Touzi Decomposition Research Articles

Investigation of the sensitivity response of Touzi target scattering decomposition to modeled early ice growth

ABSTRACT Touzi’s scattering vector model allows for a unified decomposition of both coherent and incoherent target scattering. Based on this model, a unique and roll-invariant model can be developed for target decomposition. Our study aims at the investigation of the Touzi decomposition for lake and sea ice monitoring. Thus, we investigate the sensitivity of the target parameters obtained from the Touzi incoherent decomposition to modeled ice growth. Our study focuses on thermodynamically-grown fast lake and sea ice during the early ice growth. A time-series quad pol synthetic aperture radar (SAR) imagery was acquired over a study site around the Resolute Bay area. Results indicate that for lake ice, the scattering type magnitude () decreases within a thickness up to 20 cm. This means switching from volume scattering to dominant surface scattering. The same sensitivity is observed for young sea ice with thickness up to 30 cm, which corresponds to modeled ice bulk salinity of 8.5‰. Remarkably, we found a trend of increasing target orientation angle () for thin sea ice up to 20 cm, which corresponds to a bulk salinity of 10.3‰. This trend is unique to the case of sea ice.

Estimating Forest Above-Ground Biomass in Central Amazonia Using Polarimetric Attributes of ALOS/PALSAR Images

Polarimetric synthetic aperture radar (SAR) images are essential to understand forest structure and plan forest inventories with the purpose of natural resource management and environmental conservation efforts. We developed a method for estimating above-ground biomass (AGB) from power and phase-radar attributes in L-band images. The model was based on the variables “Pv” (from Freeman–Durden decomposition) and “σ°HH”, complemented by the attributes of Touzi decomposition “αS2”, “τm”, “ ΦS3”, and “ ΦS2”. The analyses demonstrated the contribution of volumetric, multiple, and direct scattering resulting from the interaction between the signal and the random structure of canopies and their forest biomass. The proposed model had good predictive capacity and a positive correlation (R2 = 0.67 and = 0.81, respectively), with Syx = 56.9 Mg ha−1 and a low average estimation error of 7.5% at R2 = 0.81 in the validation. An additional exploratory analysis of the parallel polarimetric responses did not reveal a defined pattern for the different phytophysiognomies—although all indicated a predominance of multiple and/or volumetric scattering. This fact can be related to the floristic and structural variation in the primary forest units, the degree of human intervention in legal logging, and the differences among succession stages.

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.

Parameter selection of Touzi decomposition and a distribution improved autoencoder for PolSAR image classification

Touzi decomposition provides more scattering information corresponding to parameters describing different targets. It is possible to explore the selection method of parameters, which plays an important role in polarimetric synthetic aperture radar (PolSAR) image classification. Therefore, this paper presents an innovative parameter ordering scheme based on the histogram of parameters obtained by Touzi decomposition. Then, a parameter selection scheme is proposed by analyzing the influence of the increase of parameters on the overall accuracy obtained by the Softmax classifier. Based on the Gamma distribution, the parameter selection method of eigenvalue decomposition is also given. As the data distribution of the selected parameters does not always satisfy the Gaussian distribution, it may not be sufficient to directly apply the autoencoder network. Thus, the loss function of the autoencoder network is modified by the construction of different data error terms according to a different data distribution form of the selected parameters, and then an improved autoencoder network is proposed. The process of feature extraction and classification can be regarded as a whole network to better accomplish the task of classification. An extensive set of experiments are done on four real PolSAR images. Compared with the classification results of all parameters, the gap in the overall accuracy of the parameters obtained by the proposed selection scheme is only about 1%. In terms of the classification overall accuracy, considering the distribution of parameters in the autoencoder network and the construction of the whole classification network can improve it by about 6–7% for four data sets. Code is available at https://github.com/Justin20220123/ISPRS2022.

On the Role of Polarimetric Decomposition and Speckle Filtering Methods for C-Band SAR Wetland Classification Purposes

Previous wetlands studies have thoroughly verified the usefulness of data from synthetic aperture radar (SAR) sensors in various acquisition modes. However, the effect of the processing parameters in wetland classification remains poorly explored. In this study, we investigated the influence of speckle filters and decomposition methods with different combinations of filter and decomposition windows sizes on classification accuracy. We used a C-band Radarsat 2 image acquired over a wetland located in northeast Poland. We processed the SAR data using various speckle filters: boxcar, intensity-driven adaptive-neighborhood (IDAN), improved Lee sigma, refined Lee (in 5×5 to 11×11 pixel window sizes), and a nonlocal NL-SAR. Next, we processed the nonfiltered and filtered data using nine polarimetric decompositions, also in 5×5 to 11×11 pixel window sizes. The extracted polarimetric features were applied as an input dataset in the random forest classification model in single- and multidecomposition scenarios. In the single-decomposition scenario, the Cloude–Pottier decomposition produced the highest (72%) and the Touzi decomposition achieved the lowest (38%) accuracy. The IDAN filter with an 11×11 filter window and a 9×9 decomposition window had the highest, and the nonfiltered data with a 5×5 decomposition window had the lowest accuracy in the multidecomposition scenario. The most important features were the alpha parameter from the Cloude–Pottier decomposition, the polarimetric contribution of the Shannon entropy, and the volume backscattering components. The results stress the importance of appropriate processing parameters in the SAR data classification workflow, and the study guides in selecting the most suitable combination of radar image processing parameters for wetland classification.

Investigating the Potential Use of RADARSAT-2 and UAS imagery for Monitoring the Restoration of Peatlands

The restoration of peatlands is critical to help reduce the effects of climate change and further prevent the loss of habitat for many species of flora and fauna. The objective of this research was to evaluate RADARSAT-2 satellite imagery and high-resolution Unmanned Aerial Systems (UASs) to determine if they could be used as surrogates for monitoring the success of peatland restoration. Areas of peatland that were being actively harvested, had been restored from past years (1994–2003), and natural shrub bog in Lac St. Jean, Quebec were used as a test case. We compared the Freeman–Durden and Touzi decompositions by applying the Bhattacharyya Distance (BD) statistic to see if the spectral signatures of restored peatland could be separated from harvested peat and natural shrub bog. We flew Unmanned Aerial Surveys (UASs) over the study site to identify Sphagnum and Polytrichum strictum, two indicator species of early peatland restoration success. Results showed that the Touzi decomposition was better able to separate the spectral signatures of harvested, restored, and natural shrub bog (BD values closer to 9). Symmetric scattering type αs1, Helicity |τ1,2,3|, a steep incidence angle, and peak growing season appear to be important for separating the spectral signatures. We had moderate success in detecting Sphagnum and Polytrichum strictum visually by using texture and pattern but were unable to use colour due to differences in sun angle and clouds during the UAS flights. Results suggest that RADARSAT-2 data using the Touzi decomposition and UAS imagery show potential for monitoring peatland restoration success over time.

A Novel Unsupervised Approach for Land Classification Based on Touzi Scattering Vector Model in the Context of Very High Resolution PolSAR Imagery

With the popularization of very high resolution polarimetric ll-invariant decomposition solution, is employed to extract the scattering properties of different land covers. The parameters of Touzi decomposition act as input dataset for initial classification. A novel classifying algorithm is put forward by means of integrating the Touzi decomposition with conventional Wishart statistical models. Quantitative experiments are then conducted using uninhabited aerial vehicle synthetic aperture radar sample data for evaluating the performance of this new proposed approach. It can be concluded from the experimental results that the new proposed method is superior to the classical method in terms of producer accuracy, user accuracy, and overall accuracy.

Building damage mapping based on Touzi decomposition using quad-polarimetric ALOS PALSAR data

Building damage assessment is of great significance to disaster monitoring. Polarimetric Synthetic Aperture Radar (SAR) can record the polarization scattering measurement matrix of ground objects and obtain more abundant ground object information, meaning that they can be used for assessing damage to buildings. In this paper, a new approach is proposed to assess building damage using the Touzi incoherent decomposition and SAR-based characteristics of buildings before and after damage. The March 11th, 2011 earthquake that struck the coast of northeast Japan serves as the demonstration of the technique using quad-polarimetric ALOS PALSAR data acquired before and after the disaster. The analysis shows that after the buildings are damaged, there is a clear decrease in the αs1 (the dominant scattering-type magnitude) components and the degree of this reduction corresponds to the degree of building damage. This means that the αs1 components obtained by Touzi decomposition can effectively reflect the degree of building damage. On this basis, a model based on Touzi decomposition was established to evaluate the degree of damage to buildings, and the accuracy of the model was validated using high-resolution optical data acquired before and after the earthquake. The experimental results show that Touzi decomposition can be effectively used for damage assessment mapping in built-up areas.

Scattered and Received Wave Polarization Optimization for Enhanced Peatland Classification and Fire Damage Assessment Using Polarimetric PALSAR

The complementarity of the “scattered” and “received” wave polarization signatures is demonstrated for enhanced characterization of peatlands and surrounding forests. Polarimetric L-band ALOS-PALSAR data collected for the Athabasca oil sand exploration region, with peatlands and forests partially affected by multiple wildfires, are used. It is shown that the scattered wave polarization signature, which represents the explicit variations of the degree of polarization (DoP) and the total scattered intensity $R_{0}$ with transmitted polarization, permits enhanced discrimination of treed bogs from upland forests and improved identification of wildfire damage in peatlands and surrounding forests. Scattered wave optimization is used as a convenient method for efficient exploitation of the scattered wave polarization signature. The Touzi decomposition is adopted for the optimization of the received wave polarization signature. The unique potential of the scattering-type phase generated with the Touzi decomposition is confirmed for enhanced discrimination of poor fens from bogs. These two important peatland classes cannot be separated with the scattered wave optimization, the conventional multipolarization (HH–HV–VV) channels, and the Freeman model based decomposition. Finally, the Touzi decomposition is combined with the extrema of the scattered wave main parameters (DoP and $R_{0}$ ) for optimum extraction of polarimetric PALSAR information. This information is, then, fused with Landsat-5-TM for enhanced peatland classification. The comparison with optical Landsat5-TM-based classification confirms the valuable added information that a long penetrating polarimetric L-band PALSAR can provide for enhanced peatland classification and efficient assessment of peat health in burnt peatlands.

Multifrequency and Full-Polarimetric SAR Assessment for Estimating Above Ground Biomass and Leaf Area Index in the Amazon Várzea Wetlands

The aim of this study is to evaluate the potential of multifrequency and Full-polarimetric Synthetic Aperture Radar (SAR) data for retrieving both Above Ground Biomass (AGB) and Leaf Area Index (LAI) in the Amazon floodplain forest environment. Two specific questions were proposed: (a) Does multifrequency SAR data perform more efficiently than single-frequency data in estimating LAI and AGB of várzea forests?; and (b) Are quad-pol SAR data more efficient than single- and dual-pol SAR data in estimating LAI and AGB of várzea forest? To answer these questions, data from different sources (TerraSAR-X Multi Look Ground Range Detected (MGD), Radarsat-2 Standard Qual-Pol, advanced land observing satellite (ALOS)/ phased-arrayed L-band SAR (PALSAR-1). Fine-beam dual (FDB) and quad Polarimetric mode) were combined in 10 different scenarios to model both LAI and AGB. A R-platform routine was implemented to automatize the selection of the best regression models. Results indicated that ALOS/PALSAR variables provided the best estimates for both LAI and AGB. Single-frequency L-band data was more efficient than multifrequency SAR. PALSAR-FDB HV-dB provided the best LAI estimates during low-water season. The best AGB estimates at high-water season were obtained by PALSAR-1 quad-polarimetric data. The top three features for estimating AGB were proportion of volumetric scattering and both the first and second dominant phase difference between trihedral and dihedral scattering, extracted from Van Zyl and Touzi decomposition, respectively. The models selected for both AGB and LAI were parsimonious. The Root Mean Squared Error (RMSEcv), relative overall RMSEcv (%) and R2 value for LAI were 0.61%, 0.55% and 13%, respectively, and for AGB, they were 74.6 t·ha−1, 0.88% and 46%, respectively. These results indicate that L-band (ALOS/PALSAR-1) has a high potential to provide quantitative and spatial information about structural forest attributes in floodplain forest environments. This potential may be extended not only with PALSAR-2 data but also to forthcoming missions (e.g., NISAR, Global Ecosystems Dynamics Investigation Lidar (GEDI), BIOMASS, Tandem-L) for promoting wall-to-wall AGB mapping with a high level of accuracy in dense tropical forest regions worldwide.

SAR polarimetric decomposition with ALOS PALSAR-1 for agricultural land and other land use/cover classification: case study in Rajasthan, India

Synthetic aperture radar (SAR) has operational applications in crop mapping and monitoring in many countries due to the distinct backscatter signature at various stages of crop growth. Polarimetric analysis of SAR data from different satellites was used for information extraction from different types of scatters in imaged terrain. The scattering processes were analyzed through the received scatter matrix derived from the target decomposition of SAR data. Three decomposition techniques, namely Freeman–Durden, Cloude–Pottier and Touzi decomposition of the ALOS PALSAR-1 data, were used in this study to extract land use/cover information with a specific emphasis on agriculture. The decomposed output parameters from these techniques were classified with supervised classifier of support vector machine (SVM) using region of interest (ROI) selected land use/cover classes. An accuracy assessment for the classified output was carried out using the ROI. The Ramgarh village in Jaisalmer district of Rajasthan with the predominance of agricultural land, sand dunes and settlements was chosen as the study area. Freeman–Durden decomposition resulted in the highest overall accuracy of about 85% in the land use/cover classification among the three decomposition techniques adopted in the study. It was also observed that the accuracy of land use/cover mapping derived from Cloude–Pottier and Touzi decompositions improved with the use of eigenvalues in the SVM classification. Higher accuracies in the classification of agriculture land were noted with all the three decomposition techniques. The four parameters of Cloude–Pottier (H, A, α, β) and Touzi (α s, Φ s, ψ, τ) decompositions improved the classification accuracy for all the classes due to eigenvalues. The overall classification accuracy was above 88% for both the decomposition techniques with four parameters. The soil moisture values for agriculture land and sand dunes were validated through soil moisture maps generated using Oh 1992 and 2004 models.

A Pol-SAR Analysis for Alpine Glacier Classification and Snowline Altitude Retrieval

In this study, we investigated the use of synthetic aperture radar (SAR) polarimetry (Pol-SAR) and a supervised classification technique, support vector machine (SVM), for the classification of bare soil, ice, and snow, over the Ortles–Cevedale massif, (Eastern Italian Alps). We analyzed the importance of topographic correction on the backscattering and polarimetric SAR signature and the advantage of quad-pol with respect to dual-pol data. When backscattering values only are employed, the incidence angle used as input feature of the SVM classifier assures the best classification accuracy, 9.9% higher than the accuracy obtained with cosine corrected ${\gamma ^0}$ backscattering. The introduction of polarimetric features and decomposition parameters (such as Cloude–Pottier or Touzi decomposition parameters) increases the classification accuracy by 5.2% with respect to the backscattering case. The simulation of RADARSAT-2 data as Sentinel-1 like for dual-pol data shows a decrease of accuracy equal to 7.8% with respect to the fully polarimetric case (93.5%). The first Sentinel-1 image acquired on our test area was also employed for classification. We then tested the capability of C-band SAR to detect accumulation and ablation zones of the glaciers under the winter dry snow by setting up a multi-incidence angle and fully polarimetric SVM classifier, exploiting ascending and descending RADARSAT-2 data. In this case, the accuracy increased by 14.7% combining different geometric acquisitions (88.9%) with respect to the single geometry case. Finally, from the resulting classification maps, we extracted the snowline altitude for a sample of three glaciers, using both optical and SAR data, comparing the different products.

A Generic Land-Cover Classification Framework for Polarimetric SAR Images Using the Optimum Touzi Decomposition Parameter Subset—An Insight on Mutual Information-Based Feature Selection Techniques

This correspondence proposes a generic framework for land-cover classification using support vector machine (SVM) classifier for polarimetric synthetic aperture radar (SAR) images considering the optimum Touzi decomposition parameters. Some new concerns have been raised recently with the Cloude-Pottier decomposition. Cloude's α scattering type ambiguities may take place for certain scatterers, and some of the Cloude-Pottier's parameters may not be roll-invariant for asymmetric targets. The Touzi decomposition is a relatively new roll-invariant target scattering decomposition, and it uses the target helicity, symmetric scattering type magnitude and phase. The parameters generated by the Touzi decomposition are of different physical significances, i.e., some of them are angular in nature where others are from R. Thus, classification using the Touzi parameters requires them to be normalized within the similar dynamic range preserving their physical properties. Here, a linear normalization technique has been introduced, which maps the angular parameters to R without loss of generalization. The power of mutual information (MI) has been explored hence after for selecting the optimum set of classification parameters. A third-order class-dependent MI-based method and another method based on the Eigen-space decomposition of the class conditional MI matrix have been introduced for this purpose. For SVM-based final classification, a normalized histogram intersection kernel (NIKSVM) has been proposed that boosts the generalization accuracy to a considerable extent as compared to normal histogram intersection kernel. An ALOS L-band SAR image of Mumbai area, India has been considered here to exhibit the performance of the proposed cost-effective classification framework.

Digital Mapping of Soil Texture Using RADARSAT-2 Polarimetric Synthetic Aperture Radar Data

This study aimed to assess the contribution of RADARSAT-2 polarimetric synthetic aperture radar (SAR) data to digital mapping of soil surface texture of Rouville County, near Montreal, Canada. First, compositional data transformation using isometric log ratio (ilr) was applied to soil texture data to transpose the simplex into multidimensional real space, which is better suited to multivariate analysis. Thereafter, two assumptions on the relationships between RADARSAT-2 data and the ilr components were evaluated: (i) as linearly dependent by applying cokriging (CK) and regression kriging (RK); and (ii) as nonlinearly dependent by applying the e-insensitive and nonlinear support vector regression (SVR). The results were compared with ordinary kriging (OK). The environmental variables used to define the covariates were monopolarization SAR channels, the parameters extracted from entropy/anisotropy/ mean α polarimetric, Freeman and Durden, and Touzi decompositions. Using 283 soil samples for training and 89 for validation, the results showed that the root mean square error (RMSE) of prediction obtained by OK was 8% for silt, 13% for clay, and 13% for sand. The SVR produced the best prediction accuracy compared with the geostatistical interpolation techniques. Compared with OK, the improvement of the digital mapping accuracies (in terms of RMSE reduction) with SVR was significant: RMSE was reduced by 18% for sand, 17% for silt, and 35% for clay. It was followed by RK, with RMSE reduction ranging from 6 to 13%, and then CK (3–5%). Using SAR polarimetric data extracted from a RADARSAT-2 image as covariates was found to be very useful for digital mapping of soil surface texture.

Polarimetric Radarsat-2 wetland classification using the Touzi decomposition: case of the Lac Saint-Pierre Ramsar wetland

Wetlands play a key role in regional and global environments and are linked to climate change, water quality, and hydrological and carbon cycles. They also contribute to wildlife habitat and biodiversity and can act as indicators of overall environmental health. Unfortunately, wetlands continue to be under threat. There is an immediate need for improved mapping and monitoring of wetlands to better manage and protect these sensitive areas. Recently, the Touzi decomposition was introduced and proved very promising for wetland characterization using polarimetric airborne (Convair-580) SAR data. The purpose of this study is to assess the Touzi incoherent target-scattering decomposition (ICTD) for wetland classification using polarimetric Radarsat-2 (RS2) data collected over the RAMSAR wetland site in Lac Saint-Pierre, Canada. In particular, the sensitivity of the ICTD parameters to seasonal evolution of marsh and swamp scattering is discussed and demonstrated. The intent is to show that the dominant scattering type magnitude (αs1) and phase (Φs1), and the dominant (η1) and lowest scattering eigenvalues (η3), lead to an effective characterization of the various backscattering mechanisms of the wetland plant species. The ICTD parameters form the basis of a new hierarchical classification that is efficient for wetland classification. The use of multitemporal information obtained from multidate RS2 acquisitions between April and September allows an accurate wetland classification. The RS2 polarimetric classification is then compared with a supervised maximum-likelihood classification using a pair of Landsat-5 images.

Point Target Classification via Fast Lossless and Sufficient $\Omega$–$\Psi$–$\Phi$ Invariant Decomposition of High-Resolution and Fully Polarimetric SAR/ISAR Data

The classification of high-resolution and fully polarimetric SAR/ISAR data has gained a lot of attention in remote sensing and surveillance problems and is addressed by decomposing the radar target Sinclair matrix. In this paper, the Sinclair matrix has been projected onto the circular polarization basis and is decomposed into five parameters that are invariant to the relative phase Φ, the Faraday rotation Ω, and the target orientation Ψ without any information loss. The physical interpretation of these parameters, useful for target classification studies, is found in the wave-particle nature of radar scattering phenomenon given the circular polarization of elemental packets of energy. The proposed deterministic target decomposition is based on the left-orthogonal special unitary SU(2) basis, decomposing the signal backscattered by point targets, represented by the target vector, via six special unitary SU(4) rotation matrices, and by providing full resolution and lossless analysis. Comparisons between the proposed deterministic target decomposition and the Cameron, Kennaugh, Krogager, and Touzi decompositions are also pointed out. Generally, the proposed decomposition provides simpler interpretation, faster parameter extraction, and better generalization properties for the analysis of nonreciprocal or random targets. Several polarimetric SAR/ISAR data sets of UWB data, airborne fully polarimetric EMISAR data, and spaceborne RADARSAT2 are used for illustrating the effectiveness and the usefulness of this decomposition for the classification of point targets. Results are very promising for application use in the next generation of high-resolution spaceborne and airborne Pol-SAR and Pol-ISAR systems.

Evaluation of C-band SAR polarimetric parameters for discrimination of first-year sea ice types

In this study, the classification potential of polarimetric parameters derived after Cloude–Pottier decomposition, Touzi decomposition, Freeman–Durden decomposition, normalized radar cross section measurements, phase differences, and statistical synthetic aperture radar correlation measures is evaluated by relating them to three pre-identified sea ice types and wind-roughened open water. A combined approach that constitutes a visual inspection of estimated probability densities of the polarimetric parameters and quantitative analysis using supervised classifications (k means and maximum likelihood) is adopted. Polarimetric parameters are iteratively combined in pairs and triplets to test for their ice type discrimination potential. Sensitivity of polarimetric parameters to radar incidence angle is also examined. Our results demonstrated strong but variable sensitivity of polarimetric parameters to different ice types, which was dependent on radar incidence angle. Results of parameter evaluation demonstrated that no single parameter discriminates significantly (>60%) between all the ice types considered in the study. Combining two low correlated parameters increased the classification accuracy by 10%–22%. Combining the third polarimetric parameter did not necessarily improve the classification results. However, the best classification results were achieved using a combination of three parameters.

Polarimetric decomposition with RADARSAT-2 for rice mapping and monitoring

Synthetic aperture radar for paddy rice crop mapping is operational in many countries in southeast Asia due to the unique temporal backscatter signature when the rice grows above the water surface. Although the rapid increase of up to 10 dB or more allows for the identification and subsequent mapping of the rice crop extent the saturation of the backscatter signal during the later growth stages makes yield determination difficult. The development of polarimetric decomposition techniques may improve the capability to estimate rice production as the phase centre of the backscatter is expected to change as the crop matures and the water distribution in the plant parts changes. These changes can potentially be related to crop yield using growth models. This capability for monitoring growth stage and enhancing yield estimation is explored with a multitemporal RADARSAT-2 polarimetric data set of a rice growing region in China. The Freeman–Durden, the Cloude–Pottier, and the Touzi decompositions were used for classification and then evaluated as a function of the growth stage, which was monitored in the rice paddies during the image acquisition period. The Touzi decomposition produced the best classification accuracy when six or more parameters were used in the classification, but all polarimetric decompositions produced suitable accuracies for rice mapping. This indicated only one date of imagery is needed for rice identification when polarimetric data are available. The results also demonstrated some sensitivity to the growth stage in the polarimetric decompositions, especially with the Freeman–Durden and the Touzi parameters. A controlled experiment is planned to further develop the methodology for using polarimetric decompositions for rice monitoring and yield estimation.

Polarimetric SAR urban classification using the Touzi target scattering decomposition

The Touzi decomposition was recently introduced for a roll and unique incoherent decomposition of target scattering. Unlike the Cloude–Pottier incoherent target scattering decomposition, no scattering parameter averaging is performed and target scattering is fully characterized by an in-depth analysis of five parameters for each of the three (dominant, medium, and low scattering) single scattererings. For practical reasons, there is an immediate need for a procedure that permits the ranking and selection of the smallest Touzi decomposition parameter subset that permits an accurate image classification. In this study, a filter method based on mutual information criteria was introduced to select and rank the most suitable decomposition parameter subset for the classification of an urban site in Ottawa, Ontario. Two approaches are considered for the selection of the suitable parameter subset: the maximum mutual information and the maximum relevancy and minimum redundancy. The two approaches were compared in terms of feature selection and class discrimination, and the optimum subsets selected were used to perform an urban site classification based on a support vector machine. Polarimetric Convair 580 synthetic aperature radar data collected over an urban site in Ottawa were used to assess and validate the selection and classification results.

Phase of Target Scattering for Wetland Characterization Using Polarimetric C-Band SAR

Wetlands continue to be under threat, and there is a major need for mapping and monitoring wetlands for better management and protection of these sensitive areas. Only a few studies have been published on wetland characterization using polarimetric synthetic aperture radars (SARs). The most successful results have been obtained using the phase difference between HH and VV polarizations, phiHH - phiVV, which has shown promise for separating flooded wetland classes. Recently, we have introduced a new decomposition, the Touzi decomposition, which describes target scattering type in terms of a complex entity, the symmetric scattering type. Huynen's target helicity is used to assess the symmetric nature of target scattering. In this paper, the new complex-scattering-type parameters, the magnitude alphas and phase Phialpha s, are investigated for wetland characterization. The use of the dominant-scattering-type phase Phialpha s makes it possible to discriminate shrub bogs from poor (sedge or shrub) fens. These two classes cannot be separated using phiHH - phiVV, or the radiometric scattering information provided by alphas, the Cloude alpha, the entropy H, and the multipolarization HH-HV-VV channels. phialpha s , which cannot detect deep (45 cm below the peat surface) water flow in a bog, is more sensitive to the shallower (10-20-cm) fen beneath water, and this makes possible the separation of poor fens from shrub bogs. Phialpha s also permits the discrimination of conifer-dominated treed bog from upland deciduous forest under leafy conditions. Target helicity information is exploited to introduce a new parameter, the target asymmetry. The latter is shown very promising for detection of forest changes between leafy and no-leaf conditions. The analysis of low-entropy marsh scattering showed that both the scattering-type magnitude and phase alphas and Phialpha s, respectively, as well as the maximum polarization intensity of the dominant scattering m, are needed for a better understanding of marsh complex scattering mechanisms. The unique information provided by the new roll-invariant decomposition parameters are demonstrated using repeat-pass Convair-580 polarimetric C-band SAR data collected in June and October 1995 over the RAMSAR Mer Bleue wetland site near Ottawa (Canada).