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Radar sounders (RSs) are gaining importance in planetary missions thanks to their unique capability of providing direct measurements of subsurface (SS) structures. To support their design and data interpretation, several electromagnetic (e.m.) simulation techniques have been developed with enhanced capabilities for emulating the RS acquisition process. However, the raw simulated radargrams obtained from e.m. simulators are difficult to interpret and analyze without a focusing operation, which results in an underestimation of the RS detection performance. While frequency methods for range and azimuth compression of real RS data are well-established, their use on simulated data is not addressed in the literature and requires major modifications. This article presents a novel method that implements azimuth compression using unfocused and focused processing on simulated raw data. The proposed method is based on an adaptation of the range-Doppler algorithm to the case of raw data generated by a coherent RS simulator. The method is demonstrated in three case studies to show the similarity between simulated and real data processing: 1) simple geometries; 2) a simulated SHAllow RADar (SHARAD) radargram compared with the real data product; and 3) a real application scenario for supporting the design of a new RS instrument. The results indicate higher fidelity of the focused simulated data with the real data product and the target structure, confirming the usefulness of the proposed approach in obtaining realistic processing of simulated radargrams. [ABSTRACT FROM AUTHOR]

Spatiotemporal distribution of soil moisture is important for hydrometeorological and agricultural applications. There is growing interest in monitoring soil moisture in relation to soil- and land-based natural flood management (NFM), to understand the soil’s ability, via land-use and management changes, and to delay the arrival of flood peaks in nearby watercourses. This article monitors relative surface soil moisture (rSSM) across the Thames Valley, U.K., using Sentinel-1 data, and the Vienna University of Technology (TU-Wien) Change Detection Algorithm, with a novel exploration of monthly and annual normalization factors and spatial averaging. Two pairs of normalization factors are introduced to remove impacts from varying local incidence angles through direct and multiple regression slopes. The spatiotemporal distribution of rSSM values at various spatial resolutions (1000, 500, 250, and 100 m) is assessed. Comparisons with in situ soil moisture data from the COSMOS-UK network show that, while general temporal trends agree, the difference in effective depth of measurements, coupled with vegetation impacts during the growing season, makes comparison with soil moisture observations difficult. Temporal rSSM trends can be retrieved at spatial resolutions down to 100 m, and the rSSM RMSE was found to decrease as the spatial resolution increases. The vegetation effects upon the rSSM are further explored by comparing the two dominant land cover types: Arable and Horticulture, and Improved Grassland. It was found that, while the rSSM retrieval for these land covers was possible, and the general soil moisture trend is clear, overlying vegetation during the summer artificially increased the rSSM values. [ABSTRACT FROM AUTHOR]

A quantum-state-based technique for change detection and analysis is presented. The method establishes a framework for change detection based on quantum states’ entanglement concept. The approach is applicable to quantum sensing (QS) (i.e., low field intensity, photon pairs) but is contingent to feasibility of quantum implementations and measurements. The main objective in this work is to develop a new change detection technique for classical radar/synthetic aperture radar (SAR) remote sensing that is inspired by quantum illumination (QI) and exploits the underlying characteristics and mathematical analogy between quantum-states and radar scatter-field-states. The described application of QI concept to develop a quantum signal processing (QSP) algorithm for classical radar/SAR data exploitations introduces a new change detection approach for SAR multi-pol exploitations. [ABSTRACT FROM AUTHOR]

Forward-looking multichannel synthetic aperture radar (FLMC-SAR) is an important tool for modern remote sensing applications, which has the capability to reconstruct the high-resolution image of the front area. However, due to the azimuth-variant characteristics of the motion errors over a long aperture, FLMC-SAR data processing is usually a challenging task, especially when involving the motion compensation (MOCO) coupled with Doppler ambiguity resolving. To accomplish an accurate MOCO for FLMC-SAR, a novel parametric azimuth-variant MOCO approach is proposed in this article. Aiming at the coupling problem of MOCO and Doppler ambiguity resolving over the full aperture, we can decouple them through the subaperture division. As a full synthetic aperture is decomposed into several subapertures, the high-order motion errors of the full aperture can be decomposed into the first-order motion errors of the subaperture. On this basis, the mismatch of the space–time spectrum caused by the motion errors can be solved by spectral estimation, yielding Doppler ambiguity resolving for each subaperture. Meanwhile, the azimuth-variant characteristic of motion errors in FLMC-SAR system is characterized by a parametric angle-dependent quadratic phase error (QPE) model. The motion parameters are estimated by a joint multichannel angle estimation-based signal quadratic decomposition method. Immediately, the MOCO for ambiguous targets with different motion errors can be processed separately to improve the imaging performance. Experimental results based on both simulated and real data demonstrate that the proposed method is suitable for FLMC-SAR system. [ABSTRACT FROM AUTHOR]

Remote sensing technology is an essential link in the global monitoring of the ocean surface, and radars are efficient sensors for detecting marine pollution. When used operationally by authorities, a tradeoff must usually be made between the covered area and the quantity of information collected by the radar. To identify the most appropriate imaging mode, a methodology based on receiver operating characteristic curve analysis has been applied to an original data set collected by two airborne systems operating at L-band, both characterized by a very low instrument noise floor. The data set was acquired during controlled releases of mineral and vegetable oil at sea. Various polarization-dependent quantities are investigated, and their ability to detect slick-covered areas is assessed. A relative ordering of the main polarimetric parameters is reported in this paper. When the sensor has a sufficiently low noise floor, HV is recommended because it provides the strongest slick–sea contrast. Otherwise, VV is found to be the most relevant parameter for detecting slicks on the ocean surface. Among all the investigated quad-polarimetric settings, no significant added value compared to single-polarized data was found. More specifically, it is demonstrated, by increasing the instrument noise level, that the studied polarimetric quantities which combine the four polarimetric channels have performances of detection mainly driven by the instrument noise floor, namely, the noise equivalent sigma zero. This result, obtained by progressively adding noise to the raw synthetic aperture radar (SAR) data, indicates that the polarimetric discrimination between clean sea and polluted area results mainly from the differentiated behavior between single-bounce scattering and noise. It is thus demonstrated, using SAR data collected with a low instrument noise floor, that there is no deviation from Bragg scattering for radar scattering from ocean surface covered by mineral and vegetable oil. [ABSTRACT FROM AUTHOR]

This paper presents the estimation of the ground elevation and canopy top elevation from the data collected by an airborne frequency-modulated continuous waveform profiling radar, Tomoradar. The estimated ground and canopy top elevations are critical for the derivation of reference information for the satellite-borne microwave radar data and the modeling of interaction between microwave radar signal and foliage. The methods of estimating the ground elevation and canopy top elevation from profiling radar are introduced, and the accuracy was evaluated via digital terrain model and Velodyne VLP-16 LiDAR integrated with the Tomoradar. To our knowledge, the ranging radar and the LiDAR data were simultaneously collected for the first time. The evaluation proved that the root-mean-square error (RMSE) of ground level estimation of the developed profiling radar can reach up to 0.33 m. When comparing the estimated canopy top peak elevation between the profile radar data and the LiDAR data, it was found that the side lobes of Tomoradar antenna system may produce undesired canopy backscatters when the size of the canopy gap is comparable to the footprint size of the main lobe, resulting in a higher canopy top elevation measurement from Tomoradar than that from LiDAR. The RMSE of the estimated canopy top peak elevation between two data sets was 0.32 m in the best case and 0.852 m on average. Moreover, the RMSE of point-to-point comparing the entire canopy tops elevation estimated from the data of two active remote sensing systems is 0.799 m after excluding the outliers. [ABSTRACT FROM AUTHOR]

The commonly applied K-distribution to model the synthetic aperture radar image amplitude of the heterogeneous (non-Gaussian) sea surface as the basis for vessel detection has shown deficiencies in practical cases, particularly for spacebased systems. Due to a deviation between the K-probability density function and measured histograms in the tails, even the inclusion of thermal noise is oftentimes not sufficient to cover the range of environments that are expected. As a consequence, virtually all detectors try to reduce the large number of obtained false detections by relying on rather heuristic postprocessing steps. Consequently, they forfeit the crucial property of a constant false alarm rate. This paper proposes a novel statistical sea clutter model that describes the data more accurately, especially in challenging environments and thermal-noise limited cases. This new model stands out through its numerical simplicity, permitting efficient parameter adaptation thereby enhancing robustness and reducing computational complexity. Accordingly, the presented sea data model has the potential to replace the widely adopted K-distribution as model of choice for future operational applications. [ABSTRACT FROM AUTHOR]

This paper provides an overview of the surface waves investigation and monitoring (SWIM) instrument which will be one of the two payload instruments carried by China France Oceanography SATellite (CFOSAT) with a planned launch date in mid-2018. SWIM is a real aperture wave scatterometer operated at near-nadir incidence angles and dedicated to the measurement of directional spectra of ocean waves. The SWIM flight model is currently being assembled and tested, its performance is being assessed and its prototype data processing algorithm is being developed. The aim of this paper is to provide a complete overview on the motivations and scientific requirements of this mission, together with a description of the design and characteristics of the SWIM instrument, and the analysis of its expected performances based on a prelaunch study. An end-to-end simulator has been developed to evaluate the quality of the data products, thus allowing the overall performance of the instrument to be assessed. Simulations run with two subsets of full orbit subsets show that the performances of the instrument and the inversion algorithms will meet the scientific requirements for the mission. [ABSTRACT FROM PUBLISHER]

Radiometric correction of radar images is essential to produce accurate estimates of biophysical parameters related to forest structure and biomass. We present a new algorithm to correct radiometry for 1) terrain topography and 2) variations of canopy reflectivity with viewing and tree-terrain geometry. This algorithm is applicable to radar images spanning a wide range of incidence angles over terrain with significant topography and can also take into account aircraft attitude, antenna steering angle, and target geometry. The approach includes elements of both homomorphic and heteromorphic terrain corrections to correct for topographic effects and is followed by an additional radiometric correction to compensate for variations of canopy reflectivity with viewing and tree-terrain geometry. The latter correction is based on lookup tables and enables derivation of biophysical parameters irrespective of viewing geometry and terrain topography. We evaluate the performance of the new algorithm with airborne radar data and show that it performs better than classical homomorphic methods followed by cosine-based corrections. [ABSTRACT FROM PUBLISHER]

A new and robust constant false alarm rate (CFAR) detector based on truncated statistics (TSs) is proposed for ship detection in single-look intensity and multilook intensity synthetic aperture radar data. The approach is aimed at high-target-density situations such as busy shipping lines and crowded harbors, where the background statistics are estimated from potentially contaminated sea clutter samples. The CFAR detector uses truncation to exclude possible statistically interfering outliers and TSs to model the remaining background samples. The derived truncated statistic CFAR (TS-CFAR) algorithm does not require prior knowledge of the interfering targets. The TS-CFAR detector provides accurate background clutter modeling, a stable false alarm regulation property, and improved detection performance in high-target-density situations. [ABSTRACT FROM AUTHOR]

This paper analyzes the multiangle imaging results for bistatic synthetic aperture radar (BSAR) based on global navigation satellite systems (GNSS-BSAR). Due to the shortcoming of GNSS-BSAR images, a multiangle observation and data processing strategy based on BeiDou-2 navigation satellites was put forward to improve the quality of images and the value of system application. Twenty-six BSAR experiments were conducted and analyzed in different configurations. Furthermore, a region-based fusion algorithm using region-of-interest (ROI) segmentation was proposed to generate a high-quality fusion image. Based on the fusion image, typical targets such as water area, vegetation area, and artificial targets were compared and interpreted among single/multiple-angle images. The results reveal that the multiangle imaging method was a good technique to enhance image information, which might extend the applications of GNSS-BSAR. [ABSTRACT FROM PUBLISHER]

This paper addresses the important yet unresolved problem of estimating forest properties from polarimetric-interferometric radar images affected by temporal decorrelation. We approach the problem by formulating a physical model of the polarimetric-interferometric coherence that incorporates both volumetric and temporal decorrelation effects. The model is termed random-motion-over-ground (RMoG) model, as it combines the random-volume-over-ground (RVoG) model with a Gaussian-statistic motion model of the canopy elements. Key features of the RMoG model are: 1) temporal decorrelation depends on the vertical structure of forests; 2) volumetric and temporal coherences are not separable as simple multiplicative factors; and 3) temporal decorrelation is complex-valued and changes with wave polarization. This third feature is particularly important as it allows compensating for unknown levels of temporal decorrelation using multiple polarimetric channels. To estimate model parameters such as tree height and canopy motion, we propose an algorithm that minimizes the least square distance between model predictions and complex coherence observations. The algorithm was applied to L-band NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar data acquired over the Harvard Forest (Massachussetts, USA). We found that the RMS difference at stand level between estimated RMoG-model tree height and NASA's lidar Laser Vegetation and Ice Sensor tree height was within 12% of the lidar-derived height, which improved significantly the RMS difference of 37% obtained using the RVoG model and ignoring temporal decorrelation. This result contributes to our ability of estimating forest biomass using in-orbit and forthcoming polarimetric-interferometric radar missions. [ABSTRACT FROM PUBLISHER]

We propose a new model for estimating subsurface soil moisture using P-band radar data over barren, shrubland, and vegetated terrains. The unknown soil moisture profile is assumed to have a second-order polynomial form as a function of subsurface depth with three unknown coefficients that we estimate using the simulated annealing algorithm. These retrieved coefficients produce the value of soil moisture at any given depth up to a prescribed depth of validity. We use a discrete scattering model to calculate the radar backscattering coefficients of the terrain. The retrieval method is tested and developed with synthetic radar data and is validated with measured radar data and in situ soil moisture measurements. Both forward and inverse models are briefly explained. The radar data used in this paper have been collected during the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) mission flights in September and October of 2012 over a 100 km by 25 km area in Arizona, including the Walnut Gulch Experimental Watershed. The study area and the ancillary data layers used to characterize each radar pixel are explained. The inversion results are presented, and it is shown that the RMSE between the retrieved and measured soil moisture profiles ranges from 0.060 to 0.099 m3/m3, with a Root Mean Squared Error (RMSE) of 0.075 m3/m3 over all sites and all acquisition dates. We show that the accuracy of retrievals decreases as depth increases. The profiles used in validation are from a fairy dry season in Walnut Gulch and so are the accuracy conclusions. [ABSTRACT FROM PUBLISHER]

The annual freeze/thaw (FT) cycle determines the potential growing season in boreal landscapes and is a major factor determining ecosystem productivity and associated exchange of trace gases (CO2, H2O) with the atmosphere. Accurate characterization of these processes can improve regional assessment of seasonal carbon dynamics and climate feedbacks. FT process variations are spatially and temporally complex due to topography, snow depth and wetness, land cover, or local climatic conditions. In this paper, we perform a landscape analysis of multifrequency and multitemporal satellite microwave remote sensing measurements at L-band (JERS-1), C-band (ERS), and Ku-band (QuikSCAT) for characterizing FT dynamics. We first analyze backscatter sensitivity of the three frequencies to FT conditions over selected Alaska temperature sites. We then apply an FT classifier over two study areas (wetland complex and moderate topography) and examine differences in FT timing according to vegetation, elevation, and north/south facing slope. Results show that L-, C-, and Ku-band backscatter are sensitive to landscape FT state transitions, with higher backscatter for nonfrozen than frozen conditions at C- and L-bands but the opposite response at Ku-band. We applied a change detection algorithm to the C-band and L-band data over both study areas and analyzed the FT classifications with land cover information. These results resolve characteristic patterns of earlier spring thawing for south facing slopes, lower elevations, and coniferous vegetation. Our results also inform similar FT algorithm development for the NASA Soil Moisture Active Passive mission by documenting L-band FT sensitivity and heterogeneity over a boreal landscape. [ABSTRACT FROM PUBLISHER]

Revisiting time constitutes a key constraint for continuous monitoring activities based on space- and airborne synthetic aperture radar (SAR) acquisitions. Conversely, the employment of terrestrial platforms overcomes this limitation and makes it possible to perform time-continuous observations of small space-scale phenomena. New research lines of SAR dealing with the backscattering evolution of different types of scenarios become hence possible through the analysis of ground-based SAR (gbSAR) data collections. The Remote Sensing Laboratory of the Universitat Politècnica de Catalunya drove a one-year measurements campaign in the village of Sallent, northeastern Spain, using its X-Band gbSAR sensor. The field experiment aimed at studying the subsidence phenomenon induced by the salt mining activity carried out in this area during the past decades. In this paper, the polarimetric behavior of an urban environment is investigated at different time scales. After a brief description of the test site and the measurement campaign, the analysis is focused on the stability on man-made structures at different time scales. PolSAR data monthly acquired from June 2006 to July 2007 are employed to stress the presence of nonstationary backscattering processes within the urban scene and the effect they have on differential phase information. Then, a filtering procedure aiming at reducing backscattering randomness in one-day and long-term data collections is then put forward. The improvements provided by the proposed technique are assessed using a new polarimetric descriptor, the time entropy. In the end, the importance of preserving the interferometric phase information from nonstationary backscattering contaminations using fully polarimetric data is discussed. [ABSTRACT FROM PUBLISHER]

This paper proposes a new method for the classification of synthetic aperture radar (SAR) images based on a novel feature vector. The method aims at combining the intensity information of pixels with spatial information and structural relationships. Unlike classical approaches which define a static neighborhood via a rectangular moving window of predefined size and relate spatial information for each center pixel to all the pixels within that window, the local primitives (LPs) proposed in this study provide us with an adaptive neighborhood so that spatial information for each center pixel is extracted only from the related pixels in its neighborhood. LPs correspond to local homogeneous connected components that describe the pixel neighborhood more consistently than the fixed size window approach. A feature vector, called as the LP pattern (LPP), is constructed for each pixel. The feature vector includes information about the sizes, intensity levels, and contrast differences of LPs within a disk whose center is the pixel under consideration as well as the repetitive frequency of LPs outside that disk. Finally, a kernel-based support vector machine is used with the proposed feature vectors for the classification of SAR images. Experimental analysis presents that the new feature extraction technique is well suited to depict spatial information and structural relationships and it yields promising results for the classification of SAR images when compared to common features such as gray-level co-occurrence matrix and Gabor coefficients. [ABSTRACT FROM PUBLISHER]

Polarimetric synthetic aperture radar (PolSAR) image classification, an important technique in the remote sensing area, has been deeply studied for a couple of decades. In order to develop a robust automatic or semiautomatic classification system for PolSAR images, two important problems should be addressed: 1) incorporation of spatial relations between pixels; 2) estimation of the number of classes in the image. Therefore, in this paper, we present a novel superpixel-based classification framework with an adaptive number of classes for PolSAR images. The approach is mainly composed of three operations. First, the PolSAR image is partitioned into superpixels, which are local, coherent regions and preserve most of the characteristics necessary for image information extraction. Then, the number of classes and each class center within the data are estimated using the pairwise dissimilarity information between superpixels, followed by the final classification operation. The proposed framework takes the spatial relations between pixels into consideration and makes good use of the inherent statistical characteristics and contour information of PolSAR data. The framework is capable of improving the classification accuracy, making the results more understandable and easier for further analyses, and providing robust performance under various numbers of classes. The performance of the proposed classification framework on one synthetic and three real data sets is presented and analyzed; and the experimental results show that the framework provides a promising solution for unsupervised classification of PolSAR images. [ABSTRACT FROM PUBLISHER]

The target coherency or covariance matrices are the main operators useful for characterizing the polarization transformation properties of radar target by modeling the depolarization effect. In this paper, a novel decomposition of the target coherency matrix is proposed, that is sufficient for representing the physical characteristics of the observed medium in term of a minimum set of orientation invariant parameters. The Einstein's photon circular polarization basis is used for obtaining an orientation invariant physical interpretation of the proposed parameter set both for deterministic and random target. A generalized unsupervised classification scheme is also proposed for underlining the effectiveness of the proposed decomposition theorem for classifying random reciprocal target into 75 physically meaningful clusters. The application of the proposed decomposition theorem and classification algorithm is useful for developing of novel Remote Sensing products and Data Mining softwares for monitoring the surfaces of the Earth and the Moon. [ABSTRACT FROM PUBLISHER]

Satellite radar altimeters have an established and increasingly vital role in monitoring the Earth's surface inland water resources. This paper analyzes two years of EnviSat burst echoes (which have 3.9-m nominal along-track separation) over rivers, lakes, and ephemeral water globally to assess the increase in monitoring potential afforded by the higher pulse repetition frequency (PRF) of the next generation of synthetic aperture radar altimeters and investigate spatial burst correlation. Burst echoes at 1800 Hz are successfully retracked with no waveform averaging. The conclusion is that the higher PRF allows detection and measurement of water bodies on a far finer spatial scale because water is a very bright reflector at Ku-band and land in general is relatively poor, with pools of water a few tens of meters across being successfully identified. [ABSTRACT FROM AUTHOR]

The radio echo sounding (RES) system is one of the most widely used active remote sensing techniques for polar ice sheet exploration, including bedrock morphology studies and subglacial lake investigations. Recently, bedrock characterization has been improved through the analysis of radar echo strength. In this paper, the analysis of the RES signal amplitude has been used to collect information about the controversial problem of electromagnetic ice absorption to highlight areas of high reflectivity variation, ascribable to wet ice-bedrock interfaces. A method to distinguish a wet or dry bedrock-ice interface using a model to describe the internal ice absorption is proposed and discussed. Moreover, the comparison between the ice absorption rates from RES measurements and from European Project for Ice Coring in Antarctica Dome C (Antarctica) ice core conductivity data, the signal amplitude contributions of internal ice layers, and different kinds of rock interface is evaluated. Encouraged by the results, the data analysis led to obtaining a bedrock reflectivity variation map of the Dome C area. This map outlined a wide dispersion of wet/dry rock interfaces in the studied area, indicating the possibility of flowing water along both sides of the Concordia Trench. [ABSTRACT FROM AUTHOR]

A space adaptive flexible block quantizer (SA-FBQ), suited for spaceborne synthetic aperture radar missions, is presented. This quantizer is actually an extension of the flexible dynamic block adaptive quantizer (FDBAQ), proposed in refid="ref1"/, which, in turn, is an extension of the block adaptive quantizer (BAQ). The BAQ is the optimal quantizer for a homogeneous target. The FDBAQ gets better performances on heterogeneous targets by adaptively selecting the best BAQ according to the local signal-to-thermal-noise ratio: The worst the SNTR, the lower the quantizer rate. The quantizer selection is precomputed in a lookup table (LUT), by assuming a fixed and known probability distribution function (pdf) of the reflectivity \sigma0. The SA-FBQ extends further this concept allowing the reflectivity pdf to vary, coping with this by exploiting many LUTs (i.e., quantizer set), each adapted to the local statistics. In this paper, we introduce an algorithm to adaptively find the best set of quantizers constrained on the mean bit rate; we discuss the implementation of the SA-FBQ, and we estimate its performances in comparison with the FDBAQ and the BAQ under different scenarios. Preliminary results are shown by exploiting the worldwide mosaic of C-band reflectivity derived from European Space Agency ENVISAT data and Sentinel-1 system parameters. [ABSTRACT FROM AUTHOR]

The most intuitive way to extract depth information from remote sensing images is stereogrammetry, in which a digital elevation model (DEM) is achieved by computing stereoscopic radar images. When only the amplitude of the radar images is considered, this computation is called radargrammetry. The main idea of which is to match stereopair radar images in order to create a disparity map from one image to the other and, finally, to compute the elevation. Therein, we present our studies on the extraction of 3-D information from radar images. We examine a way to produce a DEM of a challenging area of the French Alps. The central issue of this paper concerns improvements for radargrammetric synthetic aperture radar image processing for high-relief reconstruction, and we focus on the matching step, which is one of the most important points of the radargrammetric processing. Thus, we propose original methods using different correlation windows. On the one hand, we take the advantages of a multiwindow approach to combine relevant information by multiplying the correlation surfaces obtained for each correlation window size during the matching operation. On the other hand, the second improvement is based on the expansion of windows on foreshortened areas, particularly because of the side-looking radar view. These methods allow us to achieve reliable image matching and to improve the accuracy of the DEM. [ABSTRACT FROM AUTHOR]

Model-based decomposition of polarimetric radar covariance matrices holds the promise that specific scattering mechanisms can be isolated for further quantitative analysis. In this paper, we show that current algorithms suffer from a fatal flaw in that some of the scattering components result in negative powers. We propose a simple modification that ensures that all covariance matrices in the decomposition will have nonnegative eigenvalues. We further combine our nonnegative eigenvalue decomposition with eigenvector decomposition to remove additional assumptions that have to be made before the current algorithms can be used to estimate all the scattering components. Our results are illustrated using Airborne Synthetic Aperture Radar data and show that current algorithms typically overestimate the canopy scattering contribution by 10%–20%. [ABSTRACT FROM AUTHOR]

Spaceborne synthetic aperture radar (SAR) systems operating at lower frequencies, such as P-band, are significantly affected by Faraday rotation (FR) effects. A novel algorithm for calibrating the circular-transmit-and-linear-receive (CTLR) mode spaceborne compact polarimetric SAR using mixed calibrators is proposed, which is able to correct precisely both FR and radar system errors (i.e., channel imbalance and crosstalk). Six sets of mixed calibrators, consisting of both passive calibrators and polarimetric active radar calibrators (PARCs), are investigated. Theoretical analysis and simulations demonstrate that the optimal calibration scheme combines four polarimetric selective mixed calibrators, including two gridded trihedrals and two PARCs, together with total-electron-content measurements by the Global Navigation Satellite System system. [ABSTRACT FROM AUTHOR]

The Cassini RADAR is a multimode instrument used to map the surface of Titan, the atmosphere of Saturn, the Saturn ring system, and to explore the properties of the icy satellites. Four different active mode bandwidths and a passive radiometer mode provide a wide range of flexibility in taking measurements. The scatterometer mode is used for real aperture imaging of Titan, high-altitude (around 20 000 km) synthetic aperture imaging of Titan and Iapetus, and long range (up to 700 000 km) detection of disk integrated albedos for satellites in the Saturn system. Two SAR modes are used for high- and medium-resolution (300-1000 m) imaging of Titan's surface during close flybys. A high-bandwidth altimeter mode is used for topographic profiling in selected areas with a range resolution of about 35 m. The passive radiometer mode is used to map emission from Titan, from Saturn's atmosphere, from the rings, and from the icy satellites. Repeated scans with differing polarizations using both active and passive data provide data that can usefully constrain models of surface composition and structure. The radar and radiometer receivers show very good stability, and calibration observations have provided an absolute calibration good to about 1.3 dB. Relative uncertainties within a pass and between passes can be even smaller. Data are currently being processed and delivered to the planetary data system at quarterly intervals one year after being acquired. [ABSTRACT FROM AUTHOR]

An adaptive and fast constant false alarm rate (CFAR) algorithm based on automatic censoring (AC) is proposed for target detection in high-resolution synthetic aperture radar (SAR) images. First, an adaptive global threshold is selected to obtain an index matrix which labels whether each pixel of the image is a potential target pixel or not. Second, by using the index matrix, the clutter environment can be determined adaptively to prescreen the clutter pixels in the sliding window used for detecting. The C° distribution, which can model multilook SAR images within an extensive range of degree of homogeneity, is adopted as the statistical model of clutter in this paper. With the introduction of AC, the proposed algorithm gains good CFAR detection performance for homogeneous regions, clutter edge, and multitarget situations. Meanwhile, the corresponding fast algorithm greatly reduces the computational load. Finally, target clustering is implemented to obtain more accurate target regions. According to the theoretical performance analysis and the experiment results of typical real SAR images, the proposed algorithm is shown to be of good performance and strong practicability. [ABSTRACT FROM AUTHOR]

In semiarid regions, thousands of small reservoirs provide the rural population with water, but their storage volumes and hydrological impact are largely unknown. This paper analyzes the suitability of weather-independent radar satellite images for monitoring small reservoir surfaces. The surface areas of three reservoirs were extracted from 21 of 22 ENVISAT Advanced Synthetic Aperture Radar scenes, acquired bimonthly from June 2005 to August 2006. The reservoir surface areas were determined with a quasi-manual classification approach, as stringent classification rules often failed due to the spatial and temporal variability of the backscatter from the water. The land—water contrast is critical for the detection of water bodies. Additionally, wind has a significant impact on the classification results and affects the water surface and the backscattered radar signal (Bragg scattering) above a wind speed threshold of 2.6 m · s-1. The analysis of 15 months of wind speed data shows that, on 96% of the days, wind speeds were below the Bragg scattering criterion at the time of night time acquisitions, as opposed to 50% during the morning acquisition time. Night time acquisitions are strongly advisable over day time acquisitions due to lower wind interference. Over the year, radar images are most affected by wind during the onset of the rainy season (May and June). We conclude that radar and optical systems are complimentary. Radar is suitable during the rainy season but is affected by wind and lack of vegetation context during the dry season. [ABSTRACT FROM AUTHOR]

In this paper, effects of birefringence on radio waves obliquely propagating though polar ice sheets are examined to facilitate interpretations of bistatic and side-looking radar data. A formalism applicable for arbitrary radar configurations is developed to predict the returned power from within and beneath the ice sheets that have arbitrary alignments of ice crystals (ice fabrics). We applied this formalism to a range of ice fabrics found in ice cores and assessed the effects of birefringence in terms of ray-path configurations, ice fabrics, and radar frequency. Predicted frequency dependence of the bed return power replicates prominent features observed at Greenland NGRIP ice-core site. Results show that birefringence in ice of 1 km or more thickness with strong (weak) fabric can reduce the power returned from the bed 2 dB or more at frequencies higher than 200 MHz (20 MHz) as compared to isotropic ice. This suggests that quantitative interpretation of the power returned from the bed requires careful assessment of birefringence almost everywhere over the ice sheets. Application of this formalism also suggests a radar-frequency range usable for attenuation measurements, possible effects of fabric on synthetic aperture radar processing, and a feasibility of remote sensing of ice fabric. [ABSTRACT FROM AUTHOR]

In this paper, we present a generalized Wishart classifier derived from a non-Gaussian model for polarimetric synthetic aperture radar (PolSAR) data. Our starting point is to demonstrate that the scale mixture of Gaussian (SMoG) distribution model is suitable for modeling PolSAR data. We show that the distribution of the sample covariance matrix for the SMoG model is given as a generalization of the Wishart distribution and present this expression in integral form. We then derive the closed-form solution for one particular SMoG distribution, which is known as the multivariate K-distribution. Based on this new distribution for the sample covariance matrix, termed as the K-Wishart distribution, we propose a Bayesian classification scheme, which can be used in both supervised and unsupervised modes. To demonstrate the effect of including non-Gaussianity, we present a detailed comparison with the standard Wishart classifier using airborne EMISAR data. [ABSTRACT FROM AUTHOR]

We consider the exploitation of dielectric properties of saline deposits for the detection and mapping of moisture in arid regions on both Earth and Mars. We present simulated and experimental study in order to assess the effect of salinity on the complex permittivity of geological materials and, therefore, on the radar backscattering coefficient in the [1-7 GHz] frequency range. Laboratory measurements are performed on sand/sodium chloride aqueous mixtures using a vectorial network analyzer coupled to an open-ended coaxial dielectric probe. We aim at calibrating and validating semiempirical dielectric mixing models. In particular, we evaluated the dependence of the real and imaginary parts of complex permittivity on the microwave frequency, water content, and salinity. Our results confirm that if the real part is mainly affected by the moisture content, the imaginary part is more sensitive to salinity. In addition to the classic formulas of mixing models, the ionic-conductivity losses, which are due to mobile ions in the saline solution, are taken into account in order to better assess the effect of salinity on the dielectric properties of mixtures. Dielectric mixing models are then used as input parameters for the simulation of the radar backscattering coefficients by means of an analytical model: the integral equation model. Simulation results show that salinity should have a significant impact on the radar backscattering recorded in synthetic aperture radar data in terms of the magnitude of the backscattering coefficient. Moreover, our results suggest that VV polarization provides a greater sensitivity to salinity than HH polarization. [ABSTRACT FROM AUTHOR]

This paper introduces the innovative concept of multidimensional waveform encoding for spaceborne synthetic aperture radar (SAR). The combination of this technique with digital beamforming on receive enables a new generation of SAR systems with improved performance and flexible imaging capabilities. Examples are high-resolution wide-swath radar imaging with compact antennas, enhanced sensitivity for applications like along-track interferometry and moving object indication, and the implementation of hybrid SAR imaging modes that are well suited to satisfy hitherto incompatible user requirements. Implementation-specific issues are discussed and performance examples demonstrate the potential of the new technique for different remote sensing applications. [ABSTRACT FROM AUTHOR]

The improved ground resolution of state-of-the-art synthetic aperture radar (SAR) sensors suggests utilizing SAR data for the analysis of urban areas. The appearance of buildings in SAR or interferometric SAR (InSAR) data is characterized by the consequences of the inherent oblique scene illumination, such as layover, occlusion by radar shadow, and multipath signal propagation. Therefore, particularly in dense built-up areas, building reconstruction is often impossible from a single SAR or InSAR measurement alone. But, the reconstruction quality can be significantly improved by a combined analysis of multi-aspect data. In this paper, two approaches are proposed to detect and reconstruct buildings of different size from multi-aspect high-resolution InSAR data sets. Both approaches focus on the recognition of buildings supported by knowledge-based analysis considering the mentioned SAR-specific effects observed in urban areas. Building features are extracted independently for each direction from the magnitude and phase information of the interferometric data. Initial primitives are segmented and afterward projected from slant-range into the world coordinate system. From the fused set of primitives of both flight directions, building hypotheses are generated. The first approach exploits the frequently observed lines of bright double-bounce scattering, which are used for building reconstruction in residential districts. In the case of larger buildings, such as industrial halls, often additional features of roof and façade elements are visible. Therefore, in a second approach, extended buildings are extracted by grouping primitives of different kinds. The two approaches are demonstrated in an urban environment for an InSAR data set, which has spatial resolution of about 30 cm and was taken from two orthogonal flight directions. [ABSTRACT FROM AUTHOR]

In this paper, a novel method is proposed for retrieving 3-D topography of the ground surface by using a single-antenna squint-mode airborne synthetic aperture radar (SAR). The main idea of the method is based on the fact that the spatial 3-D location of a target can be found as the intersection of the range sphere, the Doppler cone, and the elevation plane of the antenna pattern. An accurate estimation of the Doppler centroid for each resolution cell is required to realize this method. Experimental results obtained with a Ku-band airborne SAR are presented. The method calls for using a precise calibrated attitude determination unit. [ABSTRACT FROM AUTHOR]

In this paper, we consider landmine detection using ultrawideband synthetic aperture radar, where the two main challenges are feature extraction and discriminator design. The space-wavenumber processing is proposed to retrieve the frequency- and aspect-angle-dependent scattering features of suspected objects. In order to reduce the dimensionality of the input feature vector for a discriminator, the sequential forward floating selection method is used to choose efficient features. Based on the obtained feature vector, a fuzzy hypersphere support vector machine is designed to deal with the problem of detecting landmines in an unconstrained environment. The experimental results show that the proposed method can achieve a significant improvement in detection performance for antitank mines. [ABSTRACT FROM AUTHOR]

Inverse synthetic aperture radar (ISAR) images are frequently used in target classification and recognition applications. Some classifiers often require features that can be more easily obtained by extracting scattering centers from ISAR data rather than by reconstructing ISAR images. An available method for scattering center extraction, namely, the CLEAN technique, was proposed in a recent paper by Yang et al. In this paper, an improvement of this CLEAN technique is proposed that introduces a new method for detecting scattering centers. The proposed technique is based on a Gaussianity test, and its effectiveness is first theoretically proven and then tested on real data. Moreover, a comparison with the technique proposed by Yang et al. is shown. [ABSTRACT FROM AUTHOR]

In this paper, the polarization orientation angle shifts induced by artificial structures were analyzed and attempted to combine target orientation information which characterizes an urban area with the polarization orientation angle information extracted by the circular-polarization method. In order to illustrate the characteristic of the polarization orientation angle shifts represented in an urban area, the X-band polarimetric synthetic aperture radar (SAR) datasets of Sendai City acquired by Pi-SAR were used. This paper is based on Kimura's idea that the variation of the polarization orientation angle shifts depends on the artificial structures, particularly for the target rotation angle. We found that the polarization orientation angle shift is also affected by the relative relationship between the target rotation angle and the flight direction. From these results, it is possible to estimate the target rotation angle via computation of the polarization orientation angle shift from polarimetric SAR data. [ABSTRACT FROM AUTHOR]

The Advanced Land Observing Satellite (ALOS) is Japan's new-generation Earth Observation satellite, launched in January 2006 by the Japan Aerospace Exploration Agency. ALOS carries two optical instruments (Panchromatic Remote-sensing Instrument for Stereo Mapping and Advanced Visible and Near-Infrared Radiometer type 2) and, to maintain Japan's commitment to spaceborne L-band Synthetic Aperture Radar (SAR), the Phased Array L-band SAR (PALSAR). The successor to the SAR onboard the Japanese Earth Resources Satellite (1992-1998), the PALSAR instrument provides enhanced sensor characteristics, including full polarimetry, variable off-nadir viewing, and ScanSAR operations, as well as significantly improved radiometric and geometric performance. As important as the technical improvements and the reason PALSAR here is referred to as a pathfinder mission for global environmental monitoring is the systematic data-acquisition strategy which has been implemented for ALOS. With a priority second only to emergency observations, the PALSAR observation strategy has been designed to provide consistent, wall-to-wall observations at fine resolution of all land areas on the Earth on a repetitive basis, in a manner which has earlier been conceived only for coarse- and medium-resolution instruments. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of modeling high-resolution synthetic aperture radar clutter data from different vegetated areas. We analyzed moving and stationary target recognition (MSTAR) data sets focusing on histograms, moments, and covariance of clutter amplitude, texture, and speckle. The most celebrated statistical models are tested on real data of grass field or wood and trees to validate the goodness of fit of the compound Gaussian model in different scenarios. The results demonstrate that for grass fields, the compound Gaussian model provides a good data fitting. This is not the case for woods images where the speckle is not more Gaussian distributed. Covariance analysis and concluding remarks complete this paper. [ABSTRACT FROM AUTHOR]

In this paper, we propose a bistatic linear array synthetic aperture radar (BLA-SAR) system for moving target detection, location, and imaging. In the BLA-SAR system, a geo-stationary satellite is used as a transmitter, and two airborne linear array radars are used as passive receivers, where the transmitted waveforms from the geostationary satellite may have two different carrier frequencies, two linear array antennas on two different airplanes may be equipped with different spacings, or two airplanes may fly with two different velocities. It is shown that, using the BLA-SAR, not only the stationary clutter can be suppressed but also locations of both slow and fast moving targets can be accurately estimated. Furthermore, an effective BLA-SAR algorithm of moving target imaging is also proposed. Lastly, some numerical experiments are given to demonstrate the effectiveness of the BLA-SAR. [ABSTRACT FROM AUTHOR]

Ground moving target indication (GMTI) for synthetic aperture radar (SAR) provides information on nonstatic objects in radar imagery of a static ground scene. An efficient approach for GMTI is the use of multichannel SAR systems for a space- and time-variant analysis of moving targets. This allows the indication, correction of position displacement, and estimation of radial velocity components of moving targets in a SAR image. All three steps are possible due to a determinable Doppler frequency shift in the radar signal caused by radial target movement. This paper focuses on the millimeter wave (mmW) SAR system MEMPHIS with multichannel amplitude-comparison monopulse data acquisition and the ability to use carrier frequencies of 35 and 94 GHz simultaneously, making it a dual-frequency SAR. This paper includes mmW-specific SAR GMTI considerations, an adaptive algorithm to collect velocity and position information on moving targets with mmW monopulse radar, and a discussion on GMTI blind speed elimination and target velocity ambiguity resolving by dual-frequency SAR. To determine the capabilities of both, system and algorithm, three large-scale experiments with MEMPHIS in different environments are presented. [ABSTRACT FROM AUTHOR]

A novel approach to polarimetric image simulation for synthetic aperture radar (SAR) observation over comprehensive terrain scenes is developed based on mapping and projection principles. It incorporates penetrable and impenetrable objects, volumetric and surface scatterers in the imaging space with extinction, attenuation, shadowing, and multiple-scattering effects. Scattering of the vegetation canopy is modeled as a layer of random nonspherical particles by using the vector radiative transfer model, and scattering from the ground surface and building objects is calculated by using the integral equation method. As an example, polarimetric SAR images at L-band and C-band and the different spatial resolutions for a virtual terrain scene composed of tree canopies, farmland, buildings, rough land surface, hills, and rivers are simulated. The imaging simulation results demonstrate the feasibility of the mapping and projection approach and the potential utilities of SAR imaging simulation. [ABSTRACT FROM AUTHOR]

Focusing on a forest dominated by Poplar Box (Eucalyptus populnea) near Injune in Queensland, Australia, light detection and ranging (LiDAR) and optical remote sensing data are integrated with tree- and stand-level information to parameterize a coherent L-band synthetic aperture radar (SAR) imaging simulation that models microwave penetration and interaction with the canopy, understory, and ground. The approach used LiDAR data to generate a three-dimensional representation of the distribution of tree components (leaves and small branches) by species (based on 1-m³ voxels) and the ground surface. Tree trunks were mapped across a 7.5-ha forest stand using a LiDAR-derived height-scaled crown openness index. Primary and secondary branches were modeled as tapering cylinders and linked the canopy voxels to the LiDAR trunks. The dimensions of vegetation and soil components and their geometric and dielectric properties required by the model were calibrated with field-based measurements. Visual and numerical comparison between NASA JPL Airborne SAR data and the model simulation suggests the effective modeling of SAR imagery at L-band. The study provides a proof-of-concept approach for integrating LiDAR data in the parameterization of coherent SAR simulation models, and the model presents options for better understanding of the information content of SAR data in real forest situations. [ABSTRACT FROM AUTHOR]

We analyze the generalization of a parametric segmentation technique adapted to Gamma-distributed synthetic aperture radar (SAR) images to nonparametric noise models. This approach is based on a polygonal grid which can have an arbitrary topology and whose number of regions and regularity of its boundaries are obtained by minimizing the stochastic complexity of a quantified version on Q levels of the image. It thus leads to a criterion without parameters to be tuned by the user and adapted to different noise models. We analyze the influence of the quantization scheme and of the optimization procedure on the quality of the partitioning. We then compare the performance of the proposed approach to the parametric one on synthetic images. Finally, we show results obtained on real images and compared with a standard segmentation algorithm of SAR images. [ABSTRACT FROM AUTHOR]

Coherence magnitude is a fundamental parameter for the analysis of applications using interferometric synthetic aperture radar (InSAR). The coherence magnitude estimators are biased and need bias removal. The sample coherence magnitude estimation, computed on a window basis, depends on the number of independent samples and theoretical coherence. It has been shown that the sample coherence magnitude estimator is the maximum-likelihood one. It is a biased estimator, especially for low coherence values. In this paper, we present a novel coherence magnitude estimator obtained from the method of moments using "second kind statistics." Classical methods (with regular statistics) for coherence computation are based on a probability density function (pdf) model for estimating regular moments (first kind statistics) defined with the Fourier transform. The proposed approach is based on the same pdf model to compute the second kind statistics defined with the Mellin transform (log-moment). Thus, it is shown that the estimated coherence given by the first log-moment is less biased. Moreover, it is shown that the coherence magnitude estimation from complex coherence maps (interferometric data) using second kind statistics is the optimal estimation procedure of interferometric coherence. It gives the smallest bias near zero comparing with existing estimators. The developed estimation approaches have been applied to obtain coherence measurements from tandem European Remote Sensing 1 and 2 satellite interferometric data, collected over varying terrain with a variety of ground cover types (agriculture field, forest, lake, urban area, sea) in Tunisia, France, and Nepal. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to change detection in multitemporal synthetic aperture radar (SAR) images. The proposed approach exploits a wavelet-based multiscale decomposition of the log-ratio image (obtained by a comparison of the original multitemporal data) aimed at achieving different scales (levels) of representation of the change signal. Each scale is characterized by a different tradeoff between speckle reduction and preservation of geometrical details. For each pixel, a subset of reliable scales is identified on the basis of a local statistic measure applied to scale-dependent log-ratio images. The final change- detection result is obtained according to an adaptive scale-driven fusion algorithm. Experimental results obtained on multitemporal SAR images acquired by the ERS-1 satellite confirm the effective- ness of the proposed approach. [ABSTRACT FROM AUTHOR]

We propose a two-layer integral equation model (IEM) model including multiple-scattering terms to reproduce the phase signature of buried wet structures that we observed on L-band synthetic aperture radar (SAR) images. We have good agreement between the extended (single+multiple scattering) IEM model and previous results obtained using a single-scattering IEM model combined with finite-difference time-domain simulations. We show that the multiple scattering not only significantly influences the copolarized phase difference but can also be related to the soil moisture content. In order to assess the validity of our extended model, we performed radar measurements on a natural outdoor site and showed that they could be fairly well fitted to the extended model. A parametric analysis presents the dependence of the copolarized phase difference on roughness parameters (rms height and correlation length) and radar parameters (frequency and incidence angle). Our study also shows that the phase signature should allow detection of buried wet structures down to a larger depth for C-band (3.8 m) than for L-band (2.6 m). This signature could then be used to map subsurface moisture in arid regions using polarimetric SAR systems. [ABSTRACT FROM AUTHOR]

The Shuttle Radar Topography Mission (SRTM) provided data for detailed topographical maps of about 80% of the Earth's land surface. SRTM consisted of single-pass C- and X-band interferometric synthetic aperture radars (INSARs). In order to utilize SRTM data in remote sensing applications the data must be calibrated and validated. This paper presents The University of Michigan's SRTM calibration and validation campaign and our results using recently acquired C-band SRTM data of our calibration sites. An array of calibration targets was deployed with the intention of determining the accuracy of INSAR-derived digital elevation maps. The array spanned one of the X-band swaths and stretched from Toledo, OH to Lansing, MI. Passive and active targets were used. The passive targets included trihedrals and tophats. The locations in latitude, longitude, and elevation of the point targets were determined using differential GPS. We also acquired U.S. Geological Survey (USGS) digital elevation models (DEMs) to use in the calibration and validation work. The SRTM data used in this study are both Principal Investigator Processor (PI) data, which are not the refined final data product, and the ground data processing system (GDPS) data, which are a more refined data product. We report that both datasets for southeastern Michigan exceed the SRTM mission specifications for absolute and relative height errors for our point targets. A more extensive analysis of the SRTM GDPS data indicates that it meets the absolute and relative accuracy requirements even for bare surface areas. In addition, we validate the PI height error files, which are used to provide a statistical characterization of the difference between the SRTM GDPS and USGS DEM heights. The statistical characterization of the GDPS-USGS difference is of interest in forest parameter retrieval algorithms. [ABSTRACT FROM AUTHOR]

This paper is comment on “Orientation Angle Preserving A Posteriori Polarimetric SAR Calibration”. [ABSTRACT FROM AUTHOR]