Synthetic Aperture Radar Model 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 σ 0 w b was calculated using a theoretical approach that employs the VV-polarized calibrated NRCS σ 0 v v 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 ( σ 0 w b / σ 0 v v ) 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.

A Physically Interpretable Rice Field Extraction Model for PolSAR Imagery

Reliable and timely rice distribution information is of great value for real-time, quantitative, and localized control of rice production information. Synthetic aperture radar (SAR) has all-weather and all-day observation capability to monitor rice distribution in tropical and subtropical areas. To improve the physical interpretability and spatial interpretability of the deep learning model for SAR rice field extraction, a new SHapley Additive exPlanation (SHAP) value-guided explanation model (SGEM) for polarimetric SAR (PolSAR) data was proposed. First, a rice sample set was produced based on field survey and optical data, and the physical characteristics were extracted using decomposition of polarimetric scattering. Then a SHAP-based Physical Feature Interpretable Module (SPFIM) combing the long short-term memory (LSTM) model and SHAP values was designed to analyze the importance of physical characteristics, a credible physical interpretation associated with rice phenology was provided, and the weight of physical interpretation was combined with the weight of original PolSAR data. Moreover, a SHAP-guided spatial interpretation network (SSEN) was constructed to internalize the spatial interpretation values into the network layer to optimize the spatial refinement of the extraction results. Shanwei City, Guangdong Province, China, was chosen as the study area. The experimental results showed that the physical explanation provided by the proposed method had a high correlation with the rice phenology, and spatial self-interpretation for finer extraction results. The overall accuracy of the rice mapping results was 95.73%, and the kappa coefficient reached 0.9143. The proposed method has a high interpretability and practical value compared with other methods.

Denoising convolution algorithms and applications to SAR signal processing

Convolutions are one of the most important operations in signal processing. They often involve large arrays and require significant computing time. Moreover, in practice, the signal data to be processed by convolution may be corrupted by noise. In this paper, we introduce a new method for computing the convolutions in the quantized tensor train (QTT) format and removing noise from data using the QTT decomposition. We demonstrate the performance of our method using a common mathematical model for synthetic aperture radar (SAR) processing that involves a sinc kernel and present the entire cost of decomposing the original data array, computing the convolutions, and then reformatting the data back into full arrays.

An Image-Domain Signal Model for Azimuth Multichannel Reconstruction and Its Applications

High-resolution wide-swath (HRWS) synthetic aperture radar (SAR) may benefit from reconstructing signal in image domain, for taking local characteristics of the scene into account. An image-domain signal model for HRWS SAR to reconstruct non-ambiguous image is presented in this paper. This model makes the idea of controlling the regional reconstruction performance and taking the advantage of nonuniform scatter distribution possible. Resolution is also preserved perfectly by the proposed model despite of whatever tradeoff is made between azimuth-ambiguity-to-signal-ratio (AASR) and signal-to-noise-ratio (SNR). The model is derived on back projection (BP) image and can deal with different squint angles and beam steering strategies thanks to BP algorithm. Although the reconstruction is done pixel by pixel, computational burden, which depends on beam steering and the specific constraints in reconstruction, may not severely increase. Two methods for Spotlight SAR and two methods for Stripmap SAR are also presented based on the proposed model to verify the model and demonstrate the potential of it. Simulations on both point target and extended target with different squint angles are carried out to verify the proposed methods, which also verify the proposed model indirectly.

Total Stem Biomass Estimation Using Sentinel-1 and -2 Data in a Dense Coniferous Forest of Complex Structure and Terrain

Accurate above-ground biomass (AGB) estimation across multiple spatial and temporal scales is essential for mitigating climate change and optimizing forest management strategies. The aim of the present study was to investigate the potential of Sentinel optical and Synthetic Aperture Radar (SAR) data in reliably estimating the plot-level total stem biomass (TSB), which constitutes the dominant material among the different tree components of AGB (stem, branches, and leaves). The study area was located in a dense coniferous forest characterized by an uneven-aged structure and intense topography. A random forest (RF) regression analysis was performed to develop TSB predictive models using Sentinel-1 and -2 images in an individual and combined manner. Consequently, three RF models were produced and evaluated for their predictive performance through the k-fold cross-validation (CV) method. The results showcased that the individual use of Sentinel-1 contributed to the production of the most accurate plot-level TSB estimates (i.e., coefficient of determination-R2 = 0.74, relative mean square error (RMSE) = 1.76 Mg/1000 m2, mean absolute error (MAE) = 1.48 Mg/1000 m2), compared to the use of Sentinel-2 data individually and the Sentinel-1 and -2 combination. In fact, the synergistic use of optical and SAR data led to the generation of an RF model that only marginally underperformed the SAR model (R2 = 0.73 and R2 = 0.72, respectively).

Research on Configuration Constraints of Airborne Bistatic SARs.

Based on the analysis of the airborne bistatic synthetic aperture radar (SAR) imaging geometric mode, an extended nonlinear chirp scaling algorithm is employed to simulate and verify the imaging effect of the bistatic SARs. A gradient theory-based two-dimensional resolution bistatic SAR model is proposed, and the constraints of the multi-platform flight trajectory parameters meeting the imaging accuracy of the bistatic SAR are analyzed. Finally, through the bistatic SAR imaging simulation of cooperative flight trajectories under various situations, the spatial configuration constraint envelope between the flight vehicles to achieve the optimal resolution is revealed. The results of this paper will provide a theoretical reference for the SAR application in formation flight control.

Monitoring peatland water table depth with optical and radar satellite imagery

Peatland water table depth (WTD) and wetness have widely been monitored with optical and synthetic aperture radar (SAR) remote sensing but there is a lack of studies that have used multi-sensor data, i.e., combination of optical and SAR data. We assessed how well WTD can be monitored with remote sensing data, whether multi-sensor approach boosts explanatory capacity and whether there are differences in regression performance between data and peatland types. Our data consisted of continuous multiannual WTD data from altogether 50 restored and undrained Finnish peatlands, and optical (Landsat 5–8, Sentinel-2) and Sentinel-1 C-band SAR data processed in Google Earth Engine. We calculated random forest regressions with dependent variable being WTD and independent variables consisting of 21 optical and 10 SAR metrics. The average regression performance was moderate in multi-sensor models (R 2 43.1%, nRMSE 19.8%), almost as high in optical models (R 2 42.4%, nRMSE 19.9%) but considerably lower in C-band SAR models (R 2 21.8%, nRMSE 23.4%) trained separately for each site. When the models included data from several sites but were trained separately for six habitat type and management option combinations, the average R 2 was 40.6% for the multi-sensor models, 36.6% for optical models and 33.7% for C-band SAR models. There was considerable site-specific variation in the model performance (R 2 −3.3–88.8% in the multi-sensor models ran separately for each site) and whether multi-sensor, optical or C-band SAR model performed best. The average regression performance was higher for undrained than for restored peatlands, and higher for open and sparsely treed than for densely treed peatlands. The most important variables included SWIR-based optical metrics and VV SAR backscatter. Our results suggest that optical data works usually better than does C-band SAR data in peatland WTD monitoring and multi-sensor approach increases explanatory capacity moderately little.

Ice Detection with Sentinel-1 SAR Backscatter Threshold in Long Sections of Temperate Climate Rivers

Climate change leads to more variable meteorological conditions. In many Northern Hemisphere temperate regions, cold seasons have become more variable and unpredictable, necessitating frequent river ice observations over long sections of rivers. Satellite SAR (Synthetic Aperture Radar)-based river ice detection models have been successfully applied and tested, but different hydrological, morphological and climatological conditions can affect their skill. In this study, we developed and tested Sentinel-1 SAR-based ice detection models in 525 km sections of the Nemunas and Neris Rivers. We analyzed three binary classification models based on VV, VH backscatter and logistic regression. The model sensitivity and specificity were used to determine the optimal threshold between ice and water classes. We used in situ observations and Sentinel-2 Sen2Cor ice mask to validate models in different ice conditions. In most cases, SAR-based ice detection models outperformed Sen2Cor classification because Sen2Cor misclassified pixels as ice in areas with translucent clouds, undetected by the scene classification algorithm, and misclassified pixels as water in cloud or river valley shadow. SAR models were less accurate in river sections where river flow and ice formation conditions were affected by large valley-dammed reservoirs. Sen2Cor and SAR models accurately detected border and consolidated ice but were less accurate in moving ice conditions. The skill of models depended on how dense the moving ice was. With a lowered classification threshold and increased model sensitivity, SAR models detected sparse frazil ice. In most cases, the VV polarization-based model was more accurate than the VH polarization-based model. The results of logistic and VV models were highly correlated, and the use of VV was more constructive due to its simpler algorithm.

Facet-Based Hybrid Method for Electromagnetic Scattering From Shallow Water Waves Modulated by Submarine Topography

This article presents a mechanism model of synthetic aperture radar (SAR) remote sensing imaging of submarine topography in shallow areas. According to the shallow water effect and complex geographical factors, a 3-D geometric model of multiscale wave is generated by combining sea spectrum separation and phase superposition methods. It has the advantage of reflecting the random fluctuation characteristics of waves and the refraction phenomenon influenced by submarine topography. In addition, the modulation relationship among submarine topography, tidal current, and sea surface microroughness is established. This constitutes hydrodynamic modulation. The facet-based hybrid method that combines the geometry optics (GO) and the integral equation method (IEM) is adopted to solve the electromagnetic (EM) scattering of super electrically large sea surface. This contains tilt modulation. The effectiveness of the proposed model is demonstrated through the comparisons with the measured results. The influence of the parameters of radar, sea state, water depth, and tidal current direction on the scattering intensity of sea surface is studied as well. It is revealed that the shallower the water depth, the more significant the hydrodynamic and tilt effect. In addition, the velocity bunching (VB) modulation is a special modulation method in SAR imaging. According to the simulated results, the contributions of tilt, hydrodynamic, and VB modulation in SAR imaging mechanism are compared. The VB is the main factor, hydrodynamic is second, and tilt is the least.

Location of Synthetic Aperture Radar Imagery via Range History

Geolocation is one of the key synthetic aperture radar (SAR) image processing procedures in SAR-based remote sensing applications. The conventional SAR imagery geolocation model is based on the range-Doppler (RD) equation, which is derived from the Fresnel approximation of SAR. Currently, there is no other rigorous geolocation model for modern high-resolution SAR; however, this approximation may not be fully applicable for various modern SAR imaging modes and algorithms. This study aims to develop a novel SAR image geolocation model based on the SAR range history (RH) equation, which is derived from the basic SAR backscattered signal property. Moreover, a new geometric calibration model is presented for RH-based location. Experiments based on real spaceborne SAR data are presented as examples, and the location accuracy of RH and RD location models are compared. The results show that the location accuracies of the RH and RD models are consistent under the existing SAR processing framework, but those for the RH model demonstrate better performance in the azimuth direction. In theory, the proposed RH model can be used for SAR image products generated from complex modes and non-Fresnel-approximation-based imaging algorithms, making it suitable for future SAR systems.

Multi-Electromagnetic Jamming Countermeasure for Airborne SAR Based on Maximum SNR Blind Source Separation

Synthetic aperture radar (SAR) may be attacked by multifarious kinds of the active-jamming, which will lead to the ineffectiveness of SAR in complex electromagnetic environment. In this paper, a novel multi-electromagnetic jamming counter-measure for the airborne SAR is proposed based on maximum signal-to-noise ratio (SNR) blind source separation. Firstly, the imaging geometry and multi-component mixed signal model of airborne SAR are established. Then, based on multi-component mixed signal matrix, a blind source separation (BSS) method based on the maximum SNR is proposed to separate the real target echo signal from the multi-electromagnetic jamming signals. After real target echo signal identification, the high resolution images of the interested target area can be achieved by the corresponding SAR imaging method. The simulated and measured data results are present to prove the feasibility and effectiveness of the proposed method.

CryoSat-2 Significant Wave Height in Polar Oceans Derived Using a Semi-Analytical Model of Synthetic Aperture Radar 2011–2019

This paper documents the retrieval of significant ocean surface wave heights in the Arctic Ocean from CryoSat-2 data. We use a semi-analytical model for an idealised synthetic aperture satellite radar or pulse-limited radar altimeter echo power. We develop a processing methodology that specifically considers both the Synthetic Aperture and Pulse Limited modes of the radar that change close to the sea ice edge within the Arctic Ocean. All CryoSat-2 echoes to date were matched by our idealised echo revealing wave heights over the period 2011–2019. Our retrieved data were contrasted to existing processing of CryoSat-2 data and wave model data, showing the improved fidelity and accuracy of the semi-analytical echo power model and the newly developed processing methods. We contrasted our data to in situ wave buoy measurements, showing improved data retrievals in seasonal sea ice covered seas. We have shown the importance of directly considering the correct satellite mode of operation in the Arctic Ocean where SAR is the dominant operating mode. Our new data are of specific use for wave model validation close to the sea ice edge and is available at the link in the data availability statement.

Geolocation Error Transfer Model and Trajectory Calibration Method for Airborne SAR Considering Motion Compensation Residual Error

Airborne Synthetic Aperture Radar (SAR) location error is affected by the position/speed measurement error of the aircraft, system time error, etc., and also related to the residual error of motion compensation. However, the existing airborne SAR location model rarely considers the effect of residual motion error. Considering that motion and trajectory measurement errors are common in practice, this paper derives a location error transfer model of an airborne SAR image based on the motion compensation and frequency-domain imaging algorithms. The proposed model clarifies the influence of trajectory measurement error on location deviation when residual motion error exists and provides a method of error calibration measurement. The simulation experiments validate the correctness of the proposed location error transfer model. The present method obtains a more accurate error calibration measurement result than the location error model that does not consider the residual motion error, proving the superiority of the proposed model.

Multimodal remote sensing benchmark datasets for land cover classification with a shared and specific feature learning model

As remote sensing (RS) data obtained from different sensors become available largely and openly, multimodal data processing and analysis techniques have been garnering increasing interest in the RS and geoscience community. However, due to the gap between different modalities in terms of imaging sensors, resolutions, and contents, embedding their complementary information into a consistent, compact, accurate, and discriminative representation, to a great extent, remains challenging. To this end, we propose a shared and specific feature learning (S2FL) model. S2FL is capable of decomposing multimodal RS data into modality-shared and modality-specific components, enabling the information blending of multi-modalities more effectively, particularly for heterogeneous data sources. Moreover, to better assess multimodal baselines and the newly-proposed S2FL model, three multimodal RS benchmark datasets, i.e., Houston2013 – hyperspectral and multispectral data, Berlin – hyperspectral and synthetic aperture radar (SAR) data, Augsburg – hyperspectral, SAR, and digital surface model (DSM) data, are released and used for land cover classification. Extensive experiments conducted on the three datasets demonstrate the superiority and advancement of our S2FL model in the task of land cover classification in comparison with previously-proposed state-of-the-art baselines. Furthermore, the baseline codes and datasets used in this paper will be made available freely at https://github.com/danfenghong/ISPRS_S2FL.

Predicting paddy yield at spatial scale using optical and Synthetic Aperture Radar (SAR) based satellite data in conjunction with field-based Crop Cutting Experiment (CCE) data

ABSTRACT The accurate, reliable, and robust information on crop yield has great importance in food security measures. In this study, both optical (Moderate Resolution Imaging Spectroradiometer (MODIS)-derived Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI)) and Synthetic Aperture Radar (SAR) (Sentinel-1A) satellite datasets were used for predicting paddy yield at a spatial scale in conjunction with Crop Cutting Experiment (CCE) data in Sahibganj district of the Jharkhand state (India) during the monsoon season 2017. The yield prediction models were developed from linear (LR) and multiple regression (MR) analysis. The AquaCrop model was also employed to simulate the paddy yield. The key findings showed that the MR-based yield model developed from SAR (Vertical transmission and Vertical reception (VV) + Vertical transmission and Horizontal reception (VH) polarizations) data was more accurate, reasonable, and reliable as compared to the optical-based (NDVI + EVI) MR yield model. The SAR-based MR yield model showed better accuracy between predicted and observed yield (CCE) as evaluated using Nash-Sutcliffe Efficiency (NSE = 0.68). However, the optical-based MR yield model showed relatively lower accuracy (NSE = 0.62). The relative deviation (RD) of the predicted yield from the SAR and optical-based MR model was nearly 3% and 4%, respectively. Using the AquaCrop model, the simulated yield was underestimated by approximately 4%. We conclude that the SAR-based MR-yield model outperformed the optical-based model to predict paddy yield at a spatial scale, and the adopted methodology can beneficial for decision-makers withing agriculture monitoring. Hence, satellite imagery has always been a reliable source for yield forecasting and crop assessment at a regional and national scale.

Combined Sentinel-1A With Sentinel-2A to Estimate Soil Moisture in Farmland

In this article, seven filter algorithms were compared. The Lee sigma method was more suitable for estimating soil moisture (SM) than the other filtering methods under different land cover types. First, we used a combination of roughness and the dual-polarized Sentinel-1A backscattering coefficients (VV and VH) to estimate SM in bare soil areas. Second, we employed water cloud model (WCM) to remove the influence of vegetation signals on the land surface backscattering and estimate SM in vegetation-covered areas. SM was also retrieved by modified soil moisture monitoring index (MSMMI) and modified perpendicular drought index (MPDI) of Sentinel-2A images. The results show that MSMMI can more accurately monitor SM in bare soil areas, which was slightly better than synthetic aperture radar (SAR) results. The SAR backscattering coefficients after the removal of vegetation influence by WCM can more precisely estimate SM in vegetation-covered areas, which is significantly better than MSMMI and MPDI, especially in high vegetation-covered areas. Optics and SAR differ in their abilities to estimate SM under different land cover, but the powerful fitting ability of machine learning can make full use of their advantages. We employed the generalized regression neural network (GRNN), support vector regression (SVR), random forest regression (RFR), and deep neural network (DNN) algorithms to estimate SM combining Sentinel-1A with Sentinel-2A images. The estimation accuracies of SM by regression algorithms were higher than those by the semiempirical SAR and optical models. The accuracy of estimated SM by DNN was higher than that of GRNN and RFR, which were better than SVR.

Monitoring of flood water propagation based on microwave and optical imagery

The economy of northern part of Bangladesh mainly depends upon agriculture based production. Flood disasters in Dinajpur along the Punarbhaba, Atrai Rivers result in significant loss of life and economic damage. This paper finds the optimum technique to use microwave (Sentinel-1A) and optical (Landsat-8) imagery alternatively along with GIS for fast and effective flood mapping, semi-real-time monitoring and analyzing the propagation of flood water in a flood prone area. Since flooded areas usually are covered by dense clouds, Synthetic Aperture Radar (SAR) has the advantage over optical data by operating at wavelengths not hampered by cloud cover or a lack of illumination. It has capability to continuous observation of flood events for producing rapid, accurate and cost effective flood mapping. This characteristic makes SAR a potential alternative to optical sensors. The focal steps in flood monitoring using optical satellite data are: identifying water bodies efficiently; disregarding some cloud influences; and, monitoring the flood process dynamically. The purpose of this study is to evaluate the effectiveness of using freely available time series of Copernicus Sentinel-1 SAR and Landsat-8 imagery. This study also assess the TRMM data for precipitation measurement in the study area for hydrological analyses of watersheds. The SAR images were first calibrated, geometrically corrected and filtered. Afterward, to extract the inundated areas from the SAR images threshold method was applied. In threshold method and MNDWI index was used to separate the open water and nonwater (land) areas from the images. This research proposed the combination model of SAR and optical data that describes how we can get water extraction result with higher accuracy during flood using optical and microwave data. The results showed that the VH of SAR backscattering coefficient achieved higher accuracy than VV of SAR backscattering coefficient, and the combination model provides the highest accuracy of flood water extraction. The highest number of populations are affected by flood that is around 827822 in 2017. Therefore, the findings of this study will help to require initiative to scale back the flood hazard impact and increase the pliability within the process of flood management. This study proposed a method to automatically generate periodic surface water at 10 m resolution, which may contribute to monitoring surface water in a timely manner. This has also played a significant role in the monitoring and early warning of emerging major natural disasters at home and abroad.

SAR analysis of wetland ecosystems: Effects of band frequency, polarization mode and acquisition dates

The availability of high spatial resolution synthetic aperture radar (SAR) sensors with a wide range of acquisition modes has increased greatly over the past decade and contributed significantly to the study of wetland ecosystems. However, the relative influence of acquisition configurations (i.e. band frequency, polarization mode, number of acquisition dates) in wetland analysis remains poorly explored. This article investigated the relative influence of X-/C-band frequency, dual-/quad-polarization and single-/multi-acquisition features on discrimination of vegetation types in 632 ha Ramsar-protected temperate riverine marshes (Mont-Saint-Michel Bay, France). Three SAR datasets (i.e. quad-pol/C-band, dual-pol/C-band and dual-pol/X-band) were generated from five pairs of TerraSAR-X and RADARSAT-2 images. First, a set of 25 SAR features, including backscattering coefficients and polarimetric parameters, was extracted from the SAR datasets. Second, correlation between each pair of images was calculated using the polarimetric parameter Shannon entropy to select the most similar pairs in the time series. Third, the importance of each SAR feature and modeling accuracy were calculated using a conditional random forest model for each of the three datasets. Finally, analysis of variance was performed to assess the impact of band frequency, polarization mode and number of acquisition dates on the classification of vegetation types. The results highlighted that although the time-shift of each pair of TerraSAR-X and RADARSAT-2 images was short (3–11 days), only three pairs were sufficiently similar, highlighting the high variability in wetland ecosystems. The polarimetric parameter Shannon entropy was the most discriminating feature, regardless of the frequency or polarization. Most variance in the model accuracy was explained by the number of acquisition dates (68%), followed by the frequency (23%), while polarization explained little. This article will help select the most suitable SAR sensor acquisition modes for wetland conservation.

Landslide detection in mountainous forest areas using polarimetry and interferometric coherence

The cloud-free, wide-swath, day-and-night observation capability of synthetic aperture radar (SAR) has an important role in rapid landslide monitoring to reduce economic and human losses. Although interferometric SAR (InSAR) analysis is widely used to monitor landslides, it is difficult to use that for rapid landslide detection in mountainous forest areas because of significant decorrelation. We combined polarimetric SAR (PolSAR), InSAR, and digital elevation model (DEM) analysis to detect landslides induced by the July 2017 Heavy Rain in Northern Kyushu and by the 2018 Hokkaido Eastern Iburi Earthquake. This study uses fully polarimetric L-band SAR data from the ALOS-2 PALSAR-2 satellite. The simple thresholding of polarimetric parameters (alpha angle and Pauli components) was found to be effective. The study also found that supervised classification using PolSAR, InSAR, and DEM parameters provided high accuracy, although this method should be used carefully because its accuracy depends on the geological characteristics of the training data. Regarding polarimetric configurations, at least dual-polarimetry (e.g., HH and HV) is required for landslide detection, and quad-polarimetry is recommended. These results demonstrate the feasibility of rapid landslide detection using L-band SAR images.

An Antideceptive Jamming Method for Multistatic Synthetic Aperture Radar Based on Collaborative Localization and Spatial Suppression

In recent years, the research of multistatic synthetic aperture radar (SAR) has become a hot spot because of its great potentials in military and civil applications. However, it is inevitably disturbed by electronic countermeasures. In the multistatic SAR image, the false target caused by transmitting deceptive jamming leads to misjudgment of the image information. In this article, a new antideceptive jamming method for multistatic SAR is proposed. This method can locate the deceptive jammer and effectively suppress it. First of all, the echo model of multistatic SAR in the jamming environment is set up. Based on this model, the relationship expression for the location of jammer is derived. Then, we use the maximally stable extremal region method and the Euclidean-distance-based method to detect and identify false target in the multistatic SAR image. By using the location information of false target, receivers, and transmitters, the expression of the jammer localization is solved to locate the jammer. Finally, the minimum variance distortionless response beamforming method and image mosaic method are used to effectively suppress the jamming. The simulation results show that the method is effective.