribution of different types of features. A support vector machine (SVM) is used as the classifier for PolSAR images. The proposed approach is tested on a Flevoland PolSAR data set and a San Francisco Bay data set, which are in fine quad-pol mode. For the Flevoland PolSAR data set, the overall accuracy and kappa coefficient of the proposed method, compared with the traditional method, increased from 95.7% to 96.1% and from 0.920 to 0.942, respectively. For the San Francisco Bay data set, the overall accuracy and kappa coefficient of the proposed method increased from 92.6% to 94.4% and from 0.879 to 0.909, respectively. Experimental results verify the benefits of using both polarimetric and spatial information via composite kernel feature fusion for the classification of PolSAR images.

Underwater Topography Detection in Coastal Areas Using Fully Polarimetric SAR Data

Fully polarimetric synthetic aperture radar (SAR) can provide detailed information on scattering mechanisms that could enable the target or structure to be identified. This paper presents a method to detect underwater topography in coastal areas using high resolution fully polarimetric SAR data, while less prior information is required. The method is based on the shoaling and refraction of long surface gravity waves as they propagate shoreward. First, the surface scattering component is obtained by polarization decomposition. Then, wave fields are retrieved from the two-dimensional (2D) spectra by the Fast Fourier Transformation (FFT). Finally, shallow water depths are estimated from the dispersion relation. Applicability and effectiveness of the proposed methodology are tested by using C-band fine quad-polarization mode RADARSAT-2 SAR data over the near-shore area of the Hainan province, China. By comparing with the values from an official electronic navigational chart (ENC), the estimated water depths are in good agreement with them. The average relative error of the detected results from the scattering mechanisms based method and single polarization SAR data are 9.73% and 11.53% respectively. The validation results indicate that the scattering mechanisms based methodology is more effective than only using the single polarization SAR data for underwater topography detection, and will inspire further research on underwater topography detection with fully polarimetric SAR data.

Object-oriented fusion of RADARSAT-2 polarimetric synthetic aperture radar and HJ-1A multispectral data for land-cover classification

The contribution of the integration of optical and polarimetric synthetic aperture radar (PolSAR) data to accurate land-cover classification was investigated. For this purpose, an object-oriented classification methodology that consisted of polarimetric decomposition, hybrid feature selection, and a support vector machine (SVM) was proposed. A RADARSAT-2 Fine Quad-Pol image and an HJ-1A CCD2 multispectral image were used as data sources. First, polarimetric decomposition was implemented for the RADARSAT-2 image. Sixty-one polarimetric parameters were extracted using different polarimetric decomposition methods and then merged with the main diagonal elements ( T 11 , T 22 , T 33 ) of the coherency matrix to form a multichannel image with 64 layers. Second, the HJ-1A and the multichannel images were divided into numerous image objects by implementing multiresolution segmentation. Third, 1104 features were extracted from the HJ-1A and the multichannel images for each image object. Fourth, the hybrid feature selection method that combined the ReliefF filter approach and the genetic algorithm (GA) wrapper approach (ReliefF–GA) was used. Finally, land-cover classification was performed by an SVM classifier on the basis of the selected features. Five other classification methodologies were conducted for comparison to verify the contribution of optical and PolSAR data integration and to test the superiority of the proposed object-oriented classification methodology. Comparison results show that HJ-1A data, RADARSAT-2 data, polarimetric decomposition, ReliefF–GA, and SVM have a significant contribution by improving land-cover classification accuracy.

Analysis of different polarimetric target decomposition methods in forest density classification using C band SAR data

ABSTRACTThe ability of synthetic aperture radar (SAR) C-band microwave energy to penetrate within forest vegetation makes it possible to extract information on crown components, which in turn gives a better approximation of relative canopy density than optical data-derived canopy density. Many studies have been reported to estimate forest biomass from SAR data, but the scope of C-band SAR in characterizing forest canopy density has not been adequately understood with polarimetric techniques. Polarimetric classification is one of the most significant applications of polarimetric SAR in remote sensing. The objective of the present study was to evaluate the feasibility of different polarimetric SAR data decomposition methods in forest canopy density classification using C-band SAR data. Landsat (Land Satellite) 5 TM (Thematic Mapper) data of the same area has been used as optical data to compare the classification result. RADARSAT (Radar Satellite)-2 image with fine quad-pol obtained on 27 October 2011 over tropical dry forests of Madhav National Park, India, was used for the analysis of full polarimetric data. Six decomposition methods were selected based on incoherent decomposition for generating input images for classification, i.e. Huynen, Freeman and Durden, Yamaguchi, Cloude, Van zyl, and H/A/α. The performance of each decomposition output in relation to each land cover unit present in the study area was assessed using a support vector machine (SVM) classifier. Results show that Yamaguchi 4-component decomposition (overall accuracy 87.66% and kappa coefficient (κ) 0.86) gives better classification results, followed by Van Zyl decomposition (overall accuracy 87.20% and κ 0.85) and Freeman and Durden (overall accuracy 86.79% and κ 0.85) in forest canopy density classification. Both model-based decompositions (Freeman and Durden and Yamaguchi4) registered good classification accuracy. In eigenvector or eigenvalue decompositions, Van zyl registered the second highest accuracy among different decompositions. The experimental results obtained with polarimetric C-band SAR data over a tropical dry deciduous forest area imply that SAR data have significant potential for estimating canopy density in operational forestry. A better forest density classification result can be achieved within the forest mask (without other land cover classes). The limitations associated with optical data such as non-availability of cloud-free data and misclassification because of gregarious occurrence of bushy vegetation such as Lantana can be overcome by using C-band SAR data.

Change Detection with Compact Polarimetric SAR for Monitoring Wetlands

. Compact polarimetric synthetic aperture radar (SAR) architecture is an SAR configuration that consists of transmitting a single circular polarization (left or right) or a 45° oriented linear signal while receiving two linear polarizations, horizontal and vertical. In this study we investigate the potential of the compact polarimetric SAR mode for wetland monitoring applications. Whitewater Lake located in Manitoba, Canada, is selected as a case study where simulated compact polarimetric SAR data are obtained using RADARSAT-2 Fine Quad-POL SAR images. The ability of the compact polarimetric data to monitor wetlands using the Wishart-Chernoff distance is studied and compared to the results obtained using fully polarimetric data. Results of this study show that compact polarimetry provides monitoring capabilities for wetlands. Promising change detection mapping results based on the compact polarimetric coherency matrices are obtained using the Wishart-Chernoff distance. This could be useful for flagging change in the wetland environment for further evaluation and action if required. The compact polarimetry mode could be an attractive configuration for future SAR systems due to the combination of swath coverage, moderate resolution, and enhanced information content for monitoring changes in surface water and flooded vegetation.Résumé. L’architecture du radar à synthèse d’ouverture (SAR) en polarimétrie compacte est une configuration SAR qui transmet un seul signal en polarisation circulaire (gauche ou droite) ou un signal linéaire à 45 degrés tout en recevant deux polarisations linéaires, horizontales et verticales. Dans cette étude, nous étudions le potentiel du mode en polarimétrie compacte du SAR pour les applications de surveillance des zones humides. Le lac Whitewater, situé au Manitoba, au Canada, est sélectionné pour une étude de cas où les données SAR en polarimétrie compacte simulées sont obtenues en utilisant les images SAR en mode quad-pol fin du RADARSAT-2. La capacité des données en polarimétrie compacte pour surveiller les zones humides à l’aide de la distance Wishart-Chernoff a été étudiée, puis comparée aux résultats obtenus en utilisant les données en polarimétrie complète. Les résultats de cette étude montrent que la polarimétrie compacte offre des capacités de surveillance pour les zones humides. Des résultats prometteurs de la cartographie de détection du changement basée sur les matrices de cohérence de la polarimétrie compacte ont été obtenus en utilisant la distance Wishart-Chernoff. Ceci pourrait être utile pour signaler les changements dans les zones humides pour une évaluation supplémentaire et une action le cas échéant. Le mode en polarimétrie compacte pourrait être une configuration attrayante pour les futurs systèmes SAR en raison de la combinaison de la largeur de la couverture au sol, de la résolution moyenne et du contenu d’information effective pour la surveillance des changements dans les eaux de surface et la végétation inondée.

VALIDATION OF SPACEBORNE RADAR SURFACE WATER MAPPING WITH OPTICAL sUAS IMAGES

Abstract. The Canada Centre for Remote Sensing (CCRS) has over 40 years of experience with airborne and spaceborne sensors and is now starting to use small Unmanned Aerial Systems (sUAS) to validate products from large coverage area sensors and create new methodologies for very high resolution products. Wetlands have several functions including water storage and retention which can reduce flooding and provide continuous flow for hydroelectric generation and irrigation for agriculture. Synthetic Aperture Radar is well suited as a tool for monitoring surface water by supplying acquisitions irrespective of cloud cover or time of day. Wetlands can be subdivided into three classes: open water, flooded vegetation and upland which can vary seasonally with time and water level changes. RADARSAT‐2 data from the Wide-Ultra Fine, Spotlight and Fine Quad-Pol modes has been used to map the open water in the Peace‐Athabasca Delta, Alberta using intensity thresholding. We also use spotlight modes for higher resolution and the fully polarimetric mode (FQ) for polarimetric decomposition. Validation of these products will be done using a low altitude flying sUAS to generate optical georeferenced images. This project provides methodologies which could be used for flood mapping as well as ecological monitoring.

Ocean surface wave measurements from fully polarimetric SAR imagery

A new method for the retrieval of ocean wave parameters from SAR imagery is developed, based on the shape-from-shading (SFS) technique. Previously, the SFS technique has been used in the reconstruction of 3D landform information from SAR images, in order to generate elevation maps of topography for land surfaces. Here, in order to retrieve ocean wave characteristics, we apply the SFS methodology, together with a method to orient the angular measurements of the azimuth slope and range slope, in the measurement of ocean surface waves. This method is applied to high resolution fine-quad polarization mode (HH, VV, VH and HV) C-band RADARSAT-2 SAR imagery, in order to retrieve ocean wave spectra and extract wave parameters. Collocated in situ buoy measurements are used to validate the reliability of this method. Results show that the method can reliably estimate wave height, dominant wave period, dominant wave length and dominant wave direction from C-band SAR images. The advantage of this method is that it does not depend on modulation transfer functions (MTFs), in order to measure ocean surface waves. This method can be used in monitoring ocean surface wave propagation through open water areas into ice-covered areas, especially the marginal ice zone (MIZ) in polar oceans.

Synergistic use of RADARSAT-2 Ultra Fine and Fine Quad-Pol data to map oilsands infrastructure land: Object-based approach

The landscape of Alberta’s oilsands regions is undergoing extensive change due to the creation of infrastructure associated with the exploration for and extraction of this resource. Since most oil sands mining activities take place in remote forests or wetlands, one of the challenges is to collect up-to date and reliable information about the current state of land. Compared to optical sensors, SAR sensors have the advantage of being able to routinely collect imagery for timely monitoring by regulatory agencies. This paper explores the capability of high resolution RADARSAT-2 Ultra Fine and Fine Quad-Pol imagery for mapping oilsands infrastructure land using an object-based classification approach. Texture measurements extracted from Ultra Fine data are used to support an Ultra Fine based classification. Moreover, a radar vegetation index (RVI) calculated from PolSAR data is introduced for improved classification performance. The RVI is helpful in reducing confusion between infrastructure land and low vegetation covered surfaces. When Ultra Fine and PolSAR data are used in combination, the kappa value of well pads and processing facilities detection reached 0.87. In this study, we also found that core hole sites can be identified from early spring Ultra Fine data. With single-date image, kappa value of core hole sites ranged from 0.61 to 0.69.

Polarimetric Classification of C-Band SAR Data for forest Density Characterization

Polarimetric classification is one of the most significant applications of synthetic aperture radar (SAR) remote sensing. Sensitivity of C-band SAR in discerning the variation in canopy roughness and limited penetration capability through forest canopy have been well studied at a given frequency, polarization and incidence angle. However, the scope of C-band SAR in characterizing and monitoring forest density has not been adequately understood with polarimetric techniques. The objectives of the present study were to understand the scattering behaviour of different landcover classes and evaluate the feasibility of polarimetric SAR data classification methods in forest canopy density slicing using C-band SAR data. The RADARSAT- 2 image with fine quad-pol obtained on 27 October 2011 over Madhav National Park, Madhya Pradesh, India and its surroundings was used for the analysis. Forest patches exhibit α-angle around 45°, which means the dominant scattering mechanism is volume; entropy of one or a value close to it denotes distributed targets and low anisotropy values than all other land units, which shows a dominant first scattering mechanism. This study comparatively analysed Wishart supervized classifier and Support Vector Machine (SVM) classifier for classification of the forest canopy density along with other associated land-cover classes for a better understanding of the class separability. All forest density classes showed comparatively good separability in Wishart supervized classification (73.8-84.7%) and in SVM classifier (82.3-84.8%). The results demonstrate the effectiveness of SVM classifier (88.7%) over Wishart supervized classifier (87.8%) with kappa coefficient of 0.86 and 0.85 respectively. The experimental results obtained with polarimetric C-band SAR data over dry deciduous forest area imply that SAR data have a significant potential for estimating stand density in operational forestry.

Characterization of Marine Surface Slicks by Radarsat-2 Multipolarization Features

In this paper, we study surface slick characterization in polarimetric C-band synthetic aperture radar (SAR) data. The objective is to identify the most powerful multipolarization SAR descriptors for mineral oil spill versus biogenic slick discrimination. A systematic comparison of eight well-known multipolarization features is provided. The analysis is performed on data that we collected during a large-scale oil spill exercise at the Frigg field situated northwest of Stavanger, in June 2011. Controlled oil spills and simulated look-alikes were simultaneously captured within fine quad-polarization Radarsat-2 acquisitions during this experiment. Multipolarization features derived from only the copolarized complex scattering coefficients are explored. We find that the two most powerful multipolarization features extracted from this data set are the geometric intensity, measuring the combined intensity based on the determinant of the coherency matrix, and the real part of the copolarization cross product, which is related to the scattering behavior of the target. We show that these two features can distinguish between the simulated biogenic slicks and mineral oil types such as Balder and Oseberg blend, and that the discriminative power seems to be persistent with time.

Characterizing Scattering Behaviour and Assessing Potential for Classification of Arctic Shore and Near-Shore Land Covers with Fine Quad-Pol RADARSAT-2 Data

Freeman–Durden and Cloude–Pottier decompositions were applied to polarimetric RADARSAT-2 data to characterize the scattering behaviour of shore and near-shore land cover types for two study areas in the Beaufort Sea, Canada: Richards Island and Tuktoyaktuk Harbour. The impact of incidence angle was evaluated through comparison of Single Look Complex Fine Quad-Pol images acquired at three angles: shallow (45.3°–49.5°), medium (39.3°–41.6°), and steep (20.9°–24.2°), and the potential for accurate classification was assessed using three unsupervised polarimetric SAR classifiers: Wishart-entropy/alpha, Wishart-entropy/anisotropy/alpha, and Freeman-Wishart. Results indicate that overall, medium, and shallow incidence angle images provide more useful information than steep angle images and that these classifiers are capable of distinguishing some general land cover types, including substrates, wetlands, vegetated tundra, and water. Results from this analysis will inform future research related to classification and interpretation of RADARSAT-2 data for shoreline mapping.Résumé. Des décompositions Freeman–Durden et Cloude–Pottier ont été appliquées à des données polarimétriques RADARSAT-2 pour caractériser le comportement de diffusion des différents types de couverture terrestre des milieux littoraux et côtiers pour deux zones d'étude dans la mer de Beaufort, au Canada: l'île Richards et le port de Tuktoyaktuk. L'impact de l'angle d'incidence a été évalué par comparaison des images singulières complexes <<Single Look Complex (SLC)>> quad-pol fines acquises à trois angles; plus rasants (45.3°–49.5°), moyens (39.3°–41.6°) et abrupts (20.9°–24.2°), tandis que le potentiel pour une classification précise a été évalué à l'aide de trois classificateurs RSO polarimétriques non dirigés: Wishart-entropie/alpha, Wishart-entropie/anisotropie/alpha et Freeman–Wishart. Les résultats indiquent que, dans l'ensemble, les images avec des angles d'incidence moyens à rasants fournissent des informations plus utiles que les images aux angles abrupts, et que ces classificateurs sont capables de distinguer certains types généraux de couverture terrestre, y compris: les substrats, les zones humides, la toundra végétalisée et l'eau. Les résultats de cette analyse seront utiles dans les recherches futures liées à la classification et à l'interprétation des données RADARSAT-2 pour la cartographie des rivages.

Leaf area index estimation of lowland rice using semi-empirical backscattering model

Rice crop monitoring using synthetic aperture radar (SAR) polarimetry is one of the thrust areas of research in radar remote sensing due to unavailability of optical data in critical growth stages of crop because of dense cloud cover during the growing season (monsoon) in India and Southeast Asia. The prime objective of this study was to assess the potential of polari- metric C-band SAR data to estimate the leaf area index (LAI) of two varieties (non-basmati and basmati) of paddy rice. Seven fine Quad-pol single look complex (SLC) RADARSAT-2 data were acquired over part of the Indo-Gangetic plain, India, in 2011 and 2012, and ground data were collected during the same period of the satellite pass. The backscatter of rice in differ- ent polarizations over different growth periods was analyzed and LAI estimated using a semi- empirical model. The performance of the model was evaluated by statistical parameters, namely. root mean square error and coefficient of determination (R 2 ). The model performed well for the LAI estimation of non-basmati rice using HV backscatter while achieving acceptable accuracy for others. Results of this study provided the promising approach for LAI prediction using SAR data in India. © 2013 Society of Photo-Optical Instrumentation Engineers (SPIE) (DOI: 10.1117/1.JRS.7

Ocean Vector Winds Retrieval From C-Band Fully Polarimetric SAR Measurements

We present an efficient algorithm for retrieving the ocean-surface wind vector from C-band Radar Satellite RADARSAT-2 fully polarimetric synthetic aperture radar (SAR) measurements based upon the copolarized geophysical model function, i.e., CMOD5.N, and the cross-polarized ocean backscatter model, i.e., C-2PO. The analysis of fine quad-polarization mode single-look complex SAR data and collocated in situ moored buoy observations reveals that the polarimetric correlation coefficient between co- and cross-polarization channels has odd symmetry with respect to the wind direction. This characteristic is different from the feature that normalized radar cross sections for quad-polarization have even symmetry regarding the wind direction. We first use the C-2PO model to directly retrieve wind speeds without any external wind-direction and radar-incidence-angle inputs. Subsequently, the retrieved wind speeds, along with incidence angles and CMOD5.N, are employed to invert the wind direction, still with ambiguities. The odd-symmetry property is then applied to remove the wind direction ambiguities. Thus, it is shown that fully polarimetric SAR measurements provide complementary directional information for the ocean-surface wind fields. This method has the potential to improve wind vector retrievals from space.

INVENTORY OF IRRIGATED RICE ECOSYSTEM USING POLARIMETRIC SAR DATA

Abstract. An attempt has been made in the current study to assess the potential of polarimetric SAR data for inventory of kharif rice and the major competing crop like cotton. In the process, physical process of the scattering mechanisms occurring in rice and cotton crops at different phonological stages was studied through the use of temporal Radarsat 2 Fine quadpol SAR data. The temporal dynamics of the volume, double and odd bounce, entropy, anisotropy, alpha parameters and polarimertic signatures, classification through isodata clustering and Wishart techniques were assessed. The Wishart (H-a) classification showed higher overall as well as rice and cotton crop accuracies compared to the isodata clustering from Freeman 3-component decomposition. The classification of temporal SAR data sets independently showed that the rice crop forecasting can be advanced with the use of appropriate single date polarimetric SAR data rather than using temporal SAR amplitude data sets with the single polarization in irrigated rice ecosystems

Geomorphological mapping in the Antarctic Peninsula region applying single and multipolarization RADARSAT-2 data

RADARSAT-2 data in the ultra-fine and fine quad polarization mode was evaluated to identify morpho-structural, periglacial, and other geomorphological features within the Antarctic Peninsula region. The study was carried out within Byers Peninsula, the largest ice-free area on the South Shetland Islands. A methodology was developed to integrate and compare data from different sources to optimize the selection of criteria and techniques to obtain information from RADARSAT-2 relevant to the geomorphology of the study area. Ultra-fine data were focused on the extraction of linear features and fine structures; whereas, fine quad polarized data were implemented to identify scattering mechanisms for different surface covers. It was possible to extract a greater number of lineaments and structures with the ultra-fine data. A supervised classification was carried out using the fine quad polarized data, where a careful selection of training and validation sites was needed. The overall classification accuracy was 75% with a Kappa coefficient of 0.71. Surface cover features with low entropy scattering mechanisms were well identified with a maximum of 86% accuracy. The accuracy for higher entropy volume and multiple scattering mechanisms were only slightly lower and are considered especially important for the type of geomorphological features studied on Byers Peninsula.

An assessment of the use of RADARSAT-2 for detailed topographic mapping in a tropical semiarid terrain of Brazil

In this paper, the feasibility of using planialtimetric information derived from RADARSAT-2 (RST-2) ultra-fine (UF) stereo pairs and fine quad-pol (FQP) images for detailed topographic mapping was investigated for a semiarid terrain in the Curaçá Valley, northeast of Brazil. Precise topographic field information acquired from a global positioning system was used for ground control points for the modeling of the stereoscopic digital surface models (DSMs), ortho-images, and as independent check points for the calculation of planialtimetric accuracies. The analysis was performed with the following two approaches: (i) the use of root mean square error for the overall classification of the DSMs and ortho-images considering the Brazilian Map Accuracy Standard limits, and (ii) calculations of systematic errors (bias) and accuracy based on a methodology that takes into account computed discrepancies and standard deviations. Thematic information was extracted from FQP data through the use of an unsupervised terrain and land-use classification scheme based on the Freeman–Durden decomposition and the Wishart classifier. The investigation showed that the planialtimetric accuracies of UF DSMs and ortho-images and the thematic information of the FQP data fulfilled the requirements compatible to detailed topographic mapping (1:50000). Thus, the use of RST-2 data can be considered a real alternative as a primary source for detailed topographic mapping programs in similar environments of Brazil, where terrain information is limited or of a poor quality.

Goodness-of-Fit Tests for Multilook Polarimetric Radar Data Based on the Mellin Transform

The advent of polarimetric synthetic aperture radar has spurred a growing interest in statistical models for complex-valued covariance matrices, which is the common representation of multilook polarimetric radar images. In this paper, we respond to an emergent need by proposing statistical tests for the simple and composite goodness-of-fit (GoF) problem for a class of compound matrix distributions. The tests are based on Mellin-kind matrix cumulants. These are derived from a novel characteristic function for positive definite Hermitian random matrices, defined in terms of a matrix-variate Mellin transform instead of the conventional Fouriér transform, and belong to a new framework for statistical analysis of multilook polarimetric radar data recently introduced by the authors. The cumulant-based tests are easy to compute, and the asymptotic sampling distribution of the test statistic is chi-square distributed in the simple hypothesis case. Under the composite hypothesis, the sampling distribution is obtained by Monte Carlo simulations. We evaluate the power of the proposed GoF tests with simulated data. We also use them to assess the fit of several matrix distributions to real data acquired by Radarsat-2 in fine-quad polarization mode.

Generalized-K (GK)-Based Observation of Metallic Objects at Sea in Full-Resolution Synthetic Aperture Radar (SAR) Data: A Multipolarization Study

An electromagnetic model, based on the generalized-K (GK) speckle distribution, has been developed to read the scattering features associated with metallic objects at sea in full resolution single-look complex (SLC) synthetic aperture radar (SAR) data. The sensitivity of the GK parameters is investigated in both copolarized and cross-polarized SAR data. It is shown that a proper combination of GK parameters exhibits a completely different behavior with respect to sea surface with and without metallic objects, when measured over cross-polarized SAR data. As a matter of fact, a simple and very effective approach to observe metallic objects in full resolution SLC cross-polarized SAR data, is proposed. Experiments undertaken over a large data set consisting of RADARSAT-2 SLC fine quad polarization SAR data, confirm model predictions, and show that the proposed approach is both physically-based and operationally effective, since it is able to provide a logical true and false output.