Radar Data Processing Research Articles

Foreword to the Special Issue on Forest Structure Estimation in Remote Sensing

FORESTS are important ecosystems to all life on Earth. They provide shelter for many flora and fauna, rich resources for anthropogenic development and an important sink in the global carbon cycle. Satellite earth observation provides a critical capacity to observe this ecosystem wall to wall. Especially, synthetic aperture radar (SAR) and Lidar technologies hold large potential to measure forest structure properties such as forest height and biomass and are consequently used in existing observation systems. In addition to the existing observation systems space agencies are implementing or planning dedicated satellite missions capitalizing on these technologies, such as ESAs BIOMASS, NASAs GEDI, and NISAR missions or DLRs Tandem-L mission concept. Despite the sensitivity of SAR and Lidar technologies to forest structure the actual interpretation of the measurements is challenging. Forest structure, environmental effects and system parameters have a confounding effect on the measurements and need to be accounted for in the biophysical parameter retrieval. The impact of these factors is often not well understood and hampers our ability to make optimum use of newly available technologies and their synergies. The most striking development in the last years was in the three-dimensional (3-D) radar data processing. The evolution and refinement of coherent multibaseline (tomographic) processing techniques dramatically increased the quality of the reconstructed 3-D radar reflectivity allowing a deeper insight in the 3-D scattering processes at different polarizations and frequencies and of the opportunities arising from this new measurement technique to monitor and investigate 3-D forest structure. This special issue collect and highlight the latest results, Digital Object Identifier 10.1109/JSTARS.2018.2870266 data processing developments and lessons learned from existing satellite data, airborne data and tower based research campaigns.

QUALITY ASSESSMENT OF AN EXTENDED INTERFEROMETRIC RADAR DATA PROCESSING APPROACH

Abstract. Radar data acquisition is a reliable technology to provide base data for topographical mapping. Its flexibility and weather independency makes radar data more attractive in comparison with traditional airborne data acquisition. This advantage emplaces radar data acquisition as an alternative method for many applications including Large Scale Topographical Mapping (LSTM). LSTM i.e. larger or equal than 1 : 10.000 map scale is one of the prominent priority tasks to be finished in an accelerated way especially in third world countries such as Indonesia. The available TerraSAR-X Add on Digital Elevation Model X (TanDEM-X) Intermediate Digital Elevation Model (IDEM) from German Aerospace Center (DLR) as one useful global scientific data set however still complies with High Resolution Terrain Information (HRTI) Level 3 only. The accuracy of the end product of pairwise bi-static TanDEM-X data can be improved by some potential measures such as incorporation of Ground Control Points (GCPs) within the interferometric data processing. It is expected that the corresponding end product can fulfil HRTI Level 4 specification. From this point, we focus on the step of phase difference measurements in radar interferometry to generate elevation model with least square adjustment approach using three main parameters i.e. height reference, absolute phase offset and baseline. Those three parameters are considered to be essential within the Digital Surface Model (DSM) generation process. Therefore it is necessary to find the optimal solution within aforementioned adjustment model. In this paper we use an linearized model, as discussed in section 2.4, to process the bi-static TanDEM-X datasets and investigate how this improves the accuracy of the generated DSM. As interferometric radar data processing relies on accurate GCP data we use Indonesian Geospatial Reference System (SRGI) for our investigations. Also, we use baseline and phase offset information from TanDEM-X metadata. Subsequently, the DSM generated using Sentinel Application Platform (SNAP) desktop, is the main product used for LSTM. This product has to be assessed using check points derived from conventional airborne data acquisition using RCD-30 metric camera and the accuracy is compared with the accuracy of the IDEM. Summarized, this paper aims on an improvement of the DSM generation by adjusting main parameters through our linearized model.

A Compensation Method for Airborne SAR with Varying Accelerated Motion Error

Motion error is one of the most serious problems in airborne synthetic aperture radar (SAR) data processing. For a smoothly distributing backscatter scene or a seriously speed-varying velocity platform, the autofocusing performances of conventional algorithms, e.g., map-drift (MD) or phase gradient autofocus (PGA) are limited by their estimators. In this paper, combining the trajectories measured by global position system (GPS) and inertial navigation system (INS), we propose a novel error compensation method for varying accelerated airborne SAR based on the best linear unbiased estimation (BLUE). The proposed compensating method is particularly intended for varying acceleration SAR or homogeneous backscatter scenes, the processing procedures and computational cost of which are much simpler and lower than those of MD and PGA algorithms.

Radarsat-1 image processing for regional-scale geological mapping with mining vocation under dense vegetation and equatorial climate environment, Southwestern Cameroon

The potential of Radarsat-1 beam mode Synthetic Aperture Radar (SAR) data processing for geological investigation in an equatorial environment has been evaluated. This approach used textural analysis based on Grey Level Co-occurrence Matrix (GLCM) on our image, followed by Principal Component Analysis (PCA) performed on eight normalized co-occurrence indices created (mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment and correlation) and directional filters for lithological discriminations and lineament investigations. Red-Green-Blue (RGB) color-composite was applied to three of the indices, the mean, variance and homogeneity, highlighting the morphostructure of the study area and facilitate lithology discrimination. The PC1 band was multiplied by itself (as PC1 × PC1 image) to enhance the information contained in this neo-canal and to reduce noise during filtering. Directional filters were then applied to the PC1 × PC1 image at 0°, 45°, 90° and 135° directions and the structure lines were extracted manually in a GIS software. From the results obtained, color-composite produced image map containing lithological units easily identified formations such as continental and coastal deposits, sedimentary stack, micaschists, garnet micaschists, micaceous quartzites, charnockitic orthogneisses, and coincided with those already existing on published geological map from Maurizot et al. (1986) and non-published geological map after IRGM geological field campaign. A total of 572 lineaments features (fractures and major faults) were identified on the filtered images and mapped. Major structures (faults) were considered as those clearly identified in the four directions while minor structures (fractures) were those observed in at least two directions. They are oriented in one of the two main directions: NE-SW (N040–N060) and NNW-SSE (N345–N360). The lineament result showed those that already existed on the reference maps and the newly updated lineaments. Spatial relationships between mapped lineaments and areas of current and historical mining exploration were examined by overall lineament density. GPS points of gold indices existing in the area correlate with areas of high lineament density particularly around the Ngovayang massif within the Paleoproterozoic Nyong unit. This study stresses the usefulness of remote sensing data and methods in field campaign, improvement of published geological maps and mining prospecting in areas with an equatorial climate.

Robust Reading Approach for Moving Chipless RFID Tags by Using ISAR Processing

A robust technique for reading moving chipless tags exploiting the inverse synthetic aperture radar data processing is presented. The procedure takes advantage from the known tag movement to improve the tag detection capability by integrating several received signals. The implicit advantage of the proposed detection scheme is that the background measurement, collected in the absence of the tag, can be easily performed before the tag passes in front of the reader antenna. The reading procedure allows for a significant increase of the interrogation distance and, more importantly, it greatly improves the detection probability in complex scenarios, where the tag is attached on arbitrary and highly scattering objects. A simplified model is used to demonstrate the capabilities of the presented procedure in detecting tags even in the presence of high clutter. Detection of tags with signal-to-noise ratio as low as −10 dB is experimentally demonstrated.

Spatial integration of navigation data as efficient method to improve quality of vessel traffic services support

Practical need for the integration of navigation data received by spatially-dispersed network of coast navigation stations of vessels traffic service in coastal regions has been proved. Technical supportability, potential and conceptual peculiarities of the integrated processing implementation of the abovementioned navigation data have been outlined. Perspectives of effective implementation of various well-known conceptual approaches to the development of such systems have been compared and analyzed. The functional scheme of the working automated radio system with a multi radar data processing subsystem has been developed. Its accuracy has been analyzed. The method has been offered and the experimental studies results have been cited to prove the effectiveness of the navigation data integrated processing to support the theoretical expectations. The presented data show that the current integral of the error parameter is considered to have minimal fluctuations, and the standard error shall not exceed the corresponding errors of each of the partial channels.

The GF-3 SAR Data Processor.

The Gaofen-3 (GF-3) data processor was developed as a workstation-based GF-3 synthetic aperture radar (SAR) data processing system. The processor consists of two vital subsystems of the GF-3 ground segment, which are referred to as data ingesting subsystem (DIS) and product generation subsystem (PGS). The primary purpose of DIS is to record and catalogue GF-3 raw data with a transferring format, and PGS is to produce slant range or geocoded imagery from the signal data. This paper presents a brief introduction of the GF-3 data processor, including descriptions of the system architecture, the processing algorithms and its output format.

Gaussian Distributions on Riemannian Symmetric Spaces: Statistical Learning With Structured Covariance Matrices

The Riemannian geometry of covariance matrices has been essential to several successful applications, in computer vision, biomedical signal and image processing, and radar data processing. For these applications, an important ongoing challenge is to develop Riemannian-geometric tools which are adapted to structured covariance matrices. This paper proposes to meet this challenge by introducing a new class of probability distributions, Gaussian distributions of structured covariance matrices . These are Riemannian analogs of Gaussian distributions, which only sample from covariance matrices having a preassigned structure, such as complex, Toeplitz, or block-Toeplitz. The usefulness of these distributions stems from three features: 1) they are completely tractable, analytically, or numerically, when dealing with large covariance matrices; 2) they provide a statistical foundation to the concept of structured Riemannian barycentre ( i.e. , Frechet or geometric mean); and 3) they lead to efficient statistical learning algorithms, which realise, among others, density estimation and classification of structured covariance matrices. This paper starts from the observation that several spaces of structured covariance matrices, considered from a geometric point of view, are Riemannian symmetric spaces. Accordingly, it develops an original theory of Gaussian distributions on Riemannian symmetric spaces, of their statistical inference, and of their relationship to the concept of Riemannian barycentre. Then, it uses this original theory to give a detailed description of Gaussian distributions of three kinds of structured covariance matrices, complex, Toeplitz, and block-Toeplitz. Finally, it describes algorithms for density estimation and classification of structured covariance matrices, based on Gaussian distribution mixture models.

风廓线雷达资料在一次降水过程中的处理和应用

风廓线雷达资料在一次降水过程中的处理和应用

Assessing the suitability of Sentinel-1 data for landslide mapping

ABSTRACTLandslides are recognized as one of the most damaging natural hazards in Western Greece due to the complex geological structure, the intense precipitation and the high seismicity. In that context, the development of low-cost, semiautomatic, monitoring methodologies based on satellite data is a necessity in order to prevent and mitigate the landslide risk and decrease the damages in a future reactivation. The current study focuses on the application of three different processing methodologies on Sentinel-1 data: (a) interferogram generation, (b) comparison of interferometric Digital Surface Model (DSM) before and after the landslide event and (c) another processing technique called “offset tracking”. The selection of Sentinel-1 data was based on the fact that the specific mission provides timely, freely accessible data, with satisfactory spatial resolution for repeated earth observation. The study area is located almost 20 km south of the city of Patras near to the Village Moira. The landslide with a total length of 300 m and a head width of 330 m is being investigating by repeated field campaigns and Global Navigation Satellite System measurements, and thus, the derived results from the Synthetic Aperture Radar (SAR) data processing were evaluated with the in situ measurements.

АЛГОРИТМ ОБРАБОТКИ ДАННЫХ ГЕОРАДИОЛОКАЦИИ ДЛЯ ВЫЯВЛЕНИЯ КОНТУРОВ ДЕФОРМАЦИОННЫХ ГЕОЛОГИЧЕСКИХ СТРУКТУР

АЛГОРИТМ ОБРАБОТКИ ДАННЫХ ГЕОРАДИОЛОКАЦИИ ДЛЯ ВЫЯВЛЕНИЯ КОНТУРОВ ДЕФОРМАЦИОННЫХ ГЕОЛОГИЧЕСКИХ СТРУКТУР

Image processing and analysis of radar and lidar data: new discoveries in Verona southern lowland (Italy)

ABSTRACTThis contribution proposes an evaluation of lidar and radar data processing and its potential in revealing archaeological features within a level plain environment, the southern lowland of Verona (Italy), focusing on evidences dating back to the Bronze Age. Many archaeological sites in the research area, including some of the most outstanding settlements of Terramare Culture, were identified or at least examined through aerial photo observation. Even if in several occasions modern agricultural activities contributed to the discoveries, bringing to the surface artifacts and scrapes of buried layers, this kind of impact has also been progressively deteriorating the archaeological record, hence the proto-historic landscape is now discernible through evanescent marks which cannot be always detected using customary optical sensors. Lidar and radar data analysis has then been considered as an alternative, non-invasive method of investigation on such a vast area.

Intercomparison of snow depth retrievals over Arctic sea ice from radar data acquired by Operation IceBridge

Abstract. Since 2009, the ultra-wideband snow radar on Operation IceBridge (OIB; a NASA airborne mission to survey the polar ice covers) has acquired data in annual campaigns conducted during the Arctic and Antarctic springs. Progressive improvements in radar hardware and data processing methodologies have led to improved data quality for subsequent retrieval of snow depth. Existing retrieval algorithms differ in the way the air–snow (a–s) and snow–ice (s–i) interfaces are detected and localized in the radar returns and in how the system limitations are addressed (e.g., noise, resolution). In 2014, the Snow Thickness On Sea Ice Working Group (STOSIWG) was formed and tasked with investigating how radar data quality affects snow depth retrievals and how retrievals from the various algorithms differ. The goal is to understand the limitations of the estimates and to produce a well-documented, long-term record that can be used for understanding broader changes in the Arctic climate system. Here, we assess five retrieval algorithms by comparisons with field measurements from two ground-based campaigns, including the BRomine, Ozone, and Mercury EXperiment (BROMEX) at Barrow, Alaska; a field program by Environment and Climate Change Canada at Eureka, Nunavut; and available climatology and snowfall from ERA-Interim reanalysis. The aim is to examine available algorithms and to use the assessment results to inform the development of future approaches. We present results from these assessments and highlight key considerations for the production of a long-term, calibrated geophysical record of springtime snow thickness over Arctic sea ice.

Generalized Nonlinear Chirp Scaling Algorithm for High-Resolution Highly Squint SAR Imaging.

This paper presents a modified approach for high-resolution, highly squint synthetic aperture radar (SAR) data processing. Several nonlinear chirp scaling (NLCS) algorithms have been proposed to solve the azimuth variance of the frequency modulation rates that are caused by the linear range walk correction (LRWC). However, the azimuth depth of focusing (ADOF) is not handled well by these algorithms. The generalized nonlinear chirp scaling (GNLCS) algorithm that is proposed in this paper uses the method of series reverse (MSR) to improve the ADOF and focusing precision. It also introduces a high order processing kernel to avoid the range block processing. Simulation results show that the GNLCS algorithm can enlarge the ADOF and focusing precision for high-resolution highly squint SAR data.

Weather radar data processing on graphic cards

Weather radar operation generates data at a high rate that requires prompt processing. The operations performed on data for weather product generation are repeated in each resolution cell and thus are naturally prone to parallelization. Parallel processing using graphic cards is an emerging technology that allows for implementation of high-throughput algorithms at a low cost. In this paper, the parallel implementation of the main product of a polarimetric weather radar using GPU is presented, focusing on its optimization. A speedup exceeding 20 $$\times $$ is obtained when compared to the serial implementation. Also processing is found to be memory bound, which results in a counter-intuitive performance improvement when the number of threads per job is reduced.

Integration of upward GPR and water content reflectometry to monitor snow properties

ABSTRACTWe adopt upward ground‐penetrating radar (up‐GPR) and water content reflectometry sensors to monitor the seasonal behaviour of snow density. Upward ground‐penetrating radar permitted observation at a single fixed station the time‐lapse response of the electromagnetic signal at the main frequency of 1500 MHz, with the antenna radiating upward from the soil toward the snow surface. Measurements have been performed at a test site on the Italian Alps (at an elevation of about 2100 m above sea level) during the 2014–2015 winter season at an interval of 30 minutes. The data processing of radar data involved the travel‐time picking and the conversion into snow depth and density. Water content reflectometry measurements have been useful in order to calibrate the radar response and retrieve information on the presence of liquid water content. The integration of upward ground‐penetrating radar and water content reflectometry technology allows us to infer snow high and layering, snow density changes during the winter season, and a preliminary estimate of the liquid water content. For snow in dry condition, we are able to estimate density values through mixing rules or polynomial formula. Snow density varies during the season in a range between 250 kg/m3 and 450 kg/m3; the results are in good agreement with the results of the ground truth. For snow in wet condition, the residuals of the electrical permittivity, after a trend removal on the original water content reflectometry data, permitted to estimate liquid water content in the range between 3% and 5%, during some periods of the winter season, according to warmer climate condition.Snow layering and densification processes are monitored by the response of upward ground‐penetrating radar: fast phenomena such as wetting front infiltration can be also pointed out even if they appear challenging if other observations are not available (e.g., monitoring with water content reflectometry).

The Big Data Processing of HF Sky-Wave Radar Sea Echo for Detection of Sea Moving Targets

A high-frequency (HF) sky-wave radar always monitoring large area of sea surface, for detecting sea surface moving objects, there must be big data waiting to be processed. A set of data processing methods were proposed, the successful implementation of HF sky-wave radar on the sea moving target detection. By setting the HF sky-wave radar parameters, after the initial data processing, the gotten HF sky-wave radar data were saved. Then a new HF sky-wave radar data processing method was provided, this method was the so-called three-step detection method (TSTM) which based on the constant false alarm rate (CFAR) technique. By using TSTM, setting the decision threshold G, with false alarms being ruled out, a moving target was detected out at last, its speed was calculated. The results also proved that TSTM could effectively reduce the sea clutter, and greatly lessen the echo-broadening and double-image caused by ionosphere contamination.

ANN-based radar approach to detect breast cancers in fibro-glandular tissues: numerical analysis

This paper presents a new artificial neural network (ANN)-based radar data processing approach for the detection of breast cancers located inside fibro-glandular tissues. The aim is not the breast imaging but detecting tumors through ANN processing of data extracted from the radar signals measured around the breast. The proposed approach has been assessed using several realistic two-dimensional breast geometries derived from the models provided by the numerical breast phantom repository of the University of Wisconsin Cross-Disciplinary Electromagnetic Laboratory (UWCEM). The pulsed radar system was assumed to operate in the mono-static configuration. The obtained results showed the abilities of the proposed approach to detect, for any single radar trace, tumors located inside the fibro-glandular tissues with a sensitivity of 93%, a specificity of 90%, and an overall accuracy of 92%.

Analysis of surface deformations over the whole Italian territory by interferometric processing of ERS, Envisat and COSMO-SkyMed radar data

Interferometric processing of series of data acquired over time by synthetic aperture radar (SAR) satellites makes it possible to measure millimetric deformations (typically due to landslides, subsidence and earthquake or volcanic phenomena) and to monitor the stability of terrain and infrastructures. Despite the unique capability to observe very large areas, this technology has been typically applied to the analysis of relatively small sites or specific geophysical phenomena. In this work, we present the first application of this technology to a national scale project, which required the processing, through advanced persistent scatterer interferometry (PSI) techniques, of about 20,000 SAR images acquired from 1992 to 2014 over the whole Italian territory by the ERS, Envisat, and COSMO-SkyMed satellites. The obtained results provide a huge database of surface deformation measurements covering the last 20years and the whole Italian territory, which represents an extremely useful and pioneering service for geo-hazards mapping and prevention.

Hainan Coherent Scatter Phased Array Radar (HCOPAR): System Design and Ionospheric Irregularity Observations

The Hainan coherent scatter phased array radar (HCOPAR) is one of the most important radio systems of the Chinese Meridian Space Weather Monitoring Project (Meridian Project). The radar is located at Fuke station (19.5°N, 109.1°E, dip latitude 8.1°N), Hainan island, China, to observe the field-aligned irregularities in ionospheric $E$ - and $F$ -regions. It is operated with a peak power of 54 kW and uses the Barker codes for good sensitivity. Its antenna array is composed of $18 \times 4$ five-element Yagi antennas and is used for both transmitting and receiving. The Yagi antennas are arranged in a rectangular grid 100 m long and 20 m wide, covering an area of 2000 m2. This antenna arrangement forms seven radar beams from −22.5° to 22.5° in azimuth with an angle step of 7.5°. The central beam points to due geographic north. A brief description of the radar system, data processing, and signal characteristics is also provided. The intensity, Doppler velocities, and spectral widths of the quasi-periodic echoes measured in the nighttime of June 23, 2013 are presented as typical data of type II irregularities in the $E$ -region. The data recorded by the HCOPAR in the fan-beam mode on September 15, 2013 are displayed as the typical $F$ -region plasma bubbles.