Synthetic Aperture Radar Processing Research Articles

On the RFI Detection in Differential Interferometric Synthetic Aperture Radar

Abstract. Synthetic Aperture Radar (SAR) can image the ground with a wide area and high resolution at all times and in all weather and has become an important means of remote sensing. Differential interferometry SAR technology can obtain high-precision surface deformation information by processing more than two SAR images before and after deformation. In recent years, it has attracted widespread attention and research. However, due to the increasing number of ground electronic devices, ground radio frequency interference (RFI) has become one of the main problems in differential interferometry SAR processing, seriously affecting the performance of differential interferometry SAR imaging and differential interferometry surface deformation monitoring applications. In this paper, a clutter cancellation interference enhancement detection algorithm is proposed. By clutter suppression in the primary and secondary images, the interference-to-signal ratio is increased, which effectively improves the interference detection capabilities. The effectiveness of the algorithm in this paper is verified by the on-orbit measured data of the Lutan-1 satellites.

Mapping Erosion Hotspots: Coherent Change Detection in the Quilpie Region, Queensland, Australia

Erosion is a powerful force that has moulded the Earth ever since water has been present on its rocky surface. In its seemingly harmless pursuit, erosion threatens ecosystems, reduces agricultural production, and impacts water quality. When trying to investigate erosion, there is no easy way to identify hotspots, only leaving the possibility of predicting where erosion should be occurring. This study aimed to develop a method to identify erosion using Synthetic Aperture Radar (SAR) images in a process called Coherent Change Detection (CCD). In doing so, it was found that CCD can be used to identify erosion due to rain events; however, false positives were also found due to soil moisture changes. This study used a new method for removing soil moisture effects that utilised the drying out of the soil to map where changes had occurred. This helped limit false positives, but more work is required to ensure soil moisture does not interfere with the results. Field data comprising aerial imagery and soil sampling were collected to improve the SAR processing as well as validate the results. The results of this study indicate the feasibility of developing an erosion analysis system capable of providing near real-time data specifically for arid regions.

Persistent scatterer interferometry (PSI) technique for road infrastructure monitoring: a case study of the roadway network of the Nile Delta (Egypt)

A dependable transportation infrastructure is an essential local resource, linking communities and facilitating economic expansion. The Nile Delta, renowned for its high population density, is experiencing subsidence as a result of natural forces exerting pressure on its sediments. Land subsidence exerts a substantial influence on infrastructure, economic, and social progress. This study examines the evaluation of land subsidence in roads experiencing various issues in the Nile Delta. Each year, subsidence results in significant damage and, in certain instances, complete disruption of numerous road networks, with potential durations ranging from a few days to several months. Conventional methods for identifying and monitoring subsidence on a large and intricate road network can be challenging due to the substantial time and resource requirements. Satellite-based monitoring is essential as it can effectively detect ground displacements caused by human activities (such as subterranean diggings and disruption of other infrastructures) or environmental dangers, specifically those associated with crucial hydrogeological incidents. Moreover, it possesses the benefit of efficiently encompassing extensive regions with precision. This study utilized the persistent scatterer interferometry (PSI) of SNAP-StaMPS workflow to employ differential interferometry synthetic aperture radar (DInSAR) processing methods for investigating the area. By utilizing Sentinel-1 data, we successfully acquired a comprehensive understanding of the deformation occurring in the study area, specifically in the roads where subsidence is persistently taking place. The data has demonstrated its value in enhancing our understanding of the safety status of infrastructure and facilitating maintenance actions. The accuracy of the radar interferometry results was confirmed through the use of in situ methods, including Global Navigation Satellite System (GNSS), Global Positioning System (GPS), smart phone, and unmanned aerial vehicle. This approach has demonstrated its capability to consistently detect vertical displacement in critical areas of roads. Furthermore, there is a strong correlation between the PSI interferometry and the in situ measurements when absolute values are taken into consideration. The findings of this study also demonstrate that open-source solutions are more effective, which encompass freely available data and software, offered through the COPERNICUS program, possess significant potential for monitoring vertical displacements in roads.

Optimal Configuration of Omega-Kappa FF-SAR Processing for Specular and Non-Specular Targets in Altimetric Data: The Sentinel-6 Michael Freilich Study Case

In this study, the full-focusing (FF) algorithm is reviewed with the objective of optimizing it for processing data from different types of surfaces probed in altimetry. In particular, this work aims to provide a set of optimal FF processing parameters for the Sentinel-6 Michael Freilich (S6-MF) mission. The S6-MF satellite carries an advanced radar altimeter offering a wide range of potential FF-based applications which are just beginning to be explored and require prior optimization of this processing. In S6-MF, the Synthetic Aperture Radar (SAR) altimeter acquisitions are known to be aliased in the along-track direction. Depending on the target, aliasing can be tolerated or may be a severe impairment to provide the level of performance expected from FF processing. Another key aspect to consider in this optimization study is the unprecedented resolution of the FF processing, which results in a higher posting rate than the standard SAR processing. This work investigates the relationship between posting rate and noise levels and provides recommendations for optimal algorithm configurations in various scenarios, including transponder, open ocean, and specular targets like sea-ice and inland water scenes. The Omega–Kappa (WK) algorithm, which has demonstrated superior CPU efficiency compared to the back-projection (BP) algorithm, is considered for this study. But, unlike BP, it operates in the Doppler frequency domain, necessitating further precise spectral and time domain settings. Based on the results of this work, real case studies using S6-MF acquisitions are presented. We first compare S6-MF FF radargrams with Sentinel-1 (S1) images to showcase the potential of optimally configured FF processing. For highly specular surfaces such as sea-ice, distinct techniques are employed for lead signature identification. S1 relies on image-based lineic reconstruction, while S6-MF utilizes phase coherency of focalized pulses for lead detection. The study also delves into two-dimensional wave spectra derived from the amplitude modulation of image/radargrams, with a focus on a coastal example. This case is especially intriguing, as it vividly illustrates different sea states characterized by varying spectral peak positions over time.

Prediction of airport runway settlement using an integrated SBAS-InSAR and BP-EnKF approach

The prediction of surface settlement occupies a crucial role in achieving effective catastrophe prevention and mitigation, as well as facilitating the maintenance of airport runways. Given the challenges associated with the manual collection of settlement data and the suboptimal timeliness of the Back Propagation (BP) technique in neural networks, this study proposes an integrated prediction method that combines the Ensemble Kalman Filter (EnKF) with BP. This study focuses on the processing of Synthetic Aperture Radar (SAR) pictures obtained from the ascending orbit of the Sentinel-1A satellite. The Small Baseline Subset Interferometric synthetic aperture radar (SBAS-InSAR) technology is employed to derive the settlement time series on the runway of Kangding Airport. Moreover, three sites with high coherence within the primary settlement region are employed to assess the dependability of the model after the expansion of the data by cubic spline interpolation. The findings of the study indicate that both the BP-EnKF and BP models exhibit favorable outcomes in predicting airport runway settlement. However, following the alteration of data caused by external environmental influences, the BP-EnKF model has superior adaptability to variations in data. It has been shown that the BP-EnKF model exhibits a prediction accuracy that surpasses the BP model by 9.25%.

A multi-view ensemble model based on semi-supervised feature learning for small sample classification of PolSAR images

ABSTRACT Classification with a few samples of training set has been a long-standing issue in the field of polarimetric synthetic aperture radar (PolSAR) image analysis and processing. In fact, one of the most important challenges of the PolSAR image classification task is the number of labelled samples. In essence, training network by utilizing just few numbers of training samples cannot be led to the reliable result since the neural network is so sensitive to the number of training samples. In addition, in the real PolSAR image classification task, the huge number of training samples is not accessible. On the other hand, classification performance using just small number of labelled samples is not an accurate result. So, in this paper, aiming at the small number of training samples of the PolSAR image classification task, a novel ensemble self-supervised feature-learning (ESSFL) model is designed. The designed ESSFL can automatically extract PolSAR features conducive to PolSAR image classification with a small number of training samples. In addition, it can significantly decrease the dependence of neural network algorithms on large labelled samples of training set. First, to utilize the spatial–polarimetric features of PolSAR data perfectly, the EfficientNet-B0 is presented and utilized as the main section of the deep learning (DL) model to extract DL features of PolSAR data. Then, using an optimization function that constrains the cross-correlation matrix of various distortions of each sample to the identity matrix, the designed deep learning model can obtain the effective features of homogeneous samples gathering together and heterogeneous samples separating from each other in a self-supervised manner. Moreover, two ensemble learning (EL) models, feature-level and view-level ensemble, are proposed to increase the feature extraction capability and classification result by jointly using spatial features at different scales and polarimetric information at different bands. Finally, the stack of the obtained features and the main polarimetric information of PolSAR data can be input into classifier for the classification of PolSAR data. In this paper we use two different classifiers, the first one is the Random Forest (RF) classifier and the second one is the Support Vector Machine (SVM) classifier. The advantages of SVM include that it can be used to avoid the difficulties of using linear functions in the high-dimensional feature space, and the optimization problem is transformed into dual convex quadratic programs that are so usable for an actual image classification task. In addition, among all the available classification methods, random forests provide one of the highest accuracies. The random forest technique can also handle big data with numerous variables running into thousands. In fact, it can automatically balance data sets when a class is more infrequent than other classes in the data that is so important for a real PolSAR image classification task. Experimental results on three well-known PolSAR data sets illustrate that the designed ESSFL can extract more discriminant features using the designed deep learning model. In the end, the experiments prove that the designed ESSFL has a significant classification performance compared with different deep learning models in the case of small number of training samples, and also it can achieve a better result in the case of large number of training samples by comparison with the most of the deep learning models.

A field test of compact active transponders for InSAR geodesy

Abstract Compact active transponders (CATs) – also termed electronic corner reflectors – are compact electronic devices designed to receive, actively amplify and re-transmit a radar signal, e.g. a C-band radar signal received from a Sentinel-1 satellite. CATs can potentially be useful for a number of purposes, e.g. if co-located with geodetic infrastructure. However, CATs have only recently become commercially available, and therefore, the usability and long-term performance of CATs are not well known. In this study, two CATs are tested under realistic operating conditions for a period of 14 months, from July 2020 to September 2021. The displacement time series of the CATs are determined from a persistent scatterers interferometric synthetic aperture radar processing of four tracks of Sentinel-1A/-1B data with a passive corner reflector (CR) as the spatial reference. The displacement time series of the CATs are evaluated against a ground truth established from repeated levellings between the CR and the CATs. Based on the results of this study, it is found that a sudden vertical displacement of a CAT can be determined with an accuracy better than 1 cm, possibly a few millimetres. Furthermore, it is found that the mean vertical velocity of a CAT, calculated from 14 months of interferometric synthetic aperture radar displacement time series, can be determined with an accuracy of a few mm/year. Finally, the line of sight (LoS) phase error is generally found to be moderately correlated with temperature, with an instrument-specific linear relationship between LoS error and temperature ranging between approx. 0.1 and 0.2 mm/°C. This correlation between LoS phase error and temperature can in principle be used for instrument-specific calibrations, which is a topic that should be addressed in future studies.

Error Sources of Interferometric Synthetic Aperture Radar Satellites

Interferometric synthetic aperture radar (InSAR) processing techniques have been widely used to derive surface deformation or retrieve terrain elevation. Over the development of the past few decades, most research has mainly focused on its application, new techniques for improved accuracy, or the investigation of a particular error source and its correction method. Therefore, a thorough discussion about each error source and its influence on InSAR-derived products is rarely addressed. Additionally, InSAR is a challenging topic for beginners to learn due to the intricate mathematics and the necessary signal processing knowledge required to grasp the core concepts. This results in the fact that existing papers about InSAR are easy to understand for those with a technical background but difficult for those without. To cope with the two issues, this paper aims to provide an organized, comprehensive, and easily understandable review of the InSAR error budget. In order to assist readers of various backgrounds in comprehending the concepts, we describe the error sources in plain language, use the most fundamental math, offer clear examples, and exhibit numerical and visual comparisons. In this paper, InSAR-related errors are categorized as intrinsic height errors or location-induced errors. Intrinsic height errors are further divided into two subcategories (i.e., systematic and random error). These errors can result in an incorrect number of phase fringes and introduce unwanted phase noise into the output interferograms, respectively. Location-induced errors are the projection errors caused by the slant-ranging attribute of the SAR systems and include foreshortening, layover, and shadow effects. The main focus of this work is on systematic and random error, as well as their effects on InSAR-derived topographic and deformation products. Furthermore, because the effects of systematic and random errors are greatly dependent on radar wavelengths, different bands are utilized for comparison, including L-band, S-band, C-band, and X-band scenarios. As examples, we used the parameters of the upcoming NISAR operation to represent L-band and S-band, ERS-1 and Sentinel-1 to represent C-band, and TerraSAR-X to represent X-band. This paper seeks to bridge this knowledge gap by presenting an approachable exploration of InSAR error sources and their implications. This robust and accessible analysis of the InSAR error budget is especially pertinent as more SAR data products are made available (e.g., NISAR, ICEYE, Capella, Umbra, etc.) and the SAR user-base continues to expand. Finally, a commentary is offered to explore the error sources that were not included in this work, as well as to present our thoughts and conclusions.

Simultaneous Localization and Mapping (SLAM) for Synthetic Aperture Radar (SAR) Processing in the Field of Autonomous Driving

Simultaneous Localization and Mapping (SLAM) for Synthetic Aperture Radar (SAR) Processing in the Field of Autonomous Driving

Short-time Coherent Incoherent Synthetic Aperture Radar Processing for a Handheld Imaging System

Short-time Coherent Incoherent Synthetic Aperture Radar Processing for a Handheld Imaging System

A Feasibility Analysis of the Use of ISAR Training Data in Machine Learning-Based SAR ATR

Processing of synthetic aperture radar (SAR) images for automatic target recognition (ATR) is a critical application especially in military surveillance. In particular, numerous machine learning-based SAR ATR methods have been proposed for this task. However, data training and testing stages of all these methods are based on the exploitation of SAR signatures of the target under investigation. Considering the high variability of radar targets, obtaining such signature data is obviously a costly and time consuming process. In this study, therefore, a feasibility analysis of the use of inverse-SAR (ISAR) training data in SAR ATR has been made for the first time. The turntable ISAR and circular SAR images of three different vehicles are used in training and testing is performed by means of SAR images of three similar targets within the publicly available MSTAR dataset. Also, three most prominent machine learning methods, namely KNN, SVM and ANN are used in conjunction with three different feature extraction algorithms namely, GLRLM, GLSZM and GLCM. The obtained results reveal that the GLCM+SVM algorithm pair is the most effective model with 85% accuracy.

The Utility of LiCSBAS to Carry out InSAR Time Series to Analyze Surface Deformation: An Overview

Surface deformation analysis plays a crucial role in understanding geodetic phenomena and their implications for hazard assessment, infrastructure management, and environmental studies. In recent years, the incorporation of synthetic aperture radar (InSAR) and advanced processing techniques have been used to monitor surface deformation. One such technique, LiCSBAS, has shown great promise in analyzing InSAR time series data to accurately detect and quantify surface displacements. It has been successfully employed in various geodetic monitoring scenarios such as landslide monitoring, infrastructure stability assessment, and volcanic deformation analysis. In this study, studies were reviewed in which performance evaluation and comparison of LiCSBAS with other technologies were discussed as well as future developments and challenges in this field. The results of a review of previous studies show the effectiveness of LiCSBAS in detecting and analyzing surface deformation, allowing for a better understanding of geodetic processes. This overview will provide a set of ideas that will assist stakeholders involved in surface deformation analysis and geodetic monitoring and contribute to decision-making to achieve sustainable development.

A novel self‐supervised ensemble learning framework for land use and land cover classification of polarimetric synthetic aperture radar images

AbstractClassification with a few samples of training set has been a longstanding issue in the field of polarimetric synthetic aperture radar (PolSAR) image analysis and processing. Aiming at the small number of training samples of the PolSAR image classification task, a novel Self‐Supervised Ensemble Learning Framework (SSELF) is designed. The designed SSELF can automatically extract PolSAR features conducive to PolSAR image classification with a small number of training samples. In addition, it can significantly decrease the dependence of neural network algorithms on large labelled samples of training set. First, utilise the spatial–polarimetric features of PolSAR data perfectly, the EfficientNet‐B0 is presented and utilised as the main section of the Deep Learning (DL) model to extract DL features of PolSAR data. Then, using an optimisation function that constrains the cross‐correlation matrix of various distortions of each sample to the identity matrix, the designed DL model can obtain the effective features of homogeneous samples gathering and heterogeneous samples separating from each other in a self‐supervised manner. Moreover, following the great success of curriculum learning in the area of machine learning, a novel deep curriculum‐learning model is proposed, entitled Deep Curriculum Learning (DCL), to train the DL network in our self‐supervised model. The proposed DCL utilises the entropy‐alpha target decomposition to estimate the degree of complexity of each PolSAR image patch before applying it to the EfficientNet‐B0. Also, an accumulative mini‐batch pacing function is used to introduce more difficult patches to EfficientNet‐B0 in the training process of the designed self‐supervised model. Furthermore, two EL models, feature‐level and view‐level ensemble, are proposed to increase the feature extraction capability and classification result of PolSAR data by jointly using spatial features at different scales and polarimetric information at different bands. In fact, in the proposed feature‐level ensemble strategy, to improve the classification result, the extracted features of the different scales are used as the input of the designed feature fusion algorithm. Therefore, the extracted features are mapped to the new space with lower dimension. In general, in the proposed view‐level ensemble strategy, first, the Mutual Information (MI) between each feature and the other features calculates, and then based‐on the calculated MI, different group of features are selected as various views. Next, the final classification result of the PolSAR image classification is obtained by using the designed majority vote of the result in each view. It should be noted that unlike existing traditional methods, both amplitude and phase information of SAR images to devolve the training process of the proposed model are used. In addition, all of the parameters of the proposed network are developed to a complex domain. In addition, a complex backpropagation algorithm by using the gradient‐based model is used for training the network. Finally, experimental results on three well‐known PolSAR data sets illustrate that the designed SSELF can extract more discriminant features using the designed DL model and can achieve better classification results than the other six novel DL models in terms of small training samples and adequate labelled samples.

The Stability Analysis of Mt. Gongga Glaciers Affected by the 2022 Luding MS 6.8 Earthquake Based on LuTan-1 and Sentinel-1 Data

On 5 September 2022, an MS 6.8 earthquake occurred in Luding county, Sichuan province, China, with the epicenter located approximately 20 km from the main peak of Mount (Mt.) Gongga. The dynamic situation of Mt. Gongga glaciers has received widespread attention. In this study, Mt. Gongga was selected as the study area, and L-band LuTan-1 (LT-1) satellite data were used for differential interferometric synthetic aperture radar (D-InSAR) processing to obtain the coseismic landform in Luding. Based on Sentinel-1A images, pixel offset tracking (POT) technology was used to obtain the surface movement velocities of the glaciers before, during, and after the earthquake. The results showed that the overall preseismic movement of the glaciers was fast in the area where the ice cascade of the Hailuogou Glacier reached a maximum average deformation rate of 0.94 m/d. Moreover, time-series monitoring of the postseismic glaciers showed that the surface flow velocities of some glaciers in the study area increased after the earthquake. The flow velocity at the main peak of Mt. Gongga and the tongue of the Mozigou Glacier accelerated for a period after the earthquake. The study concluded that the earthquake was one of the direct causes of the increase in glacier flow velocity, which returned to a stable state more than 70 days after the earthquake. The relevant monitoring results and research data can provide a reference for earthquake-triggered glacial hazards and indicate the effectiveness of LT-1 in identifying and monitoring geological hazards.

A New Method for Continuous Track Monitoring in Regions of Differential Land Subsidence Rate Using the Integration of PS-InSAR and SBAS-InSAR

It is difficult for single time-series Interferometric Synthetic Aperture Radar (InSAR) processing to guarantee the accuracy and efficiency of continuous track monitoring in regions of differential subsidence. This paper proposes a new method, integrating the Persistent Scatterer InSAR (PS-InSAR) with high precision and the Small Baseline Subset InSAR (SBAS-InSAR) with high efficiency for continuous track monitoring in regions of differential land subsidence rates. Based on PS-InSAR processing, the Iterative Self-Organizing Data Analysis Techniques (ISODATA) algorithm is adopted to search the boundary of differential subsidence between slow and fast subsidence rates. The SBAS-InSAR processing with high frequency is used to continuously track and monitor the regions with fast subsidence rates incorporating original data and newly added data into small data sets from time to time according to SAR data updating, the monitoring results of which are obtained from the weighted average of the added results of SBAS-InSAR processing and the original results of PS-InSAR processing. The impact of SAR data updating on the slow subsidence rate region is so weak that it is not necessary to simultaneously update the corresponding monitoring results to improve global efficiency. If the slow subsidence rates region must be remeasured in relation to its previous subsidence, or the proportion of new data capacity alters compared with the original data set, PS-InSAR processing is used to analyze the whole monitoring region again using the complete data set. A case study performed on the west region of the Qinhuai River in Nanjing, China, indicates that the density of monitoring points in the fast-subsidence region is greatly improved, increasing from 711 points/km2 to 2760 points/km2—an increase of 288.2%.

The simulation of urban development with the consideration of ground deformation threats in Sidoarjo Regency, East Java Province of Indonesia

Sidoarjo plays a role as buffer zone for the expanding urban growth of Surabaya City, which results in the rapid development of built areas. After the Lapindo mudflow in 2006, subsidence occurred in the surrounding area of Porong District. Land subsidence is a slow-onset disaster that impact people's lives due to vertical displacement. Urban development in areas that are geologically prone to subsidence may have long-term impacts, both on settlements and infrastructure. Therefore, it is necessary to acknowledge its ground deformation trait and how it interacts with urban spatial development. The characteristics of ground deformation were identified utilizing Interferometric Synthetic Aperture Radar (InSAR) processing with Small Baseline Subset (SBAS) method which resulted in 10 levels of ground deformations. The map of ground deformation level was overlaid with the map of future land use of 2041 which was generated from Cellular Automata (CA) based land use simulation. Areas with alarming ground deformation level, that are “at-risk”, are found around the Lapindo area, and extends to Sidoarjo's north side. Meanwhile, the built area is estimated to grow around the major roads, in the central side from north to south, and in the middle-south part of Sidoarjo. The interaction between urban development and ground deformation provides an overview of areas that may encounter ground deformation threats, such as land subsidence and uplift movement, in the future. The results of this study can be used for spatial planning decisions with consideration of ground deformation threats by directing urban development to the “safe” areas.

Towards On-Board SAR Processing with FPGA Accelerators and a PCIe Interface

This article addresses a novel methodology for the utilization of Field Programmable Gate Array (FPGA) accelerators in on-board Synthetic Aperture Radar (SAR) processing routines. The methodology consists of using High-Level Synthesis (HLS) to create Intellectual property (IP) blocks and using the Reusable Integration Framework for FPGA Accelerators (RIFFA) to develop a Peripheral Component Interconnect express (PCIe) interface between the Central Processing Unit (CPU) and the FPGA, attaining transfer rates up to 15.7 GB/s. HLS and RIFFA reduce development time (between fivefold and tenfold) by using high-level programming languages (e.g., C/C++); moreover, HLS provides optimizations like pipeline, cyclic partition, and unroll. The proposed schematic also has the advantage of being highly flexible and scalable since the IPs can be exchanged to perform different processing routines, and since RIFFA allows employing up to five FPGAs, multiple IPs can be implemented in each FPGA. Since Fast Fourier Transform (FFT) is one of the main functions in SAR processing, we present a FPGA accelerator in charge of the reordering stage of VEC-FFT (an optimized version of FFT) as a proof of concept. Results are retrieved in reversed bit order, and the conventional reordering function may consume more than half of the total clock cycles. Next, to demonstrate flexibility, an IP for matrix transposition is implemented, another computationally expensive process in SAR due to memory access.

Strategies for improving the communication of satellite-derived InSAR data for geohazards through the analysis of Twitter and online data portals

Abstract. Satellite-based earth observation sensors are increasingly able to monitor geophysical signals related to natural hazards, and many groups are working on rapid data acquisition, processing, and dissemination to data users with a wide range of expertise and goals. A particular challenge in the meaningful dissemination of Interferometric Synthetic Aperture Radar (InSAR) data to non-expert users is its unique differential data structure and sometimes low signal-to-noise ratio. In this study, we evaluate the online dissemination of ground deformation measurements from InSAR through Twitter, alongside the provision of open-access InSAR data from the Centre for Observation and Modelling of Earthquakes, Volcanoes and Tectonics (COMET) Looking Into Continents from Space with Synthetic Aperture Radar (LiCSAR) processing system. Our aim is to evaluate (1) who interacts with disseminated InSAR data, (2) how the data are used, and (3) to discuss strategies for meaningful communication and dissemination of open InSAR data. We found that the InSAR Twitter community was primarily composed of non-scientists (62 %), although this grouping included earth observation experts in applications such as commercial industries. Twitter activity was primarily associated with natural hazard response, specifically following earthquakes and volcanic activity, where users disseminated InSAR measurements of ground deformation, often using wrapped and unwrapped interferograms. For earthquake events, Sentinel-1 data were acquired, processed, and tweeted within 4.7±2.8 d (the shortest was 1 d). Open-access Sentinel-1 data dominated the InSAR tweets and were applied to volcanic and earthquake events in the most engaged-with (retweeted) content. Open-access InSAR data provided by LiCSAR were widely accessed, including automatically processed and tweeted interferograms and interactive event pages revealing ground deformation following earthquake events. The further work required to integrate dissemination of InSAR data into longer-term disaster risk-reduction strategies is highly specific, to both hazard type and international community of practice, as well as to local political setting and civil protection mandates. Notably, communication of uncertainties and processing methodologies are still lacking. We conclude by outlining the future direction of COMET LiCSAR products to maximize their useability.

A Lightweight Radar Ship Detection Framework with Hybrid Attentions

One of the current research areas in the synthetic aperture radar (SAR) processing fields is deep learning-based ship detection in SAR imagery. Recently, ship detection in SAR images has achieved continuous breakthroughs in detection precision. However, determining how to strike a better balance between the precision and complexity of the algorithm is very meaningful for real-time object detection in real SAR application scenarios, and has attracted extensive attention from scholars. In this paper, a lightweight object detection framework for radar ship detection named multiple hybrid attentions ship detector (MHASD) with multiple hybrid attention mechanisms is proposed. It aims to reduce the complexity without loss of detection precision. First, considering that the ship features in SAR images are not inconspicuous compared with other images, a hybrid attention residual module (HARM) is developed in the deep-level layer to obtain features rapidly and effectively via the local channel attention and the parallel self-attentions. Meanwhile, it is also capable of ensuring high detection precision of the model. Second, an attention-based feature fusion scheme (AFFS) is proposed in the model neck to further heighten the features of the object. Meanwhile, AFFS constructs and develops a fresh hybrid attention feature fusion module (HAFFM) upon the local channel and spatial attentions to guarantee the applicability of the detection model. The Large-Scale SAR Ship Detection Dataset-v1.0 (LS-SSDD-v1.0) experimental results demonstrate that MHASD can balance detection speed and precision (improving average precision by 1.2% and achieving 13.7 GFLOPS). More importantly, extensive experiments on the SAR Ship Detection Dataset (SSDD) demonstrate that the proposed method is less affected by the background such as ports and rocks.

Radiometric Terrain Correction Method Based on RPC Model for Polarimetric SAR Data

Radiometric terrain correction (RTC) is an important preprocessing step for synthetic aperture radar (SAR) data application in mountainous areas. At present, the RTC processing of SAR depends on the Range Doppler (RD) positioning model. However, the solution of this model has a high threshold for ordinary remote sensing technicians. To solve this problem, we propose an RTC method based on the rational polynomial coefficient (RPC) model, which is widely used in optical remote sensing and is simpler and more practical than the RD model. China’s GF-3 polarimetric SAR data were used to verify the proposed method. The experimental results showed that the RTC method based on RPC is effective and can achieve better correction effects on the premise of reducing the complexity of the algorithm. The correction effect based on the RPC model can be similar to that based on the RD model. The proposed approach can realize the correction of 4~5 dB terrain radiation distortion to a 0.5 dB level.