Quad-pol Synthetic Aperture Radar Research Articles

Wave retrieval from quad-polarized Chinese Gaofen-3 SAR image using an improved tilt modulation transfer function

ABSTRACT An accurate Modulation Transfer Function (MTF) is essential for Synthetic Aperture Radar (SAR) wave spectra retrieval. This study aimed to investigate the performance of a quad-polarized wave retrieval algorithm based on fully polarimetric SAR image data using the improved tilt MTF and considering the influence of wind speed. The tilt MTF is the key factor in the wave retrieval scheme from quad-polarized (Vertical–Vertical (VV), Horizontal–Horizontal (HH), Vertical–Horizontal (VH), and Horizontal–Vertical (HV)) SAR images. In this study, the waves were inverted from more than 1300 Gaofen-3 (GF-3) images acquired in quad-polarization strip mode with a spatial resolution of 16 m and a swath coverage of 50 km. The winds were retrieved using the Geophysical Model Function (GMF) C-band SAR model for Gaofen-3 (CSARMOD-GF), which is suitable for re-calibrated GF-3 images in VV-polarization. The comparison of the wind speed yielded a Root Mean Square Error (RMSE) of 1.73 m/s and a Correlation Coefficient (COR) of 0.94. The validation of the Significant Wave Height (SWH) simulated using the Waves Nearshore (SWAN) model against Haiyang-2B (HY-2B) altimeter data yielded an RMSE of 0.56 m and a COR of 0.87. The results reveal that the SAR-derived wind and SWAN-simulated SWH are suitable for analysis of SAR wave retrieval. The full polarimetric technique was applied to the collected images, and the statistical analysis yielded a RMSE of 0.51 m, a COR of 0.75, and a Scatter Index (SI) of 0.44 compared with the SWHs retrieved using the simulations from the SWAN model. The non-polarized contribution in the Normalized Radar Cross Section (NRCS; unit: dB) caused by wave breaking was calculated using a theoretical approach that employs the VV-polarized calibrated NRCS and HH-polarized calibrated NRCS. The effect of wave breaking on the SAR retrieval waves was studied. The bias (SAR-derived minus SWAN-simulated SWH) increased as the (/) ratio (>0.4) increased, and the accuracy improved when the ratio was less than 0.4. This behavior is reasonable since the wave breaking inevitably affects the tilt modulation. Therefore, wave breaking should be considered in SAR wave retrieval using the approach proposed in this paper under extreme sea states, such as typhoons and hurricanes.

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.

Fine Resolution Classification of New Ice, Young Ice, and First-Year Ice Based on Feature Selection from Gaofen-3 Quad-Polarization SAR

A new method of sea ice classification based on feature selection from Gaofen-3 polarimetric Synthetic Aperture Radar (SAR) observations was proposed. The new approach classifies sea ice into four categories: open water (OW), new ice (NI), young ice (YI), and first-year ice (FYI). Seventy parameters that have previously been applied to sea ice studies were re-examined for sea ice classification in the Okhotsk Sea near the melting point on 28 February 2020. The ‘separability index (SI)’ was used for the selection of optimal features for sea ice classification. Full polarization parameters (the backscatter intensity contains the horizontal transmit-receive intensity (σhh0), Shannon entropy (SEi), the spherical scattering component of Krogager decomposition (Ks)), and hybrid polarization parameters (horizontal receive intensity(σrh0), hybrid-pol Shannon entropy (CPSEi), the correlation coefficient (ρrh−rv) between the σrh0 and σrv0, and the surface scattering component of m − α decomposition αs) were determined as the optimal parameters for the different work modes of SAR. The selected parameters were used to classify sea ice by the random forest classifier (RFC), and classification results were validated by manually interpreted ice maps derived from Landsat-8 data. The classification accuracy of OW, NI, YI and FYI reached 95%, 96%, 98% and 85%, respectively.

The InflateSAR Campaign: Developing Refugee Vessel Detection Capabilities with Polarimetric SAR

In the efforts to mitigate the ongoing humanitarian crisis at the European sea borders, this work builds detection capabilities to help find refugee boats in distress. For this paper, we collected dual-pol and quad-pol synthetic aperture radar (SAR) data over a 12 m rubber inflatable in a test-bed lake near Berlin, Germany. To consider a real scenario, we prepared the vessel so that its backscattering emulated that of a vessel fully occupied with people. Further, we collected SAR imagery over the ocean with different sea states, categorized by incidence angle and by polarization. These were used to emulate the conditions for a vessel located in ocean waters. This setup enabled us to test nine well-known vessel-detection systems (VDS), to explore the capabilities of new detection algorithms and to benchmark different combinations of detectors (detector fusion) with respect to different sensor and scene parameters (e.g., the polarization, wind speed, wind direction and boat orientation). This analysis culminated in designing a system that is specifically tailored to accommodate different situations and sea states.

Proof and Application of Discriminating Ocean Oil Spills and Seawater Based on Polarization Ratio Using Quad-Polarization Synthetic Aperture Radar

This paper focuses on the proof and application of discriminating between oil spills and seawater (including the “look-alikes”, named low wind areas) based on the polarization ratio. A new relative polarization ratio (PRr) method is proposed, which is based on the difference between the scattering mechanism and the dielectric constant for oil spills compared to that of seawater. The case study found that (1) PRr numerically amplifies the contrast between oil spills and seawater, reduces the difference between low wind areas and ordinary seawater, and exhibits better details of the image; (2) the threshold method based on Euclidean distance can obtain the highest classification overall accuracy within the allowable error range, and can be widely used in the study of different incidence angles and environmental conditions; and (3) the identification of oil spills and seawater by the proposed methods can largely avoid the misjudgment of low wind areas as oil spills. Considering visual interpretation as the reference ‘ground truth’, the overall classification accuracy of all cases is more than 95%; only the edge of the diffuse thin oil slick and oil–water mixture is difficult to identify. This method can serve as an effective supplement to existing oil spill detection methods.

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

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

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.

Retrieval of Farmland Surface Soil Moisture Based on Feature Optimization and Machine Learning

Soil moisture is an important parameter affecting environmental processes such as hydrology, ecology, and climate. Synthetic aperture radar (SAR) microwave remote sensing is an important means of farmland surface soil moisture (SSM) measurement. The inversion of farmland SSM by microwave remote sensing is greatly affected by vegetation cover. To address this problem, a multisource remote sensing inversion method of farmland SSM based on feature optimization and machine learning is proposed in this paper. Six typical machine learning algorithms suitable for small sample training, including random forest, radial basis function neural network, generalized regression neural network, support vector regression, genetic algorithm–back propagation neural network, and extreme learning machine, were selected in this paper. The features extracted from Sentinel-1/2 and Radarsat-2 remote sensing data were analyzed by Pearson correlation, and those with high correlation coefficients were selected to form the optimal feature subset as the input for the subsequent machine learning models. Then, the SSM collaborative inversion models under different machine learning algorithms were constructed, and comparative experiments were set up to select the optimal prediction model. The models’ accuracy under different feature parameters were studied, and the difference in the performance between the dual-polarization SAR data and the quad-polarization SAR data in SSM inversion was explored. The experimental results showed that among the six models, the random forest model had a higher inversion accuracy, with a coefficient of determination of 0.6395 and a root mean square error of 0.0264 cm3/cm3. Meanwhile, the inversion accuracy could be greatly improved after feature optimization, and the inversion accuracy of the quad-polarization SAR data combined with optical remote sensing data, was better than that of the dual-polarization SAR data combined with optical remote sensing data.

Influence of Residual Amplitude and Phase Error for GF-3 Quad-Polarization SAR on Wind Vector Retrieval

High-resolution wind vector is important to investigate local winds’ variability over the global ocean. Quad-polarization Synthetic Aperture Radar (SAR) can provide wind vector independently without any external wind direction inputs. Although quad-polarization SAR wind retrieval algorithms have been widely studied, improvements are still required. The amplitude and phase imbalance of polarization channel cannot be neglected for improving the wind vector retrieval precision. In this study, rainforest was performed to remove the amplitude and phase imbalance of polarization channel of GF-3 SAR. To explore the applicability of this method for sea surface measurement, the influence of residual amplitude and phase error for GF-3 quad-polarization SAR on wind vector retrieval was assessed. Variation of amplitude and phase imbalance of sea surface for transmit and receive channel were assessed against collocated wind speed and incidence angle. Considering the polarization difference of VV channel relative to HH channel, the residual amplitude and phase error was found to be closely related to wind speed and polarization isolation. Correction of residual amplitude and phase error were employed to improve the retrieval precision of wind vector. It is revealed that the wind speed retrieval precision of VV polarization improved with correction of residual amplitude error. In addition, the influence of residual amplitude and phase error on wind direction retrieval can be neglected. Thus, it is concluded that correction of amplitude and phase error has the potential to improve wind vector retrievals from GF-3 quad-polarization SAR.

A Comparative Study on Classification Features between High-Resolution and Polarimetric SAR Images through Unsupervised Classification Methods

Feature extraction and comparison of synthetic aperture radar (SAR) data of different modes such as high resolution and full polarization have important guiding significance for SAR image applications. In terms of image and physical domain for higher spatial resolution single-polarized and coarser spatial resolution quad-pol SAR data, this paper analyzes and compares the feature extraction with unsupervised classification methods. We discover the correlation and complementarity between high-resolution image feature and quad-pol physical scattering information. Therefore, we propose an information fusion strategy, that can conduct unsupervised learning of the landcover classes of SAR images obtained from multiple imaging modes. The medium-resolution polarimetric SAR (PolSAR) data and the high-resolution single-polarized data of the Gaofen-3 satellite are adopted for the selected experiments. First, we conduct the Freeman–Durden decomposition and H/alpha-Wishart classification method on PolSAR data for feature extraction and classification, and use the Deep Convolutional Embedding Clustering (DCEC) algorithm on single-polarized data for unsupervised classification. Then, combined with the quantitative evaluation by confusion matrix and mutual information, we analyze the correlation between characteristics of image domain and physics domain and discuss their respective advantages. Finally, based on the analysis, we propose a refined unsupervised classification method combining image information of high-resolution data and physics information of PolSAR data, that optimizes the classification results of both the urban buildings and the vegetation areas. The main contribution of this comparative study is that it promotes the understanding of the landcover classification ability of different SAR imaging modes and also provides some guidance for future work to combine their respective advantages for better image interpretation.

Quad-polarimetric SAR sea state retrieval algorithm from Chinese Gaofen-3 wave mode imagettes via deep learning

Synthetic aperture radar (SAR) is a powerful tool for monitoring sea states in terms of the significant wave height (SWH). Regarding the specific wave mode, to date, the previous empirical models for estimating SWH from SAR data rely on single polarization. In the emerging deep learning era, few published quad-polarized SAR SWH retrieval algorithms have been based on machine learning technique, and whether quad-polarimetry improves the skill of wave height estimation remains a question. Here we propose a deep residual convolutional neural network-based SAR SWH retrieval algorithm in quad-polarization. By collocating WaveWatch III sea state hindcasts and all available archives of quad-polarized Chinese Gaofen-3 SAR imagettes in wave mode, a database with approximately 30,000 matchups was employed to establish our deeply-learned network. The GaoFen-3 significant wave height retrievals were validated against the hindcast dataset independent of training along with altimeter observations. The result of good consistency in terms of a root mean square error of 0.32 m (under sea state conditions of approximately 0.5–7.0 m) outperforms the existing Gaofen-3 wave height retrieval algorithms. Additionally, this paper introduces a discussion about the contribution of polarizations by comparing SWH derived from single-, dual- and quad-polarized deep convolutional neural networks. Single-polarized Gaofen-3 SAR data are found to be sufficient to provide accurate estimates compared to quad-polarization via a deep learning model under moderate sea conditions. Exploitation of SAR quad-polarimetry information will improve SAR wave height retrievals under high sea conditions.

TSPol-ASLIC: Adaptive Superpixel Generation With Local Iterative Clustering for Time-Series Quad- and Dual-Polarization SAR Data

The superpixel generation is a key step for object-based classification and change detection. For the time-series polarimetric synthetic aperture radar (PolSAR) superpixel generation, the traditional polarimetric similarity measure based on the joint covariance matrix has limitations in discriminating different time-series similarity sequences with different fluctuations. Besides, in the traditional time-series PolSAR superpixel generation methods, it is difficult to determine the tradeoff factor between polarimetric and spatial similarity. In this article, an adaptive time-series PolSAR superpixel generation method based on the simple local iterative clustering (SLIC) is proposed, named time-series polarimetric SAR (TSPol)-adaptive simple local iterative clustering (ASLIC). There are three main improvements. First, a novel time-series polarimetric similarity measure based on the root mean square (rms) is proposed. Multitemporal polarimetric statistical information is combined to describe the polarimetric proximity between pixels, referring to the rms of the multitemporal proximities. Second, an edge detection method based on the stacked 2-D Gaussian-shaped (s2-D GS) window is proposed to initialize the central seeds for superpixel generation. Third, an improved SLIC clustering similarity combined with the time-series polarimetric, time-series power, and spatial similarities is proposed. Meanwhile, a homogeneity factor is applied to adaptively balance the relative weights of various similarities. We use eight Radarsat-2 quad-polarization synthetic aperture radar (SAR) images and 14 Sentinel-1 dual-polarization SAR images to evaluate the effectiveness. The results show our similarity measure and superpixel generation results are superior to those of the traditional methods. For example, as for the Radarsat-2 data, the improvement of the boundary recall by the proposed similarity measure and homogeneity factor is about 4% and 10%, respectively.

Pattern Synthesis Algorithm for Range Ambiguity Suppression in the LT-1 Mission via Sequential Convex Optimizations

The innovative spaceborne Earth observation mission LuTan-1 (LT-1) deploys advanced full polarimetric L-band synthetic aperture radar (SAR) to obtain high-precision, multidimensional ground feature information. As the quad-pol SAR system, LT-1 suffers from strong ambiguities that degrade the quality of the observation products. Antenna pattern synthesis algorithm can suppress the range ambiguities without the increase of the azimuth ambiguities and the system complexity and therefore has great potential to improve the overall ambiguity performance of the quad-pol SAR system. However, the previous research on this kind of method is generally limited to specific situations and lacks of the analysis of actual measurement results. Based on the application requirements of LT-1, this article proposes a novel pattern synthesis algorithm that suppresses the range ambiguities via sequential convex optimizations. In simulation and comparison, the proposed algorithm effectively suppresses the strong co-polarized range ambiguities and shows the flexibility, efficiency, and stability that are significantly better than the previous algorithms. What is more, the analysis of practical performance loss is performed based on the measurement result of the LT-1 antenna, and the practicality and validity of the algorithm are strongly verified.

Cross-Pol Pattern Effects of Parabolic Reflector Antennas on the Performance of Spaceborne Quad-Pol SAR Systems

The ambiguity performance related to the image quality of a synthetic aperture radar (SAR) system can be categorized into range ambiguity, azimuth ambiguity, and cross ambiguity depending on the cause of the ambiguity signals. The ambiguity performance is affected by the operational method and antenna radiation patterns of an SAR system. In this paper, the effect of the antenna cross-polarization (cross-pol) pattern on the ambiguity performance of a spaceborne quadrature polarimetric (quad-pol) SAR system using an X-band reflector antenna was analyzed. Two methods, conventional quad-pol SAR and hybrid quad-pol SAR systems, have been proposed as quad-pol SAR operational concepts for system performance analysis. We proposed a process to effectively analyze the effect of the antenna cross-pol pattern and showed this through a comparison of several analysis methods. In addition, the difference in antenna radiation patterns according to the size and structure of reflector antennas was compared, and the effects were described in detail. In conclusion, the larger the antenna and the more complex the structure, the greater the effect of the antenna cross-pol pattern, leading to a similar phenomenon as the two quad-pol SAR systems.

Ambiguity Suppression Based on Joint Optimization for Multichannel Hybrid and ±π/4 Quad-Pol SAR Systems

Hybrid and ±π/4 quadrature-polarimetric (quad-pol) synthetic aperture radar (SAR) systems operating from space can obtain all polarimetric components simultaneously but suffer from severe azimuth ambiguities in the cross-polarized (cross-pol) measurement channels. In this paper, the hybrid and ±π/4 quad-pol SAR systems with multiple receive channels in azimuth are widely investigated to suppress the azimuth ambiguities of the cross-pol components. We first provide a more thorough analysis of the multichannel hybrid and ±π/4 quad-pol SAR systems. Then, the multichannel signal processing is briefly discussed for the reconstruction of the quad-pol SAR signal from the aliased signals, in which the conventional reconstruction algorithm causes extremely severe azimuth ambiguities. To this end, an improved reconstruction method is proposed based on a joint optimization, which allows for the minimization of ambiguities from the desired polarization and the simultaneous power of undesired polarized signal. This method can largely suppress azimuth ambiguities compared with the conventional reconstruction algorithm. Finally, to verify the advantages and effectiveness of the proposed approach, the azimuth ambiguity-to-signal ratio (AASR), the range ambiguity-to-signal ratio (RASR) and signal-to-noise ratio (SNR) of all polarizations, as well as a set of imaging simulation results, are given to describe the effects of reconstruction on the multichannel hybrid and ±π/4 quad-pol SAR systems.

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.

Characteristics of Saline Soil in Extremely Arid Regions: A Case Study Using GF-3 and ALOS-2 Quad-Pol SAR Data in Qinghai, China

Soil salinization is a global problem, which seriously damages the ecological environment and considerably reduces agricultural productivity, especially in arid regions. Synthetic aperture radar (SAR) has been widely used in remote sensing due to its weather and sunlight independence. Polarimetric SAR has great potential for large-scale mapping and monitoring salt-affected soils. In this study, we investigate the characteristics of saline soil in extremely arid regions using dual-band quadrature-polarimetric (quad-pol) SAR images acquired by GF-3 (C-band) and ALOS-2 (L-band). Firstly, the effectiveness of the modified dielectric mixing model and integral equation model (IEM) in describing saline soil is evaluated. Secondly, the potential relationships between polarimetric parameters and salinity are discussed in both the C- and L-band, respectively, such as co-polarization ratio, scattering entropy H, and scattering angle α. Finally, a linear regression model for monitoring salt content is established. The main contributions of this article are as follows: (1) Simulation results suggest that the radar backscattering coefficient is a weak function of salinity at low water content, but our experimental data show that soil salinity significantly contributes to the radar backscattering coefficient, which indicates the modified dielectric mixing model and IEM model is not applicable in extremely arid areas. (2) A negative correlation between the co-polarization ratio and salinity is observed, and the correlation coefficients are 0.64 (C-band) and 0.71 (L-band). Besides, scattering entropy and scattering angle exhibit a positive correlation with salinity in the C-band with correlation coefficients 0.686 and 0.669, respectively, whereas a negative correlation is found in the L-band with correlation coefficients 0.682 and 0.680, respectively. This can be attributed to the different penetration depths and sensitivity to the surface roughness of the electromagnetic waves at two frequencies. (3) A regression model for salinity estimating based on radar backscattering coefficient, co-polarization ratio, and scattering entropy is established, with a determination coefficient (R2) of 0.79 and a root mean square error (RMSE) of 6.56%, allowing us to determine soil salinity from quad-pol SAR images without using backscattering models. Therefore, our results can be a reference for future soil salinity monitoring and inversion.

CP-IRGS: A Region-Based Segmentation of Multilook Complex Compact Polarimetric SAR Data

The Canadian RADARSAT constellation mission (RCM) is represented by three synthetic aperture radar (SAR) satellites, each of which includes a compact polarimetry (CP) mode. CP is advantageous because it provides increased backscatter information relative to single and conventional dual-polarized modes and has larger swath widths relative to a quad polarization mode. CP captures single-look complex data which can be used to derive the multilook complex (MLC) coherence matrix, or, equivalently, the Stokes vector data of the backscattered field. The challenge is to develop computer vision algorithms that can be used to effectively segment the scene using this new data source. An unsupervised region-based segmentation approach has been designed and implemented that utilizes the complex Wishart distribution characteristic of the MLC CP data. The segmentation method is based on the iterative region growing with semantics algorithm originally designed for single and dual pol intensity SAR data. The algorithm has been tested using both simulated CP SAR images and a pair of available quad polarization SAR images. The results demonstrate that the CP-IRGS algorithm provides more accurate segmentation images than those using only the RH and RV channel intensity images.

Discrimination of open water from sea ice in the Labrador Sea using quad-polarized synthetic aperture radar

A method to differentiate open water from sea ice using quad-polarization C-band synthetic aperture radar (SAR) imagery is presented, based on the roughness properties of the sea surface scattering facets and the polarization ratio (PR) between VV and HH polarization backscatter from the SAR images. In quad-polarization SAR images, the normalized radar cross section (NRCS), which is linked to sea surface roughness, can be used to derive the orientation angle shift and its standard deviation, as well as to compute the measured polarization ratio, PR. An X-Bragg backscatter model is explored to calculate a theoretical look-up table for the polarization ratio as a function of incidence angle, dielectric constant, orientation angle shift and its standard deviation. Thus, ocean surface backscatter can be identified as open water or sea ice by comparing the measured PR with a theoretical model for PR. The proposed method is validated by comparison to collocated optical video images and manual visual interpretations. Estimates of the size, shape and locations of open water from our method are shown to be in good agreement with observed data. The overall accuracy of our algorithm is about 96%. The classification accuracy for sea ice and open water is about 96% and 94%, respectively.

Azimuth Ambiguity Suppression for Hybrid Polarimetric Synthetic Aperture Radar via Waveform Diversity

Hybrid quadrature polarimetric (hybrid quad-pol) synthetic aperture radar (SAR) is proposed as a potential candidate for the full-polarimetric SAR mode. It allows balanced range ambiguity performance and simplified system structure. System based on hybrid-pol SAR mode can also implement the conventional quad-pol mode and the compact-pol mode via few adjustments. However, the azimuth ambiguity performance in cross-pol channels is proved deteriorated in hybrid quad-pol mode due to the lopsided energy distribution of ambiguities. As are generally called “ghost” targets, azimuth ambiguities usually influence the recognition of the targets in SAR imaging. This letter describes how to remove the false targets that arise from azimuth ambiguities by means of waveform diversity and dual-focus post-processing (DFPP) technique. The proposed method exploits the feature of azimuth ambiguity and yields improved image quality in cross-pol channels with strong co-pol azimuth ambiguities removed in hybrid quad-pol SAR at a low system cost. Furthermore, it offers remarkable benefits for target detecting and recognition with strong false targets removed.