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Synthetic aperture sonar and synthetic aperture radar involve the creation of high-resolution images of a scene via scattered signals recorded at different locations. Each pixel of the reconstructed image includes information obtained from multiple aspects due to the changing position of the sources/receivers. In this paper, the aspect-dependent scattering at each pixel is exploited to provide additional information about the scene; this paper presents a framework for converting and utilizing multiaspect data, as well as several examples. For sonar data, as is presented here, the aspect dependence may be leveraged to separate objects of interest from the background, to understand the local bathymetry, or for visualizing acoustic shadowing in full circular synthetic aperture sonar images. Several examples of images of the seafloor containing objects of interest are presented for both circular and linear apertures. In addition, the aspect dependence of low-frequency elastic scattering from objects may be used to understand the underlying scattering physics, which is of potential use in fields such as target recognition and nondestructive testing; a laboratory example is presented. [ABSTRACT FROM AUTHOR]

Tandem-L is proposed as a spaceborne synthetic aperture radar (SAR) mission developed and operated by the German Aerospace Center in cooperation with several Helmholtz research centers and the German space industries. The mission concept comprises two fully polarimetric radar satellites providing monostatic and bistatic SAR imagery. A key feature of these SAR sensors is the employment of large lightweight unfurlable mesh reflectors fed by digital feed arrays. The main advantage of this new SAR system concept is the provision of large antenna apertures in space and flexible operation via reconfigurable feed electronics. By this, it becomes possible to map, for the first time, a continuous 350-km wide swath with a 7-m azimuth resolution with excellent noise equivalent sigma zero and ambiguity suppression. This paper shall give an overview on the technical aspects of the Tandem-L SAR instrument and antenna design. In particular, after a short review of the SAR system requirements, the concept of reflector SAR systems is outlined and the operation principle is presented. General guidelines for the design of array-fed reflector antennas with application to SAR imaging are given. Then, the optimization approach of the feed array design is detailed with a specific emphasis on a fixed beamforming concept in azimuth. In this context, also the problem of cross-pol pattern mitigation is addressed. These optimization steps are shown to be crucial for achieving the performance requirements in quad-pol acquisitions. Beamforming in elevation is performed onboard the spacecraft via digital hardware. This paper presents the beamforming architecture on receive for Tandem-L, which would apply in general for instance also to planar multielevation beam SAR antennas with Scan-On-Receive capabilities. Tandem-L is operated as a staggered SAR, which means varying the pulse repetition interval from pulse to pulse. In this context, the major design challenges are presented. Moreover, the impact of pulse staggering on the imaging performance is discussed. Tandem-L’s SAR performance is presented by means of numerical simulations showing that the performance requirements imposed by the scientific user community could be met. The final part of this paper addresses options for high azimuth resolution imaging as well as a beamforming method for enhanced range ambiguity suppression. [ABSTRACT FROM AUTHOR]

This paper addresses the performance in the retrieval of 3-D mean deformation maps by exploiting simultaneous or quasi-simultaneous squinted synthetic aperture radar (SAR) interferometric acquisitions in a repeat-pass scenario. In multisatellite or multibeam low earth observation missions, the availability of two (or more) lines of sight (LOSs) allows the simultaneous acquisition of SAR images with different squint angles, hence improving the sensitivity to the north–south component of the deformation. Due to the simultaneity of the acquisitions, the troposphere will be highly correlated and, therefore, will tend to cancel out when performing the differential measurement between the interferograms obtained with the different LOSs, hence resulting in a practically troposphere-free estimation of the along-track deformation measurement. In practice, however, the atmospheric noise in the differential measurement will increase for increasing angular separations. This paper expounds the mathematical framework to derive the performance by properly considering the correlation of the atmospheric delays between the simultaneous acquisitions. To that aim, the hybrid Cramér–Rao bound is exploited making use of the autocorrelation function of the troposphere. Some performance examples are presented in the frame of future spaceborne SAR missions at C and L band. [ABSTRACT FROM AUTHOR]

In this paper, with respect to multichannel synthetic aperture radar (SAR), we first formulate the problems of Doppler ambiguities on the radial velocity (RV) estimation of a ground moving target in the range-compressed domain, the range-Doppler domain, and the image domain, respectively. It is revealed that in these problems, the cascaded time-space Doppler ambiguity (CTSDA) may arise; that is, the time domain Doppler ambiguity in each channel arises first and then the spatial domain Doppler ambiguity among multichannels arises second. Accordingly, the multichannel SAR systems with different parameters are investigated in three cases with different Doppler ambiguity properties. Then, a multifrequency SAR is proposed for the RV estimation by solving the ambiguity problem based on the Chinese remainder theorem (CRT). In the first two cases, the ambiguity problem can be solved by the existing closed-form robust CRT. In the third case, it is found that the problem is different from the conventional CRT problem and we call it a double remaindering problem in this paper. We then propose a sufficient condition under which the double remaindering problem, i.e., the CTSDA, can also be solved by the closed-form robust CRT. When the sufficient condition is not satisfied, a searching-based method is proposed. Finally, some results of numerical experiments are provided to demonstrate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

In recent years, synthetic aperture radar interferometry has become a recognized geodetic tool for observing ground motion. For monitoring areas with low density of coherent targets, artificial corner reflectors (CRs) are usually introduced. The required size of a reflector depends on radar wavelength and resolution and on the required deformation accuracy. CRs have been traditionally used to provide a high signal-to-clutter ratio (SCR). However, large dimensions can make the reflector bulky, difficult to install and maintain. Furthermore, if a large number of reflectors are needed for long infrastructure, such as vegetation-covered dikes, the total price of the reflectors can become unaffordable. On the other hand, small reflectors have the advantage of easy installation and low cost. In this paper, we design and study the use of small reflectors with low SCR for ground motion monitoring. In addition, we propose a new closed-form expression to estimate the interferometric phase precision of resolution cells containing a (strong or weak) point target and a clutter. Through experiments, we demonstrate that the small reflectors can also deliver displacement estimates with an accuracy of a few millimeters. To achieve this, we apply a filtering method for reducing clutter noise. [ABSTRACT FROM PUBLISHER]

This paper presents a whole new method for simulating a raw signal of synthetic aperture radar (SAR) with trajectory deviation. In this paper, a SAR raw signal is viewed as a spatial function in terms of radar location, at a fixed range frequency component of the radar signal. It is disclosed and verified that the SAR raw signals is band limited from the perspective of spatial spectrum analysis. Based on this finding, SAR raw signals under condition of trajectory deviation are obtained by interpolating the SAR raw signal along a set of ideally linear trajectories. The unprecedent performance achieved by the new method mainly includes the following. First, it provides a way to update existing simulators designed for ideally linear trajectory, giving them the capability to take trajectory deviation into consideration. Second, it is an efficient SAR raw signal simulator whose validity limit is greatly expanded compared with existing efficient simulators, as many constraints are eliminated without sacrificing computational efficiency or phase accuracy. Third, it is a uniform framework applicable for multiple SAR acquisition modes. Finally, the computational complexity of Monte Carlo experiment under various trajectory deviations is dramatically reduced, at the expense of storage memory requirement. Theoretical analyses are demonstrated by experimental results. [ABSTRACT FROM PUBLISHER]

The National Aeronautics and Space Administration’s Soil Moisture Active Passive (SMAP) mission, launched in January 2015, was designed to provide a global soil moisture product at medium resolution (~9 km), by combining observations from its radar and radiometer. Several downscaling methods have been proposed by the SMAP team for this purpose. This paper evaluates another candidate downscaling method, namely, the Bayesian merging approach. While this has been tested using a synthetic data set across the USA, it is imperative that it can also be tested using the experimental data for a comprehensive range of land surface conditions (i.e., in different hydro-climatic regions) prior to a global application. Consequently, this paper applies this method using the data collected from SMAP experiments field campaigns in south-eastern Australia that closely simulated the SMAP data stream for a single SMAP radiometer pixel over a three-week interval. The method studied here differs from the linear downscaling methods of the SMAP mission, in that it uses a nonlinear method based on Bayes’ theorem. The medium-resolution soil moisture product is obtained using background soil moisture estimates that are updated according to the difference between the observed and predicted brightness temperatures and backscatter coefficients, relating the high- and low-resolution data. Results were assessed against a reference soil moisture map derived from high-resolution airborne radiometer observations. The root-mean-square-error and R^2 for the Bayesian merging method were found to be 0.02 cm3/cm3 and 0.55, respectively, at 9-km resolution, being similar to the SMAP’s “optional” downscaling method. [ABSTRACT FROM PUBLISHER]

With three coherent synthetic aperture radar images, it is possible to form three interferograms. In some cases, the phases of the three averaged interferograms will be significantly inconsistent and indicate a sort of phase excess or deficit (which we call lack of triangularity or inconsistency). In this paper, we illustrate theoretically which models can explain such phenomenon and provide some real-data examples. It is also shown that two or more independent scattering mechanisms are necessary to explain phase inconsistencies. The observation of lack of consistency might be useful to derive information on the target and as a warning that the scatterer presents a temporal covariance matrix, which is not intrinsically real, with consequences for the processing of interferometric stacks. [ABSTRACT FROM PUBLISHER]

This paper proposes a complex-valued convolutional neural network for land form classification and discovery in interferometric synthetic aperture radar (InSAR). Since the amount of satellite-borne SAR data has been increasing drastically, it is necessary to structurize the local features contained in observation data prior to utilization in the so-called big data framework for higher usability. Convolutional neural networks have such potential in general. However, there exists no network that can deal with complex amplitude data obtained in InSAR consistently. In this paper, we propose a complex-valued convolutional neural network to deal with InSAR. We demonstrate that the network classifies slopes and plains adaptively and, moreover, indicates small volcanos similar to a sample volcano (Omuroyama) included in the InSAR data. We also find their characteristic features emerging in the kernels in the convolution layers. These results reveal that the proposed complex-valued convolutional neural network is capable of successfully discovering unidentified lands similar to a prepared sample, which is highly useful for the InSAR data structurization. [ABSTRACT FROM AUTHOR]

Capon’s minimum-variance method (MVM) and amplitude and phase estimation (APES) spectral estimation algorithms can be applied to synthetic aperture radar (SAR) processing to improve the resolution and suppress sidelobe levels. In this paper, we use Capon-/APES-based SAR reprocessing algorithms to increase the persistent scatterer (PS) density in PS interferometry (PSI). We propose a PS candidate (PSC) selection algorithm applicable to the superresolution reprocessed images and the corresponding processing chain. The performance of the proposed algorithm is evaluated by a number of simulations and a stack of TerraSAR-X data. The results show that the Capon algorithm outperforms others in PSC selection. We present a full PSI time-series analysis on the PSCs extracted from the Capon-reprocessed stacks. The results show that the PS density is increased between 50% and 60%, while their interferometric quality is maintained. [ABSTRACT FROM AUTHOR]

Polarimetric synthetic aperture radar (SAR) data are inevitably contaminated by polarization crosstalk and channel imbalance, which propagate to the error of final remote sensing product. To ensure the successful estimation of forest heights from forthcoming polarimetric SAR interferometry (PolinSAR) campaigns, a critical study on the polarimetric system requirements of PolinSAR for forest height mapping must be carried out. This paper establishes an analytical model for forest height estimation error including dependences on polarimetric system parameters including crosstalk, channel imbalance, and system noise. Simulation analyses are conducted on the real airborne SAR data acquired by the E-SAR system to validate the proposed theoretical error dependence model. We demonstrate that the established error model can be used not only by the system designers as a guidance for setting the polarimetric system requirements of PolinSAR for forest height mapping, but also by the data analyst to correct for systematic bias in the forest height inversion. [ABSTRACT FROM AUTHOR]

This paper proposes a new scheme of motion parameter estimation and relocation for airborne three-channel circular stripmap synthetic aperture radar (CSSAR)-ground moving target indication (GMTI) systems. Compared with the conventional straight-path SAR, the parameter estimation of a target is more challenging because the target’s range history and signal model are more complicated due to the complexity of the relative motion between CSSAR and ground moving target. In this paper, the signal model of a ground moving target and the expression for its along-track interferometric (ATI) phase from the environment of airborne three-channel CSSAR are derived. The coupling effect among the target’s motion and position parameters is also figured out. Then, a scheme of motion parameter estimation and relocation is proposed. The proposed scheme utilizes the ATI phase and the quadratic-term coefficient in the range equation to estimate the target’s motion and position parameters and utilizes an iterative strategy to address the coupling effect among these parameters. Numerical simulations are conducted to validate the satisfactory performance achieved by the proposed algorithm. [ABSTRACT FROM AUTHOR]

In most of the existing ground penetrating radar (GPR) imaging algorithms, using either full or sparse data collection, the ground is modeled as a single half-space layer. In this paper, a generalized sparse imaging approach with total variation minimization (TVM) for multiple-input multiple-output (MIMO) GPR imaging through multilayered subsurface is proposed. The multilayered media Green’s function is incorporated in the imaging algorithm to take into account the complex wave propagation effects under multilayered subsurface. An analytical expression of the layered subsurface Green’s function is derived using the stationary-phase method, which significantly reduces the computation time and complexity. On the other hand, as TVM minimizes the gradient of the image, its incorporation in the imaging algorithm results in a reconstruction that preserves the geometry and edges of the targets better than the standard L1-minimization-based sparsity-driven imaging. The number of antenna elements and frequency measurements in MIMO GPR system can be significantly reduced using the proposed technique without degradation of the image quality. Although MIMO configuration is investigated in this paper, the presented approach can be simply applied to monostatic synthetic aperture radar. [ABSTRACT FROM AUTHOR]

Seabed characterization has utility for numerous applications that seek to explore and interact with the seafloor, ranging from coastal habitat monitoring and subbottom profiling to man-made object detection. In this paper, we characterize seabeds based on the texture patterns within synthetic aperture SONAR (SAS) images constructed from high-frequency side-scan sonar. Features are measured from the SAS images (e.g., lacunarity, an established texture feature coding method, and a circularly shifted histogram of oriented gradients). Based on these SAS image features, we perform unsupervised clustering with a hierarchical Bayesian model, which creates categories of seabed textures. Our clustering algorithm is a new variant of the hierarchical Dirichlet process that is both adaptive to changes in seabeds and processes batches of SAS imagery in an online fashion to learn new seabed types as they are encountered. This allows observations to be clustered, as each batch is processed rather than only after all data have been collected. The model’s performance of seabed characterization by SAS image texture is demonstrated in the overall range and internal consistency of textures specific to each learned cluster with data across a variety of sites. [ABSTRACT FROM AUTHOR]

This paper focuses on the detection, from a stack of repeated-pass interferometric synthetic aperture radar (SAR) images, of such changes causing a target to completely lose the correlation between one epoch and another. This can be the consequence of human activities, such as construction, destruction, and agricultural activities, and also be the consequence of hazards, such as earthquake, landslides, or flooding, to buildings or terrains. The millimetric sensitivity of SAR makes it valuable for detecting such changes. This paper approaches two coherent change detection methods: a space coherent, time incoherent one and a full space and time coherent one, both based on the generalized likelihood ratiob (LR) test. A preliminary validation of the method is provided by processing two Sentinel-1 data stacks of 2016 Central Italy earthquake and by comparing the results with the map of damaged buildings in Amatrice and Accumoli made by Copernicus Emergency Management Service. [ABSTRACT FROM AUTHOR]

This paper deals with the compensation of phase miscalibration in the general context of tomographic synthetic aperture radar image focusing. Phase errors are typically independent of one acquisition to the other, thus leading to a spreading and defocusing in the multidimensional (3-D, 4-D, and 5-D) imaging space. Coping with this problem in presence of volumetric scattering is generally a complex issue. In this paper, we consider the approach for phase calibration characterized by the advantage, with respect to classical phase calibration algorithms, of not requiring either the identification of a reference target or specific assumptions about the unknown phase function, or a priori information about the terrain topography. The novelty of the proposed phase miscalibration estimation and compensation method is related to its ability to avoid unwanted and uncontrollable vertical shifts in the focused image. The estimation of the calibration phase is performed by optimizing the contrast or the entropy of the vertical profile with the constraint of a zero phase derivative. Such a constraint preserves the output height distribution. Experimental results of simulated and real data are included to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper critically analyzes several incoherent model-based decomposition methods for assessing the effect of deorientation in characterization of various land covers. It has been found that even after performing decomposition, ambiguity still occurs in scattering response from various land covers, such as urban and vegetation. Researchers introduced the concept of deorientation to remove this ambiguity. Therefore, in this paper, a critical analysis has been carried out using seven different three- and four-component decomposition methods with and without deorientation and two Eigen decomposition-based methods to investigate the scattering response on various land covers, such as urban, vegetation, bare soil, and water. The comprehensive evaluation of decomposition and deorientation effect has been performed by both visual and quantitative analyses. Two types of quantitative analysis have been performed; first, by observing percentage of scattering power and second, by analyzing the variation in the number of pixels in different land covers for each scattering contribution. The analysis shows that deorientation increases not only the power but also the number of pixels for surface and double bounce scattering. The number of pixels representing volume scattering remain almost the same for all the methods with or without deorientation, whereas volume scattering power reduces after deorientation. Eigen decomposition-based methods are observed to solve the problem of overestimation of volume scattering power. [ABSTRACT FROM PUBLISHER]

Sea ice concentration (SIC) is an important sea ice parameter for sea ice navigation, environmental research, and weather and ice forecasting. We have developed and tested a method for estimation of the Baltic Sea SIC using SENTINEL-1 synthetic aperture radar (SAR) and Advanced Microwave Scanning Radiometer 2 passive microwave radiometer (MWR) data. Here, we present the method and results for January 2016. Ice concentration grids of Finnish Meteorological Institute daily ice charts have been used as reference data in this paper. We present a comparison of four SIC estimation methods with our reference data. In addition to the combined SAR/MWR SIC estimation method, we also compare SIC estimates produced using SAR alone and two MWR-based methods. The main target of this paper was to develop and test a high-resolution SIC estimation method suitable for operational use. [ABSTRACT FROM PUBLISHER]

The availability of accurate trajectory information is paramount for the processing and exploitation of synthetic aperture radar (SAR) data. Considering the particular case of spaceborne SARs designed for repeat-pass interferometric applications, errors in the trajectory translate into phase artifacts that affect the interferometric performance. In this paper, we propose a model-based procedure to calibrate the trajectories of spaceborne SAR systems by the multisquint (MS) phase. The technique allows to estimate the along and the derivative of across track geometric errors. The geometric model of the InSAR phase is derived as a function of positioning errors and the MS phase model as derivative of the InSAR phase geometric model, with respect to the squint angle. We perform a sensitivity analysis of the model in order to define which geometric errors can be estimated by the MS phase, justifying the assumption that the MS phase is very poorly affected by the atmospheric phase screen. We particularly concentrate on the TOPSAR acquisition mode, where the phase is very sensitive to geometric errors. We start from the classical two-image case and then consider the extension to the multibaseline case. Experimental results obtained by processing of interferometric pairs acquired by the Sentinel-1A sensor are reported. [ABSTRACT FROM PUBLISHER]

This paper describes the Naval Research Laboratory Multichannel Synthetic Aperture Radar (NRL MSAR) and presents initial results from the first field deployment of this system. The NRL MSAR is an airborne test bed designed to investigate remote sensing and surveillance applications that exploit multiple along-track phase centers, particularly applications that require measurement of scene motion. The system operates at X-band and supports 32 along-track phase centers through the use of two transmit horns and 16 receive antennas. As illustrated in this paper, SAR images generated with these phase centers can be coherently combined to directly measure scene motion using the velocity SAR algorithm, and these measurements can then be used to correct the image distortion that the motion causes. In September 2014, this unique radar was deployed for the first time on an airborne platform. This paper presents a description of the system and results from its inaugural deployment, including the correction of distorted maritime signatures. These data were collected over an ocean inlet and contain a variety of moving backscatter sources, including automobiles, ships, shoaling ocean waves, and tidal currents. [ABSTRACT FROM PUBLISHER]

In this paper, we present a new framework for the fusion, representation, and analysis of multitemporal synthetic aperture radar (SAR) data. It leads to the definition of a new class of products representing an intermediate level between the classic Level-1 and Level-2 products. The proposed Level-1 $\beta$ products are particularly oriented toward nonexpert users. In fact, their principal characteristics are the interpretability and the suitability to be processed with standard algorithms. The main innovation of this paper is the design of a suitable RGB representation of data aiming to enhance the information content of the time-series. The physical rationale of the products is presented through examples, in which we show their robustness with respect to sensor, acquisition mode, and geographic area. A discussion about the suitability of the proposed products with Sentinel-1 imagery is also provided, showing the full compatibility with data acquired by the new European Space Agency sensor. Finally, we propose two applications based on the use of Kohonen's self-organizing maps dealing with classification problems. [ABSTRACT FROM PUBLISHER]

Monitoring the sea ice cover is important for both climate studies and ice operations, such as shipping. It is challenging to validate even basic essential variables, such as the sea ice extent, due to a lack of appropriate validation data. Instead of focusing on validation, this paper looks at the use of categorical triple collocation (CTC) for the task of quantitatively comparing three colocated data sets. CTC has been developed and used in earlier studies to rank binary data sets. In this paper, we extend earlier studies and bring in recent results from the binary classification community to estimate the class imbalance (the relative proportion of each class, ice or water). We then use this class imbalance to obtain quantitative estimates of the proportion correct of ice (sensitivity) and the proportion correct of water (specificity). The methodology is first tested using toy data, after which three data sets from the Gulf of Saint Lawrence, on the east coast of Canada, are used. These data sets are from an ice–ocean model, a passive microwave sea ice concentration retrieval, and a sea ice concentration retrieval from synthetic aperture radar (SAR). By looking at both the sensitivity and the specificity, it is found that the passive microwave data have difficulty in recognizing ice during freeze-up, but they perform well at obtaining the correct water observations. This distinction cannot be made by ranking the data sets. The CTC method is compared with, and found to be complementary to, a validation using ice/water states from the interactive multisensor snow and ice mapping system. [ABSTRACT FROM AUTHOR]

This paper investigates new approaches to estimating the motion of the dynamic ocean surface using a multi-channel synthetic aperture radar (MSAR) with $M$ phase centers arranged in an along-track configuration. The objective of this paper is to determine the processing methods that produce the finest velocity resolution, an issue that arises due to the finite coherence time of radar backscatter produced by the sea surface. The investigation is carried out both theoretically, using synthesized data produced from a modeled MSAR covariance matrix, as well as experimentally, using images collected with a 16-channel system. Three processing methods are considered: linear regression along with a multi-baseline phase progression, estimation of the velocity centroid, and coherent averaging of the shortest baseline interferograms. Both the theoretical and experimental results indicate that simple averaging of the shortest-baseline interferograms often produces the best velocity precision. [ABSTRACT FROM AUTHOR]

Similar to the conventional squint mode synthetic aperture radar (SAR) imaging processing, the estimation of Doppler centroid is one key problem for the low-squint-mode (within the range of [−5°, 5°]) multichannel in an azimuth high-resolution and wide-swath (HRWS) SAR system. In this paper, based on the electromagnetic scattering model, a novel weighted Doppler centroid estimation approach is proposed for the multichannel in the azimuth HRWS (MC-HRWS) SAR system. First, Maxwell’s equations are employed to derive the echo model for the multichannel SAR system. Then, an improved backscattering model is presented based on the small perturbation method and the available echo model, which is adopted to produce the weights for Doppler centroid estimation. More importantly, in order to improve the precision of Doppler centroid estimation, a weighted ambiguity-free Doppler centroid estimation approach is proposed, where the range-variant characteristic of Doppler centroid is employed to resolve the ambiguity number of Doppler centroid, and the echoes from different range bins with different signal-to-noise ratios are considered. In addition, the Cramer–Rao low bound of the estimated Doppler centroid is also discussed in this paper. The effectiveness of the proposed Doppler centroid estimation approach is verified via simulated and real measured low-squint-mode MC-HRWS SAR data. [ABSTRACT FROM AUTHOR]

Permanent scatterer interferometry is a multitemporal interferometric synthetic aperture radar technique that produces high-accuracy ground deformation measurement. A high density of permanent scatterer (PS) is required to provide accurate results. In natural environments with low PS density, distributed scatterers (DSs) could serve as additional coherent observations. This paper introduces a polarimetric scattering property-based adaptive filtering method that preserves PS candidates and filters DS candidates. To further increase the coherence estimate of DS candidates, the technique includes a complex coherence decomposition that adaptively selects the most stable scattering mechanisms, thus improving pixel coherence estimation. The proposed method was evaluated on 11 quad-polarized ALOS PALSAR images and 21 dual-polarized Sentinel-1 images acquired over San Fernando Valley, CA, USA, and Groningen, The Netherlands, respectively. The application of this method increased the number of coherent pixels by almost a factor of eight compared with a single-polarization channel. This paper concludes that a coherence estimate can be significantly improved by applying scattering property-based adaptive filtering and coherence matrix decomposition and accurate displacement measurements can be achieved. [ABSTRACT FROM AUTHOR]

Focusing on open forests and woodlands within the Injune Landscape Collaborative Project research area in central southeast Queensland, Australia, and using dual-pol (HH and HV) ALOS PALSAR repeat-pass InSAR data (temporal baseline of 92 days), this paper explores the detection of forest disturbance from the spaceborne repeat-pass InSAR correlation magnitude by developing a simple and efficient forest disturbance detection approach. In particular, a generic physical InSAR scattering model is derived by accounting for the forest disturbance information as well as the normal temporal decorrelation effects that are later compensated for using the modified Random Volume over Ground model. Based on the generic model, a quantitative indicator of forest disturbance is retrieved, namely, disturbance index that varies from 0 (no disturbance) to 1 (complete deforestation). This index is compared with that identified using a time series of Landsat sensor data over a selective logging area and has a relative root mean square error of 13% at a spatial resolution of 0.8 ha. This paper highlights the use of the co-pol InSAR correlation magnitude for forest disturbance detection, which serves as a complimentary application to using the cross-pol counterpart for forest height inversion in a companion work. Given the global availability of this type of data (e.g., Japanese Aerospace Exploration Agency’s ALOS-1/2 and NASA-ISRO’s NISAR), the method is anticipated to contribute to the range of tools being developed for large-scale forest disturbance assessment and monitoring. [ABSTRACT FROM AUTHOR]

The AisaOWL thermal hyperspectral instrument, manufactured by Specim, is a relatively new push-broom sensor well suited to airborne environmental surveys. The sensor covers the 7.6-12.6 μm part of the long-wave infrared region with 102 continuous bands, and is capable of imaging in low-light conditions. The detector array is a mercury cadmium telluride (MCT) semiconductor, which has an inherent randomly varying dark current for random pixels. This manifests in the raw data as a pixel switching between different intensity levels. These pixels are termed "blinkers" by the manufacturer. For each data acquisition, the pixels need to be tested for blinking behavior as different pixels are affected during each acquisition. However, little is known about the number of blink events, the duration of frames, or the optimal length of data acquisition. This paper presents the characterization of the blinking nature of pixels in the MCT detector array to provide guidance on data acquisition and processing. This paper finds that blinking behavior is not completely random, with some pixels more prone to blinking behavior than others. Most blinking pixels have only a few short blinks; therefore, there is still a considerable amount of good data in a blinking pixel. [ABSTRACT FROM AUTHOR]

Multiple-input multiple-output synthetic aperture radar (MIMO SAR) system has drawn considerable attention because of its extra degrees of freedom for high resolution and wide swath compared with the traditional multichannel SAR system. But how to extract the matched signal without the unmatched interferences is the foremost task for MIMO SAR system. In this paper, by using the orthogonal frequency division multiplexing chirp signals as the transmitted signals, it is demonstrated that the robust principal component analysis (RPCA) method can be successfully employed for ground moving target indication (GMTI) with no need for separating the matched signal and unmatched interferences. It is because the unmatched interference is proven to have low-rank property and noise-level magnitude, which can be separated apart from the matched signal with the RPCA method. However, the traditional RPCA methods may be restricted by the high computational burden due to the complex decompositions and multiple iterations. Hence, a fast interferometry RPCA method is proposed specially for GMTI mode, which takes full advantage of the characteristics of along-track interferometry SAR system. It can improve the probability of detection under low signal-to-clutter-and-noise ratio. Additionally, it will dramatically shorten the computational time. Furthermore, the proposed method can also estimate the radial velocities of the moving targets simultaneously. The results by applying the proposed method into a set of real SAR data are consistent with the analysis presented in this paper. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a novel multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) concept for multimodal operation. The proposed system employs waveforms based on the orthogonal frequency division multiplexing (OFDM) technique and digital beamforming (DBF) on receive. Thereby, it becomes feasible to maximize spatial degrees of freedom, which are necessary for the multimodal operation. The proposed MIMO SAR system produces multiple high-resolution wide-swath SAR imageries that are used for coherent postprocessing. Through this paper, we aim to open up a new perspective of using MIMO concept for a wide-swath SAR imaging with high resolution in interferometric and polarimetric modes, based on OFDM and DBF techniques. Therefore, this paper encompasses broad theoretical backgrounds and general system design issues as well as specific signal processing techniques and aspects. [ABSTRACT FROM AUTHOR]

The field of wireless communications has benefited from multiple-input and multiple-output (MIMO) techniques. As researchers seek to apply MIMO (multistatic) techniques to radar and specifically to synthetic aperture radar (SAR), a key factor in determining MIMO application and performance is the level of correlation of signals from different receiver/transmitter pairs. The level of correlation determines whether a MIMO array falls into the category of a collocated array or a distributed array. The type of array dramatically affects which MIMO techniques may be performed and what advantages MIMO offers from conventional techniques. This paper presents models for calculating geometric correlation of multistatic SAR pixels using a ground-plane image formation. The models' results are compared to previous correlation models found in literature. A key result is that correlation depends on pixel resolution and not the number of individual scatterers. This paper concludes that most MIMO arrays operating on a single platform operate in the collocated regime. [ABSTRACT FROM PUBLISHER]

This paper deals with the issue of ground moving target imaging and motion parameter estimation with an airborne dual-channel circular stripmap synthetic aperture radar (CSSAR) system. Although several methods of ground moving target motion parameter estimation have been proposed for the conventional airborne linear stripmap SAR, they cannot be applied to airborne CSSAR because the range history of a ground moving target for airborne CSSAR is different than that for airborne linear stripmap SAR. In this paper, the moving target’s range history for airborne dual-channel CSSAR and the target signal model after the displaced phase center antenna processing are derived, and a new ground moving target imaging and motion parameter estimation algorithm is developed. In this algorithm, the estimation of baseband Doppler centroid and its compensation are first performed. Then focusing is implemented in the 2-D frequency domain via phase multiplication, and the target is focused in the SAR image without azimuth displacement due to the compensation of the Doppler shift caused by its motion. Finally, the target’s motion parameters are estimated with its Doppler parameters and its position in the SAR image. Numerical simulations are conducted to validate the derived range history and the performance of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

Automatic registration of multimodal remote sensing data [e.g., optical, light detection and ranging (LiDAR), and synthetic aperture radar (SAR)] is a challenging task due to the significant nonlinear radiometric differences between these data. To address this problem, this paper proposes a novel feature descriptor named the histogram of orientated phase congruency (HOPC), which is based on the structural properties of images. Furthermore, a similarity metric named HOPCncc is defined, which uses the normalized correlation coefficient (NCC) of the HOPC descriptors for multimodal registration. In the definition of the proposed similarity metric, we first extend the phase congruency model to generate its orientation representation and use the extended model to build HOPCncc. Then, a fast template matching scheme for this metric is designed to detect the control points between images. The proposed HOPCncc aims to capture the structural similarity between images and has been tested with a variety of optical, LiDAR, SAR, and map data. The results show that HOPCncc is robust against complex nonlinear radiometric differences and outperforms the state-of-the-art similarities metrics (i.e., NCC and mutual information) in matching performance. Moreover, a robust registration method is also proposed in this paper based on HOPCncc, which is evaluated using six pairs of multimodal remote sensing images. The experimental results demonstrate the effectiveness of the proposed method for multimodal image registration. [ABSTRACT FROM PUBLISHER]

Quadratic forms play an important role in the development of several polarimetric and interferometric synthetic aperture radar (Pol-InSAR) methodologies, which are very powerful tools for earth observation. This paper investigates integrals of Pol-InSAR operators based on quadratic forms, with special interest on the Pol-InSAR coherence. A new operator, namely Trace Coherence, is introduced, which provides an approximation for the center of mass of the coherence region (CoRe). The latter is the locus of points on the polar plot containing all the possible coherence values. Such center of mass can be calculated as the integral of Pol-InSAR coherences over the scattering mechanisms (SMs). The trace coherence provides synthetic information regarding the partial target as one single entity. Therefore, it provides a representation, which is not dependent on the selection of one specific polarization channel. It may find application in change detection (e.g., coherent change detection and differential DEM), classification (e.g., building structure parameters), and modeling (e.g., for the retrieval of forest height). In calculating the integral of the Pol-InSAR coherences, an approximate trace coherence expression is derived and shown to improve the calculation speed by several orders of magnitude. The trace coherence approximation is investigated using Monte Carlo simulations and validated ESA (DLR) L-band quad-polarimetric data acquired during the AGRISAR 2006 campaign. The result of the analysis using simulated and real data is that the average error in approximating the integral of the coherence region is 0.025 in magnitude and 3° in phase (in scenarios with sufficiently high coherence). [ABSTRACT FROM PUBLISHER]

Interferograms obtained by interferometric synthetic aperture radar generally include many singular points (SPs) originating from interference distortion and noise in the measurement. The filtering process is one of the key techniques in the generation of an accurate digital elevation model (DEM). In this paper, we propose a filter to remove SPs and related distortion using a complex-valued neural network in the spectral domain. It removes SPs nonlinearly and adaptively by referring to the statistics in neighboring windows in the 2-D frequency domain, where the textural features are represented in a more continuous manner than in the real space domain. Experiments demonstrate that the proposed method removes SPs and the distortion in SP-constructing four pixels, namely, the singular unit, more effectively than the conventional filters, resulting in the generation of a more accurate DEM. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the concept, develop the theory, and demonstrate the advantages of fully focused coherent processing of pulse echoes from a nadir-looking pulse-limited radar altimeter. This process, similar to synthetic aperture radar (SAR) imaging systems, reduces the along-track resolution down to the theoretical limit equal to half the antenna length. We call this the fully focused SAR (FF-SAR) altimetry processing. The technique is directly applicable to SAR altimetry missions such as CryoSat-2, Sentinel-3, or Sentinel-6/Jason-CS. The footprint of an FF-SAR altimeter measurement is a narrow strip on the surface, which is pulse limited across track and SAR focused along track. Despite the asymmetry of the altimeter footprint, the fully focused technique may be useful for applications in which one needs to separate specific targets within highly heterogeneous scenes, such as in the case of sea ice lead detection, hydrology, and coastal altimetry applications. In addition, over rough homogeneous surfaces, such as the ocean or ice sheets, the improved multilooking capability of FF-SAR leads to a significant increase in the effective number of looks with respect to the delay/Doppler processing, resulting in better geophysical parameter estimation. The proposed processing technique was verified by processing full bit rate CryoSat-2 SAR mode data over the Svalbard transponder. Hydrology, sea ice, and open ocean applications are also demonstrated in this paper, showing the improvement of this technique with respect to conventional and delay/Doppler altimetry. [ABSTRACT FROM PUBLISHER]

Frequency-modulation continuous-wave (FMCW) synthetic-aperture radar (SAR) can minimize the peak transmission power of sensors and reduce the size and weight of the systems. Wavenumber domain algorithm (WDA) is an accurate focusing method for SAR imaging. By using the exact signal form to compensate the phase error, WDA can achieve exact scene recovery from high-squint and long aperture data as long as the platform velocity is stable. In this paper, we introduce WDA to FMCW SAR and discuss the WDA-based FMCW SAR sparse imaging method. There are two main contributions of this paper: 1) a motion compensation-based WDA imaging method is introduced to correct the motion error that is often associated in practical airborne FMCW SAR data and 2) a novel WDA-based FMCW SAR sparse imaging method is developed to further improve the performance of recovered image. Compared with the typical WDA algorithm, the sparse imaging method can suppress the noise and sidelobes and perform the sparse scene recovery from the downsampled data. Experimental results via simulated and real airborne data verify the presented WDA-based FMCW SAR imaging methods. [ABSTRACT FROM AUTHOR]

Although the first filtering algorithms have been proposed more than 30 years ago, despeckling of synthetic aperture radar images is still an open issue. A new boost has been provided by nonlocal (NL) means filters. The idea of NL filters is to move from the exploitation of spatial neighboring pixels to the exploitation of similar pixels found across the image. The difference between the NL algorithms is mainly related to the definition of the similarity between pixels and how similar pixels are exploited in the restoration process. Generally, to define the similarity, the patches are adopted. In this paper, a new similarity criterion for selecting similar pixels is presented. It is based on the definition of the ratio patch between the patch containing the pixel to be restored and the patch containing a candidate similar pixel. If the two pixels are similar, it is expected that the corresponding ratio patch will follow a specific statistical distribution. A modified version of the Kolmogorov–Smirnov distance is introduced to decide whether the statistical distribution of the ratio patch follows the expected one. To reduce the possible artifacts, anisotropy is exploited. Considering the proposed approach, the designed algorithm turns to be unbiased, able to provide the restored solution without any thresholding procedure, in which the tuning is substantially unsupervised and able to work with both single-look and multilook images. The algorithm has been tested on different simulated and real data. Qualitative and quantitative analyses validate the proposed approach, showing very good despeckling capabilities. [ABSTRACT FROM AUTHOR]

Due to the coherent processing of synthetic aperture radar (SAR) systems, multiplicative speckle noise arises providing a granular appearance in SAR images. This kind of noise makes it difficult to analyze and interpret surface images from the earth. Therefore, studying alternatives to attenuate the speckle is a constant task in the image processing literature. Current state-of-the-art filters in remote sensing area explore the philosophy of similarity between patches. This paper aims to expand the traditional nonlocal means (NLM) algorithm originally proposed for the additive white Gaussian noise (AWGN) to deal with the speckle. In our research, we consider the worst scenario, i.e., the single-look speckle noise, and apply the NLM to filter intensity SAR images in the Haar wavelet domain. To accomplish this task, the Haar coefficients were described by exponential-polynomial (EP) and gamma distributions. Furthermore, stochastic distances based on these two mentioned distributions were derived and embedded in the NLM filter by replacing the Euclidean distance of the original method. This represents the main contribution of the proposed research. Finally, this paper analyzes and compares the synthetic and real experiments of the proposed method with some recent filters of the literature demonstrating its competitive performance. [ABSTRACT FROM AUTHOR]

Multibaseline (MB) synthetic aperture radar interferometry (InSAR) has the potential to improve the traditional single-baseline InSAR from the ill-posed problem to the well-posed problem. It is because MB InSAR phase unwrapping (PU) can take advantage of the baseline diversity to significantly increase the ambiguity intervals of interferometric phases, so it completely overcomes the limitation of the phase continuity assumption. However, due to the mathematical foundation of most of the MB PU methods, i.e., the Chinese remainder theorem (CRT), has poor measurement bias robustness (measurement bias could be caused by surface deformation, atmospheric artifact, and phase noise), CRT is sensitive to the normal baseline length. In other words, even if we choose the same CRT-based MB PU method, different system baseline lengths could result in different PU performances. Therefore, how to choose the baseline lengths to optimize CRT performance is crucial to all the CRT-based MB PU methods. In this paper, a nonlinear mixed-integer programming-based baseline design criterion (referred to NIP criterion) is proposed to maximize the measurement bias tolerance of the CRT-based MB PU. The optimality condition of the NIP criterion quantitatively provides important instructions for the CRT-based MB PU applications and offers significant guidance in the development of the practical MB InSAR system. The experiment results are shown to verify the effectiveness of the NIP criterion by using three representative CRT-based MB PU methods. [ABSTRACT FROM AUTHOR]

This paper deals with precise absolute geolocation of point targets by means of a pair of high-resolution synthetic aperture radar (SAR) acquisitions, acquired from a satellite. Even though a single SAR image is a 2-D projection of the backscatter, some 3-D information can be extracted from a defocussing analysis, depending on the resolution, thanks to orbital curvature. A second acquisition, observing the same scene under a different look angle, adds stereogrammetric capability and can achieve geolocation accuracy at decimeter level. However, for the stereogrammetric analysis to work, it is necessary to match targets correctly in the two images. This task is particularly difficult if it has to be automatic and targets are dense. Unfortunately, the defocussing-based geolocation is not sufficient for reliable target matching: the limiting factor is the unknown tropospheric delay that can cause geolocation errors of several meters in the elevation direction. However, observing that the tropospheric phase screen displays a low-pass character, this paper shows how to identify statistically the local atmospheric disturbances, therefore dramatically improving the score of successful matching. All steps involved exploit peculiar radar image characteristics and, thanks to this, avoid generic point cloud matching algorithms. The proposed algorithm is shown at work on a pair of TerraSAR-X staring spotlight images. [ABSTRACT FROM AUTHOR]

Space–time adaptive processing (STAP) of multichannel radar data is an established and powerful method for detecting ground moving targets, as well as for estimating their geographical positions and line-of-sight velocities. Crucial steps for practical applications are: 1) the appropriate and automatic selection of the training data and 2) the periodic update of these data to take into account the change of the clutter statistics over space and time. Improper training data and contamination by moving target signals may result in a decreased clutter suppression performance, an incorrect constant false alarm rate threshold, and target cancelation by self-whitening. In this paper, two conventional and two novel methods for training data selection are evaluated and compared using real four-channel X-band radar data acquired with DLR’s airborne sensor F-SAR. In addition, a module for rejecting potential moving target signals and strong scatterers from the training data is proposed and discussed. All methods are evaluated for a conventional post-Doppler (PD) STAP processor and for a particular PD STAP that uses an a priori known road map. [ABSTRACT FROM AUTHOR]

A novel physical paradigm is explored in this paper: synthetic-aperture radar (SAR) ocean speckle is informative. This paper experimentally analyzes the SAR ocean speckle intensity K-distribution model versus sea-surface wind field. It is shown that the normalized intensity moments of the K-distribution measured from actual C-band SAR data well fit the theoretical ones under different wind conditions, i.e., wind regime and relative SAR-wind azimuth direction. In addition, it is observed that the K-distribution shape parameter decreases as wind speed increases. A sensitivity of the K-distribution shape parameter on incidence angle and wind direction is also observed, where the largest variability is experienced at higher incidence angles and under crosswind relative azimuth angle. [ABSTRACT FROM AUTHOR]

Frequency-modulated continuous-wave (FMCW) synthetic aperture radar (SAR) is now a commonly adopted solution for producing high-resolution electromagnetic images of the earth surface. However, many SAR applications, such as detection or imaging of moving targets, have not yet been thoroughly researched for FMCW scenarios. This paper presents a refocusing technique that works in the wavenumber domain and is based on the reference spectrum of a point-like moving target. The method is illustrated using simulated experiments, and is further tested by processing real data collected by the Ka-band MIRANDA-35 sensor. The obtained results are validated and compared with independent ground measurements, thus demonstrating the accuracy of the presented method. [ABSTRACT FROM AUTHOR]

In this paper, we discuss the application of polarimetric target decomposition (TD) theorems to circular synthetic aperture radar (CSAR) images to retrieve the polarimetric azimuthal angular characteristics of the targets. In CSAR systems, radar-carrying aircraft moves along a circular path. The antenna beam is pointed toward the center of the circular path during the data acquisition to irradiate the spotlighted area from various azimuthal or aspect angles. Therefore, the resultant polarimetric CSAR images contain information about the polarimetric scattering mechanisms of the targets at each aspect angles. To recover this information, we propose the use of spectral decomposition of CSAR images in combination with several TD algorithms. The spectral domain CSAR images can be viewed as the azimuthal angular spectrum images. Thus, the application of TD algorithms to the spectral domain images is expected to be able to obtain the angular dependence and polarimetric scattering properties of the targets simultaneously. We carried out a simple numerical simulation and an indoor laboratory experiment to validate the proposed TD scheme. [ABSTRACT FROM AUTHOR]

In this paper, we revisit the pulse-to-pulse correlation properties of nadir-looking pulse-limited altimeters, with the objective of determining the effect of the partial correlation of radar echoes transmitted at much higher rate than the conventional pulse repetition frequency (PRF). This is particularly relevant for the Sentinel-6/Jason-CS mission. The pulse-to-pulse echo power autocorrelation shows much shorter decorrelation times toward the trailing edge of the waveform than those observed for range gates close to the leading edge. At high PRFs this creates a significant variability in the statistical properties of the range gates in the 20-Hz multilooked waveforms. By processing an extensive data set of CryoSat-2 Synthetic Aperture Radar mode data in a pseudo-low resolution mode fashion, we determined that despite the fact that at higher PRFs the noise in the estimation of geophysical parameters is reduced, significant sea-state-dependent biases are also introduced during the retracking process, which are particularly relevant for sea surface height and significant wave height. Those biases will need to be appropriately accounted for when integrating Sentinel-6/Jason-CS data in a climatological data record. [ABSTRACT FROM AUTHOR]

This paper discusses an image processing architecture and tools to address the problem of ship detection in synthetic-aperture radar images. The detection strategy relies on a tree-based representation of images, here a Maxtree, and graph signal processing tools. Radiometric as well as geometric attributes are evaluated and associated with the Maxtree nodes. They form graph attribute signals which are processed with graph filters. The goal of this filtering step is to exploit the correlation existing between attribute values on neighboring tree nodes. Considering that trees are specific graphs where the connectivity toward ancestors and descendants may have a different meaning, we analyze several linear, nonlinear, and morphological filtering strategies. Beside graph filters, two new filtering notions emerge from this analysis: tree and branch filters. Finally, we discuss a ship detection architecture that involves graph signal filters and machine learning tools. This architecture demonstrates the interest of applying graph signal processing tools on the tree-based representation of images and of going beyond classical graph filters. The resulting approach significantly outperforms state-of-the-art algorithms. Finally, a MATLAB toolbox allowing users to experiment with the tools discussed in this paper on Maxtree or Mintree has been created and made public. [ABSTRACT FROM AUTHOR]

Interferograms obtained by synthetic aperture radar often include many singular points (SPs), which makes it difficult to generate an accurate digital elevation model. This paper proposes a filtering method to compensate SPs adaptively by using polarization and phase information around the SPs. Phase value is essentially related to polarization changes in scattering as well as propagation. In order to handle the polarization and phase information simultaneously in a consistent manner, we define a new number, phasor quaternion (PQ), by combining quaternion and complex amplitude, with which we construct the theory of PQ neural networks (PQNNs). Experiments demonstrate that the proposed PQNN filter compensates SPs very effectively. Even in the situations where the conventional methods deteriorate in their performance, it realizes accurate compensation, thanks to its good generalization characteristics in integrated Poincare-sphere polarization space and the complex-amplitude space. We find that PQNN is an excellent framework to deal with the polarization and phase of electromagnetic wave adaptively and consistently. [ABSTRACT FROM AUTHOR]

This paper examines the attainable performance of various Doppler sublook or subband cross correlation techniques for vessel detection in synthetic aperture radar images. Research from the past two decades claims that these techniques were capable of improving the detection of small ships in challenging maritime environments. Despite many published experimental examples, a thorough analytical investigation corroborating this claim is noticeably absent. This paper is based on a rigorous theoretical analysis founded on the statistical properties of a textured sea surface model in thermal noise with simultaneous consideration of a constant false alarm rate. Emphasis has been placed on the correct accounting for detrimental physical effects caused by the Doppler spectrum being split into nonoverlapping parts, which have not been sufficiently considered in the literature to date. The theoretical results are confirmed via simulations and are substantiated with real RADARSAT-2 data. The analysis in this paper has neither found theoretical nor empirical evidence that sublook correlation techniques outperform the classical detector based on the image magnitude. [ABSTRACT FROM AUTHOR]

This paper evaluates the capability of Sentinel-1 VH-polarized synthetic aperture radar signals, involving 738 scenes in the interferometric wide swath (IW) mode, for ocean surface wind speed retrieval using a novel subswath-based C-band cross-polarized ocean model. When compared with in situ measurements, it is observed that wind speed retrieval accuracy varies progressively along swath, with the most accurate wind speed retrievals being derived from subswath 3 [root-mean-square error (RMSE) of $1.82\,\,\text {m}\cdot \mathrm {s}^{-1}$ ], followed by subswath 2 (RMSE of $1.92\,\,\text {m}\cdot \mathrm {s}^{-1}$), while subswath 1 showed the lowest retrieval accuracy (RMSE of $2.37\,\,\text {m}\,\cdot \,\mathrm {s}^{-1}$). The average RMSE of wind speeds retrieved from all the three subswaths is $2.08\,\,\text {m}\,\cdot \,\mathrm {s}^{-1}$ under low-to-high wind speed regimes (wind speeds $< 25\,\,\text {m}\,\cdot \,\mathrm {s}^{-1}$). We further observed that the dependence of VH-polarized normalized radar cross section (NRCS) on incidence angle is attributable to the high and changing noise equivalent sigma zero (NESZ) with incidence angle under low-to-moderate wind speed regimes. And that strong VH-polarized radar signals could overcome the NESZ effect, thereby eliminating the dependence of VH-polarized NRCS on incidence angle under strong wind conditions. For Sentinel-1 IW mode VH-polarized data, the effect of NESZ could be ignored when wind speeds are greater than $15\,\,\text {m}\cdot \mathrm {s}^{-1}$ , as a better wind speed retrieval performance of these data has been recorded in this paper at wind speeds greater than $10\,\,\text {m}\cdot \mathrm {s}^{-1}$ , owing to an RMSE below $1.6\,\,\text {m}\cdot \mathrm {s}^{-1}$ and biases ranging from −0.5 to $0.5\,\,\text {m}\cdot \mathrm {s}^{-1}$. [ABSTRACT FROM AUTHOR]

Images obtained from coherent illumination processes are contaminated with speckle. A prominent example of such imagery systems is the polarimetric synthetic aperture radar (PolSAR). For such a remote sensing tool, the speckle interference pattern appears in the form of a positive-definite Hermitian matrix, which requires specialized models and makes change detection a hard task. The scaled complex Wishart distribution is a widely used model for PolSAR images. Such a distribution is defined by two parameters: the number of looks and the complex covariance matrix. The last parameter contains all the necessary information to characterize the backscattered data, and thus, identifying changes in a sequence of images can be formulated as a problem of verifying whether the complex covariance matrices differ at two or more takes. This paper proposes a comparison between a classical change detection method based on the likelihood ratio and three statistical methods that depend on information-theoretic measures: the Kullback–Leibler (KL) distance and two entropies. The performance of these four tests was quantified in terms of their sample test powers and sizes using simulated data. The tests are then applied to actual PolSAR data. The results provide evidence that tests based on entropies may outperform those based on the KL distance and likelihood ratio statistics. [ABSTRACT FROM AUTHOR]