Multi-polarization Synthetic Aperture Radar Research Articles

TENet: A Texture-Enhanced Network for Intertidal Sediment and Habitat Classification in Multiband PolSAR Images

This paper proposes a texture-enhanced network (TENet) for intertidal sediment and habitat classification using multiband multipolarization synthetic aperture radar (SAR) images. The architecture introduces the texture enhancement module (TEM) into the UNet framework to explicitly learn global texture information from SAR images. The study sites are chosen from the northern part of the intertidal zones in the German Wadden Sea. Results show that the presented TENet model is able to detail the intertidal surface types, including land, seagrass, bivalves, bright sands/beach, water, sediments, and thin coverage of vegetation or bivalves. To further assess its performance, we quantitatively compared our results from the TENet model with different instance segmentation models for the same areas of interest. The TENet model gives finer classification accuracies and shows great potential in providing more precise locations.

Unified Classification Framework for Multipolarization and Dual-Frequency SAR

For synthetic aperture radar (SAR), multi-polarization and multi-frequency modes greatly enrich the acquired earth resource information and have been widely applied in remote sensing fields. In this paper, we compare the classification capabilities of multi-polarization and dual-frequency SAR. To meet the objective of selecting consistent and complete polarimetric information, a unified classification framework is proposed. In the framework, covariance matrices are used directly as inputs instead of polarimetric indicators. Additionally, the Wishart mixture model (WMM) is utilized to characterize the statistical distribution of polarimetric SAR data. Besides, the data log-likelihood function is utilized to mitigate the influence of the initial values on the expectation-maximization (EM) algorithm. Then, among the combinations of four sample-to-subclass distances and two schemes for obtaining sample-to-class distances, the one with the best classification performance is selected as the default for this framework. In the experiments, the classification capabilities of full polarization (FP), compact polarization (CP), and dual polarization (DP) modes are first compared through the proposed classification framework. Then, we compare the classification capabilities of dual-frequency SAR in FP, CP, and DP modes. For PolSAR system design, it is necessary to strike a balance between demand indexes (classification performance, coverage width, and so on) and cost (such as budget, weight, and so on). The comparison results provide a reference for the optimization of polarization modes and frequency bands of the existing multi-polarization and dual-frequency SAR payloads and the design of future SAR systems.

Flood Detection in Dual-Polarization SAR Images Based on Multi-Scale Deeplab Model

The proliferation of massive polarimetric Synthetic Aperture Radar (SAR) data helps promote the development of SAR image interpretation. Due to the advantages of powerful feature extraction capability and strong adaptability for different tasks, deep learning has been adopted in the work of SAR image interpretation and has achieved good results. However, most deep learning methods only employ single-polarization SAR images and ignore the water features embedded in multi-polarization SAR images. To fully exploit the dual-polarization SAR data and multi-scale features of SAR images, an effective flood detection method for SAR images is proposed in this paper. In the proposed flood detection method, a powerful Multi-Scale Deeplab (MS-Deeplab) model is constructed based on the dual-channel MobileNetV2 backbone and the classic DeeplabV3+ architecture to improve the ability of water feature extraction in SAR images. Firstly, the dual-channel feature extraction backbone based on the lightweight MobileNetV2 separately trains the dual-polarization SAR images, and the obtained training parameters are merged with the linear weighting to fuse dual-polarization water features. Given the multi-scale space information in SAR images, then, a multi-scale feature fusion module is introduced to effectively utilize multi-layer features and contextual information, which enhances the representation of water features. Finally, a joint loss function is constructed based on cross-entropy and a dice coefficient to deal with the imbalanced categorical distribution in the training dataset. The experimental results on the time series of Sentinel-1A SAR images show that the proposed method for flood detection has a strong ability to locate water boundaries and tiny water bodies in complex scenes. In terms of quantitative assessment, MS-Deeplab can achieve a better performance compared with other mainstream semantic segmentation models, including PSPNet, Unet and the original DeeplabV3+ model, with a 3.27% intersection over union (IoU) and 1.69% pixel accuracy (PA) improvement than the original DeeplabV3+ model.

SAR Antenna Pattern Measurement by Internal Calibration Method for GF-3 Satellite

GF-3 is the first C-band multipolarization synthetic aperture radar (SAR) satellite in China. The SAR system is equipped with an active phased array antenna to electronically generate various antenna beams. The accuracy of SAR antenna patterns is of main importance for precise SAR image processing. The traditional method for antenna pattern measurement is the near-field method but it is unable to satisfy the demand for fast measuring in-orbit. In order to measure the SAR antenna pattern quickly with high accuracy, we propose the internal calibration method. The internal calibration uses calibration signals which are routed along the nominal signal path in SAR systems, thus monitoring the gain and phase variations for each T/R channel is possible. This paper will focus on this method, including its principle, operation steps, and results, compared to the near-field method. The internal calibration method provides a valuable solution for SAR antenna pattern measurement.

Multi-Band and Polarization SAR Images Colorization Fusion

The image fusion of multi-band and multi-polarization synthetic aperture radar (SAR) images can improve the efficiency of band and polarization information processing. In this paper, we introduce a fusion method that simultaneously fuses multi-band and polarization SAR images. In the method, we first use non-subsampled shearlet transform (NSST) to fuse multi-band and polarization SAR images. The sub-band images decomposed from the NSST are fused by the coefficient of variation (CV) and phase consistency (PC) weighted fusion rules. Subsequently, we extract the band and polarization difference information from the multi-band and polarization SAR images. The fusion image is finally colorized according to the band and polarization differences. In the experiments, we used Ka and S-band multi-polarization SAR images to test the fusion performance. The experiment results prove that the proposed fused images not only preserve much valuable information but also can be interpreted easily.

Multi-polarization radar backscatter signatures of internal waves at L-band

ABSTRACT In this study, the scattering mechanisms associated to internal waves (IWs) are investigated at L-band. IWs represent key geophysical factors for sea-air heat exchange and play a paramount role in the biological primary production and in the understanding of the evolution of climate ecosystem. In addition, a better understanding of IWs microwave scattering mechanisms can improve the modeling capability and, therefore, can boost the development on advanced synthetic aperture radar (SAR)-based added-value products to mitigate the risk for offshore drilling operations and aquaculture activities associated to IWs. The analysis of L-band multi-polarization SAR scattering of IWs under the influence of surface current straining is performed using a meaningful full-polarimetric Advanced Land Observing Satellite Phased Array type L-band 1 SAR data set collected over IWs observed under different imaging and wind conditions. Time and space co-located ancillary information is also available. Experimental results demonstrate that the non-polarized scattering mechanisms constitute a significant contribution to the total IW backscattering, especially in the case of surface current gradients owing to IWs (about 48–57%). It is also found that the non-polarized scattering contribution associated to IW concentrates along the wave crests, i.e. it is at least 60% larger than the one observed along the wave troughs. In addition, considering the IW traveling directions relative to that of the wind, the non-polarized scattering contribution associated to IWs is more remarkable at upwind direction while it is less significant at down/crosswind directions. The non-polarized scattering mechanisms also calls for a modulation induced by IWs which is much more significant,i.e. at least three times, that the one that characterizes the polarized scattering mechanism.

Large-Scale Orthorectification of GF-3 SAR Images Without Ground Control Points for China’s Land Area

GaoFen-3 (GF-3) is a C-band multipolarization synthetic aperture radar (SAR) satellite with 12 imaging modes. However, its initial positioning accuracy remains unsatisfactory, thereby hindering its use for large-area surveying and mapping. This study proposes a block orthorectification method without ground control points (GCPs) using the GF-3 Fine strip II (FSII) mode. To address the challenges with the accuracy and efficiency of this method, an integrated block orthorectification method was developed to conduct integrated processing of large-scale GF-3 satellite images without GCPs. Geometric calibration was used to improve the absolute positioning accuracy of each SAR image. Then, several tie points (TPs) were extracted using the SAR scale-invariant feature transform (SIFT) operator. A parallel matching strategy was used in the block images registration. The block adjustment model was constructed to solve the orientation parameter of all SAR images. The experimental results of 1,468 GF-3 images of China’s entire land area show a TPs root-mean-square error of 0.724 pixel and 8.014 m for the independent checkpoint, suggesting that the proposed method can effectively improve the geometric accuracy of GF-3 satellite images and demonstrate the feasibility of large-scale SAR mapping without GCPs.

An Introduction to the Chinese High-Resolution Earth Observation System: Gaofen-1~7 Civilian Satellites

The Chinese High-resolution Earth Observation System (CHEOS) program has successfully launched 7 civilian satellites since 2010. These satellites are named by Gaofen (meaning high resolution in Chinese, hereafter noted as GF). To combine the advantages of high temporal and comparably high spatial resolution, diverse sensors are deployed to each satellite. GF-1 and GF-6 carry both high-resolution cameras (2 m resolution panchromatic and 8 m resolution multispectral camera), providing high spatial imaging for land use monitoring; GF-3 is equipped with a C-band multipolarization synthetic aperture radar with a spatial resolution of up to 1 meter, mostly monitoring marine targets; GF-5 carried 6 sensors including hyperspectral camera and directional polarization camera, dedicated to environmental remote sensing and climate research, such as aerosol, clouds, and greenhouse gas monitoring; and GF-7 laser altimeter system payload enables a three-dimensional surveying and mapping of natural resource and land surveying, facilitating the accumulation of basic geographic information. This study provides an overview of GF civilian series satellites, especially their missions, sensors, and applications.

An Unsupervised Classification Method of Multi-Polarization Synthetic Aperture Radar Imagery

In this paper, a clustering method that combines a polarization scattering mechanism with the statistical properties of polarization data is proposed aiming to improve the accuracy of SAR image classification. It is based on the decomposition algorithm of Freeman-Durden scattering model and the non-Gaussian K-Wishart distribution classifier. A modified selective and Markov random field-based adaptive ability classification algorithm can be used especially for complex geomorphological environment conditions. The experimental result shows that the correction classifier iterative algorithm based on the Markov random field are highly useful for the uneven distribution area. It is possible that the method will contribute to reduce the computational complexity of PolSAR classification process and provide higher accuracy.

Improving Landslide Detection on SAR Data Through Deep Learning

In this letter, we use deep learning convolutional neural networks (CNNs) to compare the landslide mapping and classification performances of optical images (from Sentinel-2) and synthetic aperture radar (SAR) images (from Sentinel-1). The training, validation, and test zones used to independently evaluate the performance of the CNN on different datasets are located in the eastern Iburi subprefecture in Hokkaido, where, at 03.08 local time (JST) on September 6, 2018, an Mw 6.6 earthquake triggered about 8000 coseismic landslides. We analyzed the conditions before and after the earthquake exploiting multipolarization SAR as well as optical data by means of a CNN implemented in TensorFlow that points out the locations where the landslide class is predicted as more likely. As expected, the CNN runs on optical images proved itself excellent for the landslide detection task, achieving an overall accuracy of 98.96%, while CNNs based on the combination of ground range detected (GRD) SAR data reached overall accuracies beyond 95%. Our findings show that the integrated use of SAR data may also allow for rapid detection even during storms and under dense cloud cover and provides comparable accuracy to classical optical change detection in landslide recognition and detection.

Seasonal Changes of Glacier Lakes in Tibetan Plateau Revealed by Multipolarization SAR Data

In the context of global warming, monitoring glacial lakes is of great significance for revealing climate change and mitigating glacier lake outburst floods (GLOFs). Multitemporal mapping of glacier lakes through optical satellite remote-sensing imageries can only provide us comparative analysis of glacier lake changes during long time intervals. In recent years, the rapid development of synthetic aperture radar (SAR) has made it possible to observe seasonal changes of glacial lakes. This letter proposed a polarization enhancement-based maximum interclass variance (PE-MIV) method to accurately and automatically mapping the seasonal cycles of glacial lakes based on multipolarization SAR data. Taking the Gongcuo and Langcuo glacial lakes in Laigu village, Tibetan Plateau, as examples, based on 113 Sentinel-1 SAR imageries covering 2017 to 2020 with a 12-day revisiting time, the seasonal boundary changes of both lakes were extracted automatically with a relative accuracy of 93.59%. The seasonal variations of glacial lake area are closely related to temperature and precipitation. This letter demonstrated that the satellite SAR (short revisiting time, multipolarization, etc.) is an effective tool to monitor areal changes of glacier lakes, which can be widely applied in detecting glacier lakes, GLOFs, and their response to climate change.

Bankline detection of GF-3 SAR images based on shearlet.

The GF-3 satellite is China’s first self-developed active imaging C-band multi-polarization synthetic aperture radar (SAR) satellite with complete intellectual property rights, which is widely used in various fields. Among them, the detection and recognition of banklines of GF-3 SAR image has very important application value for map matching, ship navigation, water environment monitoring and other fields. However, due to the coherent imaging mechanism, the GF-3 SAR image has obvious speckle, which affects the interpretation of the image seriously. Based on the excellent multi-scale, directionality and the optimal sparsity of the shearlet, a bankline detection algorithm based on shearlet is proposed. Firstly, we use non-local means filter to preprocess GF-3 SAR image, so as to reduce the interference of speckle on bankline detection. Secondly, shearlet is used to detect the bankline of the image. Finally, morphological processing is used to refine the bankline and further eliminate the false bankline caused by the speckle, so as to obtain the ideal bankline detection results. Experimental results show that the proposed method can effectively overcome the interference of speckle, and can detect the bankline information of GF-3 SAR image completely and smoothly.

SAR data fusion and a novel joint use of neural networks for coastline extraction

ABSTRACT In this paper, a novel automated coastline extraction method from SAR (Synthetic Aperture Radar) data is presented. The method is designed to exploit radar backscatter coefficients () from multipolarization SAR acquisitions (the 4 classic co- and cross-polarized polarizations), whereas single-pol data are employed in the majority of methods in this field, implementing data fusion through the use of an autoencoder neural network and producing the coastline by harnessing a Pulse-Coupled Neural Network (PCNN). Main results are presented throughout the paper, demonstrating superiority and comparability with established methods and with a recent automated algorithm that can be considered among the state-of-the art techniques in this field; furthermore, effectiveness of data fusion and segmentation obtained through the mentioned neural networks has been compared to that of several combinations of the same networks with different frameworks: a different data fusion framework, obtained through the use of linear Principal Component Analysis (PCA), and a different binarization framework, based on the use of Expectation-Maximization (EM) image segmentation. Main achievements of presented technique consist in enabling a possible faster processing as well as the opportunity of operating with an improved fused information content on coastline, together with very high accuracy results.

A novel crop classification method based on ppfSVM classifier with time-series alignment kernel from dual-polarization SAR datasets

Rapid and accurate crop type mapping is of great significance for agricultural management and sustainable development. Time-series multi-polarization synthetic aperture radar (SAR) data is suitable for obtaining the large-scale distribution of crop types and continuously monitoring crops. At present, the classification method based on the time-series alignment of time-varying feature curves has been widely used, which can take the uncertainty of the phenological cycles of crops into account. The most classical method is the nearest neighbor (NN) classifier based on the dynamic time warping (DTW) alignment. While the DTW alignment does not consider the local shape of the curves and temporal ranges, and the NN classifier is inadequate in generalization, which restricts the accuracy of crop type mapping. In this paper, a pairwise proximity function support vector machine (ppfSVM) classification method with the time-weighted shapeDTW (TWshapeDTW) alignment is proposed. Firstly, the novel alignment method simultaneously considers the local shape of the curve and the temporal range of crops. Besides, the novel ppfSVM classifier with the time-series alignment kernel is established. Such kernel matrix considers dual-similarity metrics of multiple features, and it is positive semi-definite (PSD) in this classifier. With 42 Sentinel-1 dual-polarization SAR images located in Gansu Province, China, the crop classification maps in 2018 and 2019 are generated respectively. The proposed method in this paper obtains the overall accuracies of more than 90% in both two years, and the changes of crop types from 2018 to 2019 are also in line with the actual crop rotation. Compared with traditional classification methods (SVM and the NN method with the DTW alignment), it is found that the proposed method has higher overall accuracy (OA) and better robustness in the case of small number of samples. The OA's improvements of our method compared with the SVM method are 3% and 1% in 2018 and 2019, respectively. Such improvements are 14% and 12% respect to the NN method with the DTW alignment. This method can achieve more obvious improvement under the condition of less training samples and unaligned phenological sequences. Furthermore, the novel TWshapeDTW alignment is superior to the DTW and the time-weighted DTW alignment under the ppfSVM classifier. The OA's improvement introduced by the TWshapeDTW alignment respect to the DTW alignment can obtain 5% and 4% in 2018 and 2019, respectively.

Coastal Wetland Classification with GF-3 Polarimetric SAR Imagery by Using Object-Oriented Random Forest Algorithm.

When the use of optical images is not practical due to cloud cover, Synthetic Aperture Radar (SAR) imagery is a preferred alternative for monitoring coastal wetlands because it is unaffected by weather conditions. Polarimetric SAR (PolSAR) enables the detection of different backscattering mechanisms and thus has potential applications in land cover classification. Gaofen-3 (GF-3) is the first Chinese civilian satellite with multi-polarized C-band SAR imaging capability. Coastal wetland classification with GF-3 polarimetric SAR imagery has attracted increased attention in recent years, but it remains challenging. The aim of this study was to classify land cover in coastal wetlands using an object-oriented random forest algorithm on the basis of GF-3 polarimetric SAR imagery. First, a set of 16 commonly used SAR features was extracted. Second, the importance of each SAR feature was calculated, and the optimal polarimetric features were selected for wetland classification by combining random forest (RF) with sequential backward selection (SBS). Finally, the proposed algorithm was utilized to classify different land cover types in the Yancheng Coastal Wetlands. The results show that the most important parameters for wetland classification in this study were Shannon entropy, Span and orientation randomness, combined with features derived from Yamaguchi decomposition, namely, volume scattering, double scattering, surface scattering and helix scattering. When the object-oriented RF classification approach was used with the optimal feature combination, different land cover types in the study area were classified, with an overall accuracy of up to 92%.

High-resolution sea surface wind speeds of Super Typhoon Lekima (2019) retrieved by Gaofen-3 SAR

Gaofen-3 (GF-3) is the first Chinese space-borne multi-polarization synthetic aperture radar (SAR) instrument at C-band (5.43 GHz). In this paper, we use data collected from GF-3 to observe Super Typhoon Lekima (2019) in the East China Sea. Using a VH-polarized wide ScanSAR (WSC) image, ocean surface wind speeds at 100m horizontal resolution are obtained at 21:56:59 UTC on 8 August 2019, with the maximum wind speed, 38.9 m·s−1. Validating the SAR-retrieved winds with buoy-measured wind speeds, we find that the root mean square error (RMSE) is 1.86 m·s−1, and correlation coefficient, 0.92. This suggests that wind speeds retrieved from GF-3 SAR are reliable. Both the European Centre for Medium-Range Weather Forecasts (ECMWF) fine grid operational forecast products with spatial resolution, and China Global/Regional Assimilation and Prediction Enhance System (GRAPES) have good performances on surface wind prediction under weak wind speed condition ( 24 m·s−1). With respect to SAR-retrieved wind speeds, the RMSEs are 5.24 m·s−1 for ECMWF and 5.17 m·s−1 for GRAPES, with biases of 4.16 m·s−1 for ECMWF and 3.84 m·s−1 for GRAPES during Super Typhoon Lekima (2019).

A Classification Scheme for Sediments and Habitats on Exposed Intertidal Flats with Multi-Frequency Polarimetric SAR

We developed an extension of a previously proposed classification scheme that is based upon Freeman–Durden and Cloude–Pottier decompositions of polarimetric Synthetic Aperture Radar (SAR) data, along with a Double-Bounce Eigenvalue Relative Difference (DERD) parameter, and a Random Forest (RF) classifier. The extension was done, firstly, by using dual-copolarization SAR data acquired at shorter wavelengths (C- and X-band, in addition to the previously used L-band) and, secondly, by adding indicators derived from the (polarimetric) Kennaugh elements. The performance of the newly developed classification scheme, herein abbreviated as FCDK-RF, was tested using SAR data of exposed intertidal flats. We demonstrate that the FCDK-RF scheme is capable of distinguishing between different sediment types, namely mud and sand, at high spatial accuracies. Moreover, the classification scheme shows good potential in the detection of bivalve beds on the exposed flats. Our results show that the developed FCDK-RF scheme can be applied for the mapping of sediments and habitats in the Wadden Sea on the German North Sea coast using multi-frequency and multi-polarization SAR from ALOS-2 (L-band), Radarsat-2 (C-band) and TerraSAR-X (X-band).

Performance evaluation of multi-parametric synthetic aperture radar data for geological lineament extraction

ABSTRACT The demarcation of linear geomorphological features due to fracture systems, underlying or exposed faults, drainage lines etc. from satellite images has been one of the most widely used applications in geological studies. Synthetic Aperture Radar (SAR) data has been recognized as an effective tool for lineament extraction due to its unique sensitivity to subtle topography, soil moisture and texture. However, the typical SAR sensor parameters such as frequency, polarization and look direction plays a vital role in enhancing the performance for a specific application. Therefore, the study aims to analyse the multi-frequency (C- and L-band), multi-polarization (like and cross) and multi-look direction SAR data for geological lineaments extraction. In this paper, Sentinel-1 and Advanced Land Observation Satellite (ALOS)-2 Phased Array type L-band Synthetic Aperture Radar (PALSAR)-2 datasets acquired over a part of the Indo-Burma ranges of Manipur region (northeastern part of India) are considered for analysis. The lineaments extracted from SAR datasets are compared with reference to lineaments provided by the Geological Survey of India (GSI) through their portal. The study observes that the like-polarization C-band SAR data provide optimal results for this application. It is also observed that the integration of lineaments extracted from multi-look SAR data can significantly improve the performance than that derived from single-look data. Overall, the study also focuses on the spatial distribution of lineaments with respect to the local structural regime for a better understanding of the regional tectonic setup.

Wetland Monitoring Using SAR Data: A Meta-Analysis and Comprehensive Review

Despite providing vital ecosystem services, wetlands are increasingly threatened across the globe by both anthropogenic activities and natural processes. Synthetic aperture radar (SAR) has emerged as a promising tool for rapid and accurate monitoring of wetland extent and type. By acquiring information on the roughness and moisture content of the surface, SAR offers unique potential for wetland monitoring. However, there are still challenges in applying SAR for mapping complex wetland environments. The backscattering similarity of different wetland classes is one of the challenges. Choosing the appropriate SAR specifications (incidence angle, frequency and polarization), based on the wetland type, is also a subject of debate and should be investigated more thoroughly. The geometric distortion of SAR imagery and loss of coherency are other remaining challenges in applying SAR and its processing techniques for wetland studies. Hence, this study provides a systematic meta-analysis based on compilation and analysis of indexed research studies that used SAR for wetland monitoring. This meta-analysis reviewed 172 papers and documented an upward trend in usage of SAR data, increasing usage of multi-sensor data, increasing integration of C- and L- bands over other configurations and higher classification accuracy with multi-frequency and multi-polarized SAR data. The highest number of wetland research studies using SAR data came from the USA, Canada and China. This meta-analysis highlighted the current challenges and solutions for wetland monitoring using SAR sensors.

Road Detection Based on Shearlet for GF-3 Synthetic Aperture Radar Images

GF-3 satellite is China’s first C-band multi-polarized synthetic aperture radar (SAR) satellite with the 1-meter resolution, which has been widely used in various fields. Road detection for GF-3 SAR images is an important part of the application of GF-3, especially in fields of map update, target recognition, and image matching. However, speckle appears in GF-3 SAR images due to coherent imaging system and it hinders the interpretation of images seriously. Especially the detection of weak roads under strong speckle background becomes extremely difficult. As a representative of multiscale geometric analysis (MGA) tool, shearlet has the optimal sparse representation feature and strong directional orientation, which can effectively capture edge and other anisotropic feature information, and can accurately describe the sparse characteristics of GF-3 SAR images. Based on shearlet, a method for detecting weak roads under strong speckle interference is proposed. Firstly, the Frost filter is used for despeckling. Secondly, shearlet is used for road detection. Finally, morphological operations are adopted to obtain the final result. Road detection experiments on various types of GF-3 SAR images demonstrate that, the proposed method can effectively overcome the interference of speckle, and completely and smoothly detect road information, which is very suitable for the detection of weak roads under strong speckle interference of GF-3 SAR images.