Full-polarization Synthetic Aperture Radar Research Articles

Inversion of Forest above Ground Biomass in Mountainous Region Based on PolSAR Data after Terrain Correction: A Case Study from Saihanba, China

Accurate retrieval of forest above ground biomass (AGB) based on full-polarization synthetic aperture radar (PolSAR) data is still challenging for complex surface regions with fluctuating terrain. In this study, the three-step process of radiometric terrain correction (RTC), which includes polarization orientation angle correction (POAC), effective scattering area correction (ESAC), and angular variation effect correction (AVEC), is adopted as the technical framework. In the ESAC stage, a normalized correction factor is introduced based on local incidence angle and radar incidence angle to achieve accurate correction of PolSAR data information and improve the inversion accuracy of forest AGB. In order to verify the validity and robustness of this research method, the full-polarization SAR data of ALOS-2 and the ground measured AGB data collected in the Saihanba research area in 2020 were used for experiments. Our findings revealed that in the ESAC phase, the introduction of the normalized correction factor can effectively eliminate the ESA phenomenon and improve the correlation coefficients of the backscatter coefficient and AGB. Taking the data of 25 July 2020 as an example, ESAC increases the correlation coefficients between AGB and the backscattering coefficients of HH, HV, and VV polarization channels by 0.343, 0.296, and 0.382, respectively. In addition, the RTC process has strong robustness in different AGB statistical models and different date PolSAR data.

Combination of Hyperspectral and Quad-Polarization SAR Images to Classify Marsh Vegetation Using Stacking Ensemble Learning Algorithm

Combinations of multi-sensor remote sensing images and machine learning have attracted much attention in recent years due to the spectral similarity of wetland plant canopy. However, the integration of hyperspectral and quad-polarization synthetic aperture radar (SAR) images for classifying marsh vegetation has still been faced with the challenges of using machine learning algorithms. To resolve this issue, this study proposed an approach to classifying marsh vegetation in the Honghe National Nature Reserve, northeast China, by combining backscattering coefficient and polarimetric decomposition parameters of C-band and L-band quad-polarization SAR data with hyperspectral images. We further developed an ensemble learning model by stacking Random Forest (RF), CatBoost and XGBoost algorithms for marsh vegetation mapping and evaluated its classification performance of marsh vegetation between combinations of hyperspectral and full-polarization SAR data and any of the lone sensor images. Finally, this paper explored the effect of different polarimetric decomposition methods and wavelengths of radar on classifying wetland vegetation. We found that a combination of ZH-1 hyperspectral images, C-band GF-3, and L-band ALOS-2 quad-polarization SAR images achieved the highest overall classification accuracy (93.13%), which was 5.58–9.01% higher than that only using C-band or L-band quad-polarization SAR images. This study confirmed that stacking ensemble learning provided better performance than a single machine learning model using multi-source images in most of the classification schemes, with the overall accuracy ranging from 77.02% to 92.27%. The CatBoost algorithm was capable of identifying forests and deep-water marsh vegetation. We further found that L-band ALOS-2 SAR images achieved higher classification accuracy when compared to C-band GF-3 polarimetric SAR data. ALOS-2 was more sensitive to deep-water marsh vegetation classification, while GF-3 was more sensitive to shallow-water marsh vegetation mapping. Finally, scattering model-based decomposition provided important polarimetric parameters from ALOS-2 SAR images for marsh vegetation classification, while eigenvector/eigenvalue-based and two-component decompositions produced a great contribution when using GF-3 SAR images.

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

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

Assessment of underlying topography and forest height inversion based on TomoSAR methods

ABSTRACT Due to the strong penetrability, long-wavelength synthetic aperture radar (SAR) can provide an opportunity to reconstruct the three-dimensional structure of the penetrable media. SAR tomography (TomoSAR) technology can resynthesize aperture perpendicular to the slant-range direction and then obtain the tomographic profile consisting of power distribution of different heights, providing a powerful technical tool for reconstructing the three-dimensional structure of the penetrable ground objects. As an emerging technology, it is different from the traditional interferometric SAR (InSAR) technology and has advantages in reconstructing the three-dimensional structure of the illuminated media. Over the past two decades, many TomoSAR methods have been proposed to improve the vertical resolution, aiming to distinguish the locations of different scatters in the unit pixel. In order to cope with the forest mission of European Space Agency (ESA) that is designed to provide P-band SAR measurements to determine the amount of biomass and carbon stored in forests, it is necessary to systematically evaluate the performance of forest height and underlying topography inversion using TomoSAR technology. In this paper, we adopt three typical algorithms, namely, Capon, Multiple Signal Classification (MUSIC), and Compressed Sensing (CS), to evaluate the performance in forest height and underlying topography inversion. The P-band airborne full-polarization (FP) SAR data of Lopè National Park in the AfriSAR campaign implemented by ESA in 2016 is adopted to verify the experiment. Furthermore, we explore the effects of different baseline designs and filter methods on the reconstruction of the tomographic profile. The results show that a better tomographic profile can be obtained by using Hamming window filter and Capon algorithm in uniform baseline distribution and a certain number of acquisitions. Compared with LiDAR results, the root-mean-square error (RMSE) of forest height and underlying topography obtained by Capon algorithm is 2.17 m and 1.58 m, which performs the best among the three algorithms.

A novel full-polarization SAR image ship detector based on scattering mechanisms and wave polarization anisotropy

Synthetic aperture radar (SAR) is suitable for earth observation due to its unique advantages. In this study, we constructed an adaptive ship detector using full-polarization SAR images. First, we thoroughly investigated the differences in scattering characteristics between ships, their background, and the wave polarization anisotropy. We then constructed a novel ship detector that incorporates scattering and anisotropy (known as joint scattering–anisotropy [joint-SA]). We found that joint-SA is an effective physical quantity representing the difference between the ship and its background; thus, joint-SA can be used for ship detection using full-polarization image data. Second, we used generalized gamma distribution to characterize joint-SA statistics of sea clutter with a large homogeneity range. Third, an adaptive constant false alarm rate (CFAR) method was implemented based on joint-SA. Finally, RADARSAT-2 and GF-3 data in the C-band and ALOS data in the L-band were used for verification. We tested five datasets, and the experimental results verified the correctness and superiority of the CFAR method based on joint-SA. The results show that the signal–clutter ratio of the proposed ship detector (33.17 dB, 35.98 dB, and 57.25 dB) was higher than that of DBSP (8.92 dB, 3.43 dB, and 25.40 dB) and RsDVH (17.28 dB, 11.17 dB, and 54.55 dB). Furthermore, the proposed detector has a higher detection accuracy and lower false alarm rate than those of the other two other methods.

Ionospheric Sounding Based on Spaceborne PolSAR in P-Band

The signal of spaceborne low-frequency full-polarization synthetic aperture radar (full-pol SAR) contains abundant ionospheric information. Phased Array L-band Synthetic Aperture Radar (PALSAR) working in the L-band has been verified as an emerging ionospheric sounding technology. Aiming for a future P-band SAR system, this paper investigates the ability of the P-band SAR system in ionospheric one-dimensional and two-dimensional detection. First, considering different systematic error levels, the total electron content (TEC) retrieval in L/P-band is studied by using three typical full-pol SAR data sets based on a circular polarization algorithm. Second, the TEC data retrieved by SAR are fused with the ionosonde, and the joint retrieval of ionospheric electron density is performed. Results show that the P-band TEC retrieval is approximately twice as accurate as the L-band retrieval under the same conditions, and possesses excellent robustness. In addition, the TEC obtained by L/P-band SAR can be used to correct the electron density of the topside on the ionosonde. Results also show that compared with the topside correction accuracy of L-band SAR, that of the P-band SAR is improved by more than 20%. SAR has natural high-resolution characteristics and the P-band signal contains more obvious ionospheric information than the L-band signal. Therefore, future spaceborne P-band SAR has many advantages in two-dimensional fine ionospheric observation and one-dimensional electron density retrieval.

Synergetic Classification of Coastal Wetlands over the Yellow River Delta with GF-3 Full-Polarization SAR and Zhuhai-1 OHS Hyperspectral Remote Sensing

The spatial distribution of coastal wetlands affects their ecological functions. Wetland classification is a challenging task for remote sensing research due to the similarity of different wetlands. In this study, a synergetic classification method developed by fusing the 10 m Zhuhai-1 Constellation Orbita Hyperspectral Satellite (OHS) imagery with 8 m C-band Gaofen-3 (GF-3) full-polarization Synthetic Aperture Radar (SAR) imagery was proposed to offer an updated and reliable quantitative description of the spatial distribution for the entire Yellow River Delta coastal wetlands. Three classical machine learning algorithms, namely, the maximum likelihood (ML), Mahalanobis distance (MD), and support vector machine (SVM), were used for the synergetic classification of 18 spectral, index, polarization, and texture features. The results showed that the overall synergetic classification accuracy of 97% is significantly higher than that of single GF-3 or OHS classification, proving the performance of the fusion of full-polarization SAR data and hyperspectral data in wetland mapping. The synergy of polarimetric SAR (PolSAR) and hyperspectral imagery enables high-resolution classification of wetlands by capturing images throughout the year, regardless of cloud cover. The proposed method has the potential to provide wetland classification results with high accuracy and better temporal resolution in different regions. Detailed and reliable wetland classification results would provide important wetlands information for better understanding the habitat area of species, migration corridors, and the habitat change caused by natural and anthropogenic disturbances.

HDEC-TFA: An Unsupervised Learning Approach for Discovering Physical Scattering Properties of Single-Polarized SAR Image

Understanding the physical properties and scattering mechanisms contributes to synthetic aperture radar (SAR) image interpretation. For single-polarized SAR data, however, it is difficult to extract the physical scattering mechanisms due to lack of polarimetric information. Time-frequency analysis (TFA) on complex-valued SAR image provides extra information in frequency perspective beyond the “image” domain. Based on TFA theory, we propose to generate the subband scattering pattern for every object in complex-valued SAR image as the physical property representation, which reveals backscattering variations along slant-range and azimuth directions. In order to discover the inherent patterns and generate a scattering classification map from single-polarized SAR image, an unsupervised hierarchical deep embedding clustering (HDEC) algorithm based on TFA (HDEC-TFA) is proposed to learn the embedded features and cluster centers simultaneously and hierarchically. The polarimetric analysis result for quad-pol SAR images is applied as reference data of physical scattering mechanisms. In order to compare the scattering classification map obtained from single-polarized SAR data with the physical scattering mechanism result from full-polarized SAR, and to explore the relationship and similarity between them in a quantitative way, an information theory based evaluation method is proposed. We take Gaofen-3 quad-polarized SAR data for experiments, and the results and discussions demonstrate that the proposed method is able to learn valuable scattering properties from single-polarization complex-valued SAR data, and to extract some specific targets as well as polarimetric analysis. At last, we give a promising prospect to future applications.

Building Damage Detection Based on OPCE Matching Algorithm Using a Single Post-Event PolSAR Data

Synthetic aperture radar (SAR) is an effective tool in detecting building damage. At present, more and more studies detect building damage using a single post-event fully polarimetric SAR (PolSAR) image, because it permits faster and more convenient damage detection work. However, the existence of non-buildings and obliquely-oriented buildings in disaster areas presents a challenge for obtaining accurate detection results using only post-event PolSAR data. To solve these problems, a new method is proposed in this work to detect completely collapsed buildings using a single post-event full polarization SAR image. The proposed method makes two improvements to building damage detection. First, it provides a more effective solution for non-building area removal in post-event PolSAR images. By selecting and combining three competitive polarization features, the proposed solution can remove most non-building areas effectively, including mountain vegetation and farmland areas, which are easily confused with collapsed buildings. Second, it significantly improves the classification performance of collapsed and standing buildings. A new polarization feature was created specifically for the classification of obliquely-oriented and collapsed buildings via development of the optimization of polarimetric contrast enhancement (OPCE) matching algorithm. Using this developed feature combined with texture features, the proposed method effectively distinguished collapsed and obliquely-oriented buildings, while simultaneously also identifying the affected collapsed buildings in error-prone areas. Experiments were implemented on three PolSAR datasets obtained in fully polarimetric mode: Radarsat-2 PolSAR data from the 2010 Yushu earthquake in China (resolution: 12 m, scale of the study area: 50 km2); ALOS PALSAR PolSAR data from the 2011 Tohoku tsunami in Japan (resolution: 23.14 m, scale of the study area: 113 km2); and ALOS-2 PolSAR data from the 2016 Kumamoto earthquake in Japan (resolution: 5.1 m, scale of the study area: 5 km2). Through the experiments, the proposed method was proven to obtain more than 90% accuracy for built-up area extraction in post-event PolSAR data. The achieved detection accuracies of building damage were 82.3%, 97.4%, and 78.5% in Yushu, Ishinomaki, and Mashiki town study sites, respectively.

Forest height retrieval using P-band airborne multi-baseline SAR data: A novel phase compensation method

Synthetic aperture radar (SAR) tomography (TomoSAR) has been well-established for three-dimensional (3-D) information extraction of forests using the multi-baseline SAR data stacks. The multi-baseline SAR data stacks can be acquired by spaceborne and airborne SAR systems, but for forest scenarios, the data stacks acquired by the airborne SAR system are mostly used. Such a data stack has the advantages of short revisiting time and weak temporal decorrelation. However, due to the baseline errors (caused by the residual platform motion and the measurement errors of the navigation instruments), phase errors (PEs) will occur. PEs are independent of one track to the other, resulting in spreading and defocusing in tomographic imaging. In this paper, we proposed a novel phase compensation method named NC-PGA, which combines the methods of network construction (NC) and phase gradient autofocus (PGA) to estimate and compensate the PEs. The NC method uses the Delaunay triangulation network and beamforming to obtain an accurate elevation estimate of the selected permanent scatterers, which can be used as the prior information for subsequent processing to overcome the shortcomings of the PGA method in PEs estimation. The PGA method uses the spatial invariance of PEs in a limited area to compensate for the PE of each track. The applicability of the NC-PGA method is demonstrated using simulated data and real data. The real data contains two data stacks. The one is acquired by a full-polarization P-band airborne SAR system (developed independently by our project research team) over the study area in Saihanba Forest Farm in Hebei, China. The other one is acquired by ONERA SETHI airborne system over Paracou, French Guiana, in the frame of the European Space Agency’s campaign TropiSAR. We select a test area in the study area and successfully retrieve the height of the forest, and use LiDAR data for results validation and evaluation.

Research on Inversion of Soil Moisture in Karst Area Based on Full-Polarization SAR Data

In order to select a reasonable controlling mode of rocky desertification and restrain the development of rocky desertification with local conditions, the soil moisture in Karst area was studied by using full-polarization Synthetic Aperture Radar (SAR) data in this paper. Based on the decisive role of rocky desertification degree, vegetation, slope and characteristic of the soil, different models were employed to invert the soil moisture in different rocky desertification degree areas according to their land use type and vegetation as follows: (1) The advanced integral equation model (AIEM) was employed in the intensive rocky desertification and serious rocky desertification areas. (2) The water-cloud model (WCM) coupled with AIEM was employed in the slight rocky desertification and moderate rocky desertification areas. (3) The Michigan microwave canopy scattering (MIMICS) model coupled with AIEM was employed in the non-rocky desertification and potential rocky desertification areas. As a result, the soil moisture was inverted successfully in the whole study area with the total inversion accuracy of 84.37% and RMSE of 2.10. This result demonstrates that the proposed method can invert the soil moisture accurately and provide a powerful technical support for inverting the soil moisture in the Karst area with large span, fluctuating landform and diverse vegetation.

GA-SVR Algorithm for Improving Forest Above Ground Biomass Estimation Using SAR Data

Synthetic aperture radar (SAR) features have been demonstrated that they have the potentiality to improve forest above ground biomass (AGB) estimation accuracy, especially including polarimetric information. Genetic algorithms (GAs) have been successfully implemented in optimal feature identification, while support vector regression (SVR) has great robustness in parameter estimation. The use of combined GAs and SVR can improve the accuracy of forest AGB estimation through simultaneously identifying the optimal SAR features and selecting the SVR model parameters. In this article, 14 SAR polarimetric features were extracted from C-band and L-band full-polarization SAR images and worked as input SAR features, respectively. C-band data was acquired on GaoFen-3 mission, we also call it GF-3 image. L-band data was ALOS-2 PALSAR-2 data. Both feature subsets from GF-3 and ALOS-2 PALSAR-2 and SVR hyper parameters used in the forest AGB estimation were optimized by a GA processing, where 8 different settings of 3 kinds of parameters, as 512 kind of different combinations were applied for SVR hyper parameters searching field. The results of GA-SVR performance using the two datasets were presented and compared with two traditional methods: the algorithm of GA feature selection companied with default SVR parameters (GA+ default SVR), and the algorithm of GA feature selection companied with grid searching for SVR parameter selection (GA+Grid SVR). The results showed that the proposed GA-SVR algorithm improved the forest AGB estimation accuracy with cross-validation coefficient of 80.21% for GF-3 and 71.41% for ALOS-2 PALSAR-2 data.

CFAR Ship Detection Methods Using Compact Polarimetric SAR in a K-Wishart Distribution

Compact polarimetric target detection is currently attracting increasing interest due to the generation of synthetic aperture radar (SAR) systems. Recently, the polarimetric notch filter (PNF) has been demonstrated to be effective for ship detection in both full-polarization and compact polarization-mode SAR images. In this paper, the probability density function (PDF) and the probability of false alarms (PFA) of the PNF are derived in the K -Wishart case (where the texture obeys the gamma distribution) since K distributions are suitable for many scenes including sea clutter. The PDF and PFA are also derived in closed forms for the polarimetric whitening filter (PWF) in the K -Wishart distribution case, the performance of which is close to that of the optimal polarimetric detector. The constant false alarm rate threshold for ship detection can be obtained from the PFA via the bisection method, which allows for the automatic and adaptive implementation of ship detection in complicated sea backgrounds in practical applications. The performances of the two detectors are compared via simulated data and measured data from AIRSAR in the compact polarimetric SAR case. The results show that the detectors given are accurate, while the PWF detector is generally better than the PNF detector in the K -Wishart case of the CP SAR model.

In-season biomass estimation of oilseed rape (Brassica napus L.) using fully polarimetric SAR imagery

Accurate estimation of crop biophysical and biochemical parameters during crop growing seasons is essential for improving site-specific management and yield estimation. The potential ability of fully polarimetric synthetic aperture radar (SAR) data in estimating above-ground biomass of oilseed rape was investigated in this study. The temporal profile of different scattering intensity and polarimetric features during the entire growing season was identified with ground measurements. A polarimetric feature, relying on the polarimetric decomposition method, was put forward to estimate the biomass of oilseed rape. Validation results revealed great potential with a determination coefficient (R2) of 0.85, root mean squared error (RMSE) of 41.6 g/m2, and relative error (RE) of 28.5% for dry biomass, and an R2 of 0.76, RMSE of 527.4 g/m2 and RE of 28.6% for fresh biomass. Moreover, the use of full polarization SAR data was compared with single and dual polarization SAR data. The results suggest that when full polarization SAR data is available, a simpler model, higher saturation point and better accuracy can be achieved in biomass estimation of oilseed rape, which highlights the importance and value of polarimetry information in quantitative crop monitoring. This study provides guidelines for in-season monitoring of crop growth parameters with SAR data, which further improves crop monitoring capability in adverse weather conditions.

Geo-Positioning Accuracy Improvement of Multi-Mode GF-3 Satellite SAR Imagery Based on Error Sources Analysis.

The GaoFen-3 (GF-3) satellite is the only synthetic aperture radar (SAR) satellite in the High-Resolution Earth Observation System Project, which is the first C-band full-polarization SAR satellite in China. In this paper, we proposed some error sources-based weight strategies to improve the geometric performance of multi-mode GF-3 satellite SAR images without using ground control points (GCPs). To get enough tie points, a robust SAR image registration method and the SAR-features from accelerated segment test (SAR-FAST) method is used to achieve the image registration and tie point extraction. Then, the original position of these tie points in object-space is calculated with the help of the space intersection method. With the dataset clustered by the density-based spatial clustering of applications with noise (DBSCAN) algorithm, we undertake the block adjustment with a bias-compensated rational function model (RFM) aided to improve the geometric performance of these multi-mode GF-3 satellite SAR images. Different weight strategies are proposed to develop the normal equation matrix according to the error sources analysis of GF-3 satellite SAR images, and the preconditioned conjugate gradient (PCG) method is utilized to solve the normal equation. The experimental results indicate that our proposed method can improve the geometric positioning accuracy of GF-3 satellite SAR images within 2 pixels.

A STATISTICAL TEXTURE FEATURE FOR BUILDING COLLAPSE INFORMATION EXTRACTION OF SAR IMAGE

Abstract. Synthetic Aperture Radar (SAR) has become one of the most important ways to extract post-disaster collapsed building information, due to its extreme versatility and almost all-weather, day-and-night working capability, etc. In view of the fact that the inherent statistical distribution of speckle in SAR images is not used to extract collapsed building information, this paper proposed a novel texture feature of statistical models of SAR images to extract the collapsed buildings. In the proposed feature, the texture parameter of G0 distribution from SAR images is used to reflect the uniformity of the target to extract the collapsed building. This feature not only considers the statistical distribution of SAR images, providing more accurate description of the object texture, but also is applied to extract collapsed building information of single-, dual- or full-polarization SAR data. The RADARSAT-2 data of Yushu earthquake which acquired on April 21, 2010 is used to present and analyze the performance of the proposed method. In addition, the applicability of this feature to SAR data with different polarizations is also analysed, which provides decision support for the data selection of collapsed building information extraction.

Characteristics Analysis and Image Processing for Full-Polarization Synthetic Aperture Radar Based on Electromagnetic Scattering From Flat Horizontal Perfect Electric Conducting Reflector

In this paper, the physical optics method is employed to study the problem of characteristics analysis and image processing for full-polarization synthetic aperture radar (SAR), where electromagnetic scattering from a flat horizontal perfect electric conducting (PEC) reflector is involved. The model of the full-polarization SAR echo from a small region of flat horizontal PEC reflector is deduced based on the dyadic Green’s function theory. With the available echo model, image processing and results are presented for the full-polarization SAR. For the horizontal co-polarization channel, the amplitude of imaging result descends with the squint angle, where the well-focused imaging result can always be obtained. On the contrary, the amplitude of cross-polarization channel imaging results is increased with the squint angle. When the radar works in the side-looking mode or low-squint mode, the cross-polarization channel echo cannot be well focused. For the high-squint mode, it can be well focused. For the horizontal co-polarization channel, the amplitude of well-focused result can be approximately regarded as a constant for all squint angles. The effectiveness of the characteristics analysis results is demonstrated via simulated and real measured Ka-band airborne full-polarization SAR data.

Corrections to "Change Detection in Full and Dual Polarization, Single- and Multi-Frequency SAR Data" [Aug 15 4041-4048

When the covariance matrix formulation is used for multi-look polarimetric synthetic aperture radar (SAR) data, the complex Wishart distribution applies. Based on this distribution a test statistic for equality of two complex variance-covariance matrices and an associated asymptotic probability of obtaining a smaller value of the test statistic are given. In a case study airborne EMISAR C- and L-band SAR images from the spring of 1998 covering agricultural fields and wooded areas near Foulum, Denmark, are used in single- and bi-frequency, bi-temporal change detection with full and dual polarimetry data.

G0-WISHART DISTRIBUTION BASED CLASSIFICATION FROM POLARIMETRIC SAR IMAGES

Abstract. Enormous scientific and technical developments have been carried out to further improve the remote sensing for decades, particularly Polarimetric Synthetic Aperture Radar(PolSAR) technique, so classification method based on PolSAR images has getted much more attention from scholars and related department around the world. The multilook polarmetric G0-Wishart model is a more flexible model which describe homogeneous, heterogeneous and extremely heterogeneous regions in the image. Moreover, the polarmetric G0-Wishart distribution dose not include the modified Bessel function of the second kind. It is a kind of simple statistical distribution model with less parameter. To prove its feasibility, a process of classification has been tested with the full-polarized Synthetic Aperture Radar (SAR) image by the method. First, apply multilook polarimetric SAR data process and speckle filter to reduce speckle influence for classification result. Initially classify the image into sixteen classes by H/A/α decomposition. Using the ICM algorithm to classify feature based on the G0-Wshart distance. Qualitative and quantitative results show that the proposed method can classify polaimetric SAR data effectively and efficiently.

Observation of Wind Direction Change on the Sea Surface Temperature Front Using High-Resolution Full Polarimetric SAR Data

In this study, we derive high-resolution wind speeds and directions from full-polarization synthetic aperture radar (SAR) data. Previous wind retrieval result from conventional single-polarization SAR data has a limitation to resolve small-scale structures in the surface wind because external wind direction data with coarser spatial resolution than those of SARs have been commonly used as an input. Using fully polarimetric SAR data, however, both wind speed and direction can be derived with high resolution from the image itself without any ancillary data. We derive wind field off the southern coast of Korea from the Radarsat-2 quad-polarization data and investigate the spatial variation. The retrieved wind field from the Radarsat-2 image presents a detailed structure including small-scale variations which is unobtainable from conventional wind observations. Comparison of the derived wind directions with in-situ buoy wind measurements shows a small difference of 8° which is regarded as sufficient to analyze small-scale wind vector changes. The retrieved wind field off the southern coast of Korea demonstrates the distinct patterns of direction changes. While blowing over the sea surface temperature (SST) frontal zone, the veering angles of wind vectors decrease and then are restored. The analysis of SAR-derived wind vectors with coinciding temperature distributions confirms that the variation in SAR-derived wind vectors on the SST fronts is mainly induced by the stability effect. This study also addresses the important role of precise wind direction retrieval on the accuracy of retrieved wind speed.