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In this paper, a feature extraction technique based on the electromagnetic (EM) representation of radar signals is presented. In particular, we focus on ground-penetrating radar (GPR) imaging, where we model the backscatter from varying 2-D geometric shapes with arbitrary local coordinate rotations. Due to the electrically small nature of buried targets and the bending of the radar signal at the air–soil interface, we focus on exact methods to model the surface current density induced on scattering surfaces. Overcomplete basis sets are derived from the EM descriptions to represent the scene sparsely. From this proposed modeling framework, we devise a novel methodology to exploit the prediction of scattering behavior to extract features for classification from radar scenes when multiple buried scattering surfaces are present. We see that our method can identify and reconstruct buried scattering geometries in the presence of false targets that are brought about by the nonlinear nature of the exact EM modeling methods. A noniterative algorithm based on the conjugate of Green’s function is developed to solve for the surface current in an unknown domain using multifrequency, multiaperture data. Our modeling and feature extraction algorithms are numerically validated for different target shapes buried in lossy soil profiles. [ABSTRACT FROM AUTHOR]

Polarimetric whitening filter (PWF) can be used to filter polarimetric synthetic aperture radar (PolSAR) images to improve the contrast between ships and sea clutter background. For this reason, the output of the filter can be used to detect ships. This paper deals with the setting of the threshold over PolSAR images filtered by the PWF. Two parameter-constant false alarm rate (2P-CFAR) is a common detection method used on whitened polarimetric images. It assumes that the probability density function (PDF) of the filtered image intensity is characterized by a log-normal distribution. However, this assumption does not always hold. In this paper, we propose a systemic analytical framework for CFAR algorithms based on PWF or multi-look PWF (MPWF). The framework covers the entire log-cumulants space in terms of the textural distributions in the product model, including the constant, gamma, inverse gamma, Fisher, beta, inverse beta, and generalized gamma distributions ($\text{G}\Gamma $ Ds). We derive the analytical forms of the PDF for each of the textural distributions and the probability of false alarm (PFA). Finally, the threshold is derived by fixing the false alarm rate (FAR). Experimental results using both the simulated and real data demonstrate that the derived expressions and CFAR algorithms are valid and robust. [ABSTRACT FROM AUTHOR]

Nominal grid data of geostationary remote sensing satellites are fundamental for generating the subsequent products. It can be obtained by normalized projection models, mainly based on the imaging mode. However, there are only definitions and primary descriptive equations for the normalized geostationary projection (NGP) model in the existing literature, while the corresponding imaging mode and the physical interpretation are missing, thus hindering the understanding of the produced nominal grid dataset as well as the subsequent products based on the grid. This paper first derived the imaging mode for NGP based on the limited literature. In addition, another new imaging mode was introduced and analyzed based on NGP. The corresponding projection model [nonstandard normalized geostationary projection (NNGP)] was proposed, which is entirely consistent with the situation of America’s Geostationary Operational Environmental Satellite-R Series (GOES-R) and Chinese Fengyun-4A (FY-4A). Furthermore, this paper proposed a novel nominal projection model for frame imaging, which is consistent with the imaging mode of China’s Gaofen-4. Finally, extensive experiments were designed to comparatively analyze the three nominal grids and demonstrate a detailed difference. By providing a theoretical basis for nominal grid selection, this research is highly significant for the efficient near-real-time production and further applications of geostationary images, as well as the conversion between different datasets resulting from different nominal grid data. In addition, our models are sufficiently tested during the on-orbit running of FY-4A, the first satellite of China’s second-generation three-axis stabilized geostationary meteorological satellite series. The algorithms provide the technical support for the high-precision image navigation and registration and play a significant role in robustly producing the meteorological data with similar quality to those from GOES-R. [ABSTRACT FROM AUTHOR]

Nowadays, in the electromagnetism environment, the complex interferences, including the narrowband interferences (NBIs) and wideband interferences (WBIs), may severely affect the imaging quality of synthetic aperture radar (SAR) systems. Most traditional methods can only tackle with one kind of isolated interferences, NBIs or WBIs, which are widely distributed in the 1-D range frequency domain or 2-D range time–frequency domain. In this paper, we propose a complex tensor robust principal component analysis (CT-RPCA) method based on a novel 3-D range-azimuth-space tensor model to mitigate continuously distributed NBIs and WBIs simultaneously. The main contributions of this paper are summarized in three aspects. First, we strictly prove the low-rank property of the isolated NBIs and WBIs in the range-azimuth domain. Second, we use multiple views of the signal to construct a novel 3-D range-azimuth-space tensor model, where both the NBI tensor and the WBI tensor have spatial low-rank property due to the approximately stable frequency bands along the spatial dimension. Third, the CT-RPCA method is employed to efficiently suppress NBIs and WBIs simultaneously by solving the tensor RPCA problem. Finally, the real SAR data with simulated complex interferences are employed to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Canopy height models (CHMs) were utilized to detect treetops and estimate individual-tree parameters. The treetop detection based on CHMs was affected by surface topography and crown characteristics. However, their effects have not been well studied. Therefore, this paper aimed at assessing the impacts of aforementioned factors to facilitate treetop identification from LiDAR-derived CHMs. To fulfill this objective, we first extended and improved the previous models for cases with various terrains. Then, a new theoretical model was developed to quantify treetop displacements for ellipsoidal tree crowns. Finally, we further analyzed the treetop displacements due to terrain slope, crown radius, crown shape, and offset distance to the slope surface. Our analysis indicates that the vertical displacement increases exponentially with terrain slope; thus, the effect of terrain slope must be considered over extremely steep areas; larger errors are observed for trees with a large crown radius; the treetop displacements are highly correlated with crown shape, the effect of topographic normalization can be neglected for conical crowns with a large crown angle, and the elliptical crown shape can reduce the treetop detection errors; and treetop displacements increases with offset distance to the slope surface in , while opposite results are observed in. In addition, the results also demonstrate that the effect of slope-distorted CHMs may be quite different for different types of tree crowns and terrains. Overall, this paper makes a significant contribution to the development of theoretical models for quantifying treetop displacements. Furthermore, our findings provide a theoretical basis and guidance for better identifying treetops from LiDAR-derived CHMs. [ABSTRACT FROM AUTHOR]

This paper proposes a phase-derived velocity measurement (PDVM) method for high-speed targets based on wideband direct sampling linear frequency modulated radar. First, a high-speed target echo model considering intrapulse Doppler modulation is developed. Then, a PDVM model considering acceleration is established. The key to realizing PDVM is resolving phase ambiguity. Under low signal-to-noise ratio (SNR) conditions, a joint processing method combining acceleration information and multiframe data to solve phase ambiguity is proposed, which can significantly reduce the SNR requirement for PDVM. In this paper, the small-amplitude micromotion measurement capability of the proposed method is verified by simulation. Moreover, the measured data of a Ku-band ground-based radar are used to verify the applicability of the PDVM method under low SNR conditions and its feasibility to be applied to complex multi-scattering point targets. Both the simulation and experimental results show that the proposed method is suitable for high-speed targets with radial motion, including acceleration and jerk, and that the PDVM precision can reach the order of magnitude of centimeters per second or millimeters per second. [ABSTRACT FROM AUTHOR]

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]

Full-waveform inversion (FWI) in the time domain of ground-penetrating radar (GPR) data involves a vast number of calculations; thus, it requires a large amount of memory and is difficult to calculate on a personal computer (PC). In this paper, GPR data are analyzed with multiscale FWI using two parameters (permittivity and conductivity) based on total variation (TV) regularization, which is implemented on a PC using a graphics processing unit (GPU) parallel acceleration strategy. The inverse problem is considered to be a nonlinear optimization problem and is solved with limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) process, which is a quasi-Newton method. The gradient of the objective function is calculated using the adjoint-state method, and the finite-difference method is required to solve the forward problem many times. A multiscale serial inversion strategy is applied to optimize the inversion algorithm and to decompose the inversion problem into 2–3 frequency bands to search in the direction of the global minimum point instead of local minimums. Taking the complex model as an example, experiments are carried out to assess the parameter adjustment factor and regularization parameter. The appropriate parameter adjustment factor and regularization parameter can effectively guarantee the convergence speed and stability of dual-parameter inversion method and improve the accuracy of GPR data inversion. Finally, FWI of the noise-free and 25-dB signal-to-noise ratio (SNR) noise data of the overthrust model is performed. The results show that the multiscale and dual-parameter inversion method proposed in this paper can provide reliable constraints, has better adaptability to noisy data, and can reliably and accurately reconstruct the dielectric properties distribution of the subsurface. [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]

The Visible Infrared Imaging Radiometer Suite (VIIRS) sensor aboard the NOAA-20 (previously JPSS-1) spacecraft has successfully operated since its launch in November, 2017. Similar to the first VIIRS instrument on the Suomi-National Polar-orbiting Partnership (SNPP) spacecraft, the data are collected in 22 spectral bands that are calibrated by a set of onboard calibrators. This paper provides an overview of the NOAA-20 VIIRS on-orbit operation and calibration, with a particular focus on the thermal emissive bands (TEBs). The results presented in this paper include the on-orbit changes in the TEB spectral band responses, detector noise characterization, and key calibration parameters, such as the nonlinear coefficients derived from the blackbody warm-up cool-down cycles. Other issues, such as the early mission long-wave infrared (LWIR) response degradation due to icing on the dewar window, and their impact on sensor calibration are also discussed. Since launch, the VIIRS instrument temperature has been stable to within ±0.8 K and the cold focal plane temperatures are well controlled with variations less than 40 mK. With the exception of the early degradation observed in the LWIR bands, the TEB gains have been stable to within 0.04% (except I5 at 0.07%). Based on the current performance, VIIRS is expected to meet its calibration requirements throughout its design lifetime. [ABSTRACT FROM AUTHOR]

Compared with frequency-domain algorithms, time-domain algorithms (TDAs) can achieve image focusing under the conditions of arbitrary trajectories and large integration angles. However, the interpolations in both range and bearing angle directions are required in the coordinate transformation of fast TDAs, which inevitably increases the computational load and introduces interpolation errors. In this paper, a fast time-domain imaging and corresponding autofocus method based on the hybrid coordinate (HC) system is proposed. First, the interpolation operation is optimized in the process of fast TDAs. It transforms the 2-D interpolation into 1-D interpolation only in the bearing angle direction, which improves the execution efficiency and reduces the interpolation error. Next, a 3-D trajectory deviation estimation method based on Gauss–Newton iteration is investigated for the motion compensation in the HC system. By iterative optimization, the 3-D motion errors during the flight are estimated accurately, and the space-variant phase error is compensated precisely. This method has good robustness and universality. Simulation results and real data processing demonstrate the effectiveness and the practicability of the method presented in this paper. [ABSTRACT FROM AUTHOR]

The main challenging problems in ocean current retrieval from along-track interferometric (ATI)-synthetic aperture radar (SAR) are phase calibration and wave bias removal. In this paper, a method based on differential InSAR (DInSAR) technique for correcting the phase offset and its variation is proposed. The wave bias removal is assessed using two different Doppler models and two different wind sources. In addition to the wind provided by an atmospheric model, the wind speed used for wave correction in this work is extracted from the calibrated SAR backscatter. This demonstrates that current retrieval from ATI-SAR can be completed independently of atmospheric models. The retrieved currents, from four TanDEM-X (TDX) acquisitions over the Öresund channel in the Baltic Sea, are compared to a regional ocean circulation model. It is shown that by applying the proposed phase correction and wave bias removal, a good agreement in spatial variation and current direction is achieved. The residual bias, between the ocean model and the current retrievals, varies between 0.013 and 0.3 m/s depending on the Doppler model and wind source used for wave correction. This paper shows that using SAR as a source of wind speed reduces the bias and root-mean-squared-error (RMSE) of the retrieved currents by 20% and 15%, respectively. Finally, the sensitivity of the sea current retrieval to Doppler model and wind errors are discussed. [ABSTRACT FROM AUTHOR]

Convolutional neural network (CNN)-based methods have been successfully applied to SAR target detection. Different from prevalently used detection approaches with rectangle bounding box, rotatable bounding box (RBox)-based methods, such as DRBox-v1, can effectively reduce the interference of background pixels and locate the targets more finely for geospatial object detection. Although DRBox-v1 has achieved impressive detected performance, there still exist some remaining problems and room for improvement. In this paper, an improved RBox-based target detection framework is proposed to boost precision and recall rates of detection, and we refer to the method as DRBox-v2 and apply it to target detection in SAR images. The main improvements of DRBox-v2 as well as the contributions of this paper are fourfold. First, a multi-layer prior box generation strategy is designed for detecting small-scale targets. Since shallow layers lack strong sematic information, the feature pyramid network (FPN) module is applied. Second, a modified encoding scheme for RBox is proposed for more precisely estimating the position of RBox and orientation of targets. Third, a focal loss (FL) combined with hard negative mining (HNM) technique is proposed to mitigate the issue of the imbalance between positive and negative samples, which produces better results than solely employing either one. Fourth, comprehensive ablation studies are conducted to reveal the effect of each improvement on detected results. The results of the target detection on three data sets are illustrated and our method obtains 0.135, 0.081, 0.115 gains in average precision compared with three state-of-the-art methods, respectively. [ABSTRACT FROM AUTHOR]

A nonlinear optimization algorithm has been described for the inversion of gravity data profile by a faulted 2-D horizontal thin block. The algorithm simultaneously optimizes for the depth to the center ($z$) of the faulted block, the amount and direction of dip ($\theta $) of the fault plane, and the amplitude coefficient ($A$), which is dependent on the amount of throw $t$ and the density contrast $\Delta \rho $ of the block. The objective functional of this algorithm coupled with both the space of logarithmed absolute values of the observed and predicted gravity data and the space of logarithmed [ $\log (z)$ , $\log (|A|)$ , and $\log (\theta $)] model parameters is the basis of the new inversion scheme introduced here. It has been found essential that the objective functional of this scheme be formulated in this particular combination so that the iterative solver/minimizer (the Gauss–Newton (GN) method) can converge. The developed scheme has been successfully verified on numerical models without noise and achieved superior convergence. It is found stable and can determine the inverse parameters of the faulted block with acceptable accuracy when applied to data contaminated with insignificant noise levels and/or geologic interference. In order to investigate the usefulness of the developed scheme, a published gravity profile has been inverted and analyzed, suggesting new results that are of some geologic significance. The computational efficiency, thorough analysis of the investigated numerical examples, and comparisons of the real data inverted in this paper have demonstrated that the scheme developed here is advantageous to the existing gravity data inversion schemes that solve for the characteristic inverse parameters of a faulted thin block. [ABSTRACT FROM AUTHOR]

The nonstationary and non-Gaussian distribution of the high-resolution (HR) synthetic aperture radar (SAR) image provides much valuable information. However, the current methods, especially deep learning models, directly learn spatial features from HR SAR data while ignoring global statistical information. Combining the local spatial features and global statistical properties of HR SAR images is urgently needed to capture complete HR SAR characteristics. In this paper, a feature fusion network (Fusion-Net) using both deep spatial context encoder and nonstationary joint statistical model (NS-JSM) is proposed for the first time. Fusion-Net realizes the fusion description of local spatial and global statistical features in an end-to-end supervised classification framework. First, a deep spatial context encoder network (DSCEN) is designed based on multiscale group convolution (MSGC) module and channel attention (CA) module. The DSCEN expands the scope of context information extraction with few parameters and increases the interaction between high- level feature channels. Then, the NS-JSM is adopted to capture the unique SAR statistical information. Specifically, the SAR image is transformed into the Gabor wavelet domain. The produced sub-band magnitudes and phases are modeled by the log-normal and uniform distribution. The covariance matrix (CM) is calculated for mapped sub-band data to capture the interscale and intrascale nonstationary correlation. Finally, the group compression and smooth normalization units are introduced into Fusion-Net to fuse the statistical features and spatial features, which not only exploits the complementary information between different features but also optimizes the fusion feature representation. Experiments on four real HR SAR images validate the superiority of the proposed method over other related algorithms. [ABSTRACT FROM AUTHOR]

Most convolutional neural network (CNN)-based cloud detection methods are built upon the supervised learning framework that requires a large number of pixel-level labels. However, it is expensive and time-consuming to manually annotate pixelwise labels for massive remote sensing images. To reduce the labeling cost, we propose an unsupervised domain adaptation (UDA) approach to generalize the model trained on labeled images of source satellite to unlabeled images of the target satellite. To effectively address the domain shift problem on cross-satellite images, we develop a novel UDA method based on grouped features alignment (GFA) and entropy minimization (EM) to extract domain-invariant representations to improve the cloud detection accuracy of cross-satellite images. The proposed UDA method is evaluated on “Landsat- $8~\rightarrow $ ZY-3” and “GF- $1\rightarrow $ ZY-3” domain adaptation tasks. Experimental results demonstrate the effectiveness of our method against existing state-of-the-art UDA approaches. The code of this paper has been made available online (https://github.com/nkszjx/grouped-features-alignment). [ABSTRACT FROM AUTHOR]

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]

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]

The orbit of the tropical rainfall measuring mission (TRMM) satellite was boosted from 350 to 402.5 km in August 2001 to extend its lifetime by conserving fuel. Since the timing between transmission and reception by the precipitation radar (PR) onboard the TRMM satellite was a fixed constant for measurement from an original altitude of 350 km, the PR encountered a mismatch between the transmitting and receiving beams after the TRMM orbit boost. Although the PR algorithm in TRMM Version 7 employs a correction for the beam mismatch, its error remains as an underestimation of the precipitation estimates near surface in the second half of the swath. This paper aims to mitigate the beam-mismatch correction error in Version 7. The beam-mismatch correction needs to estimate the radar echo that would be measured in the virtual intermediate beam between the beam in question and the previous adjacent beam. The beam-mismatch correction developed in this paper assumes that the surface and precipitation echoes change linearly in the horizontal direction in parallel to the surface between the two beams. The new correction is tested with observational data, indicating that the method improves the accuracy of the correction at off-nadir angles. Statistics of the surface normalized radar cross sections and the bright band peak intensities at off-nadir angles are improved using the method. The asymmetric bias of the precipitation estimates with respect to the scan angle in Version 7 is mitigated by 95.9% over ocean and 72.5% over land with the new correction. [ABSTRACT FROM AUTHOR]

In this paper, we consider the development of algorithms for the automatic detection of buried threats using ground penetrating radar (GPR) measurements. GPR is one of the most studied and successful modalities for automatic buried threat detection (BTD), and a large variety of BTD algorithms have been proposed for it. Despite this, large-scale comparisons of GPR-based BTD algorithms are rare in the literature. In this paper, we report the results of a multi-institutional effort to develop advanced BTD algorithms for a real-world GPR BTD system. The effort involved five institutions with substantial experience with the development of GPR-based BTD algorithms. In this paper, we report the technical details of the advanced algorithms submitted by each institution, representing their latest technical advances, and many state-of-the-art GPR-based BTD algorithms. We also report the results of evaluating the algorithms from each institution on the large experimental data set used for development. The experimental data set comprised 120 000 m2 of GPR data using surface area, from 13 different lanes across two U.S. test sites. The data were collected using a vehicle-mounted GPR system, the variants of which have supplied data for numerous publications. Using these results, we identify the most successful and common processing strategies among the submitted algorithms, and make recommendations for GPR-based BTD algorithm design. [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]

Studies pertaining to small scale structures producing scintillations using synthetic aperture radars (SARs) have predominantly been conducted at low-latitude regions. The high-latitude region (auroral belt and polar caps) is highly dynamic and varies in response to stimuli from solar winds and the magnetosphere in complex ways. In this paper, the authors have shown the capability of SAR for scintillation observation in the auroral region. An attempt has been made to fit an irregularity anisotropy model to SAR measurements for characterizing the ionospheric irregularities in the auroral regions. The dependency of anisotropy irregularity model on parameters, such as irregularity structure (axial ratio), their orientation with respect to magnetic field lines, and the ionospheric plasma drift, is closely studied using Advanced Land Observing Satellite (ALOS)-2 datasets acquired over Alaska. Geomagnetic indices and total electron content data are consistent with the occurrence of the scintillation event under study. Drift velocity measurements from high-frequency radars in the super dual array radar network (SuperDARN) showed that the anisotropy is independent of the magnitude and the azimuth angle of the plasma drift. The typical range of orientation angle suitable for the high latitude regions probed by ALOS-2 is demonstrated to be between 120°–135°. This paper explores the idea of inferring irregularity anisotropy by comparing the amplitude scintillation (S4) index measured in SAR data pairs using two well-established techniques. The image contrast technique heavily relies on the accurate modeling of anisotropy, whereas the radar cross-sectional enhancement method is independent of it. This feature has been exploited in the $S_{4}$ comparison to finally fit the choice of irregularity axial ratio and conclude that the sheet-like structures best describe the ionospheric irregularity structure in the region under observation. [ABSTRACT FROM AUTHOR]

Interferogram filtering for noise reduction is a key to many radar interferometric applications. Repeat pass radar interferometry often uses data with less than ideal correlation levels resulting from either long spatial or temporal baselines or changes between observations leading to high levels of temporal correlation. To maximize the utility of such pairs filtering the interferogram to get maximal noise reduction is often needed. One technique that has proved quite useful in the geophysical community is power spectral or Goldstein–Werner filtering of the interferogram whereby a power-weighted version of the Fourier transform is used to enhance fringe visibility. Although this paper defining the filter briefly touched upon the spatial resolution and noise reduction induced by the filter, it did not provide a useful formula for predicting the phase noise after filtering. This paper derives a formula for the phase noise obtained from power spectral filtering albeit under the restriction of several simplifying assumptions to make the problem analytically tractable. In particular, it is assumed that the interferometric phase is locally well approximated by a linear phase ramp with nonlinear phase perturbations small in a spectral energy sense compared to the linear term. [ABSTRACT FROM AUTHOR]

This paper focuses on the blind unmixing technique for analyzing hyperspectral images (HSIs). A joint deconvolution and blind hyperspectral unmixing (DBHU) algorithm is proposed, which is aimed at eliminating the impact of the adjacency effect (AE) on unmixing. In remote sensing imagery, the AE occurs in the presence of atmospheric scattering over a heterogeneous surface. The AE leads to blurring and additional mixing of HSIs and makes it difficult to estimate endmembers and abundances accurately. In this paper, we first model the blurred HSIs by the use of a bilinear mixing model (BMM), where a blurring kernel is used to model the mixing caused by the AE. Based on the BMM, the DBHU problem is formulated as a constrained and biconvex optimization problem. Specifically, the minimum-volume simplex (MVS) is incorporated to deal with the additional mixing caused by the AE, and 3-D total variation (TV) priors are adopted to model the spectral–spatial correlation of the data. In DBHU, the biconvex problem is efficiently solved by a nonstandard application of the alternating direction method of multipliers (ADMM) algorithm, where a block coordinate descent scheme is applied by splitting the original problem into two saddle point subproblems, and then minimizing the subproblems alternately via the ADMM until convergence. The experimental results obtained with both simulated and real data confirm the viability of the proposed algorithm, and DBHU works well, even where both blurring and noise are present in the scene. [ABSTRACT FROM AUTHOR]

This paper examines the properties of bistatic scattering from an inhomogeneous rough surface, which, in this paper, is modeled by the transitional layer as a function of depth. The lower medium of the rough surface is horizontally uniform but vertically inhomogeneous. Both linear and circular polarizations are investigated in light of the dependences of transition rate, background dielectric constant, and surface roughness. The presence of dielectric inhomogeneity generally leads to several features that do not appear in the homogeneous surface, such as the scattering coefficient on the whole scattering plane is enhanced; the dynamic range of HH and VV over the azimuth plane is reduced; HV can be greater than VH; and the difference of LR and RR is decreased. With the increasing transition rate, the scattering coefficients for both the linear and circular polarizations are enhanced. As the background dielectric constant increases, the scattering responses of the linear and circular polarizations are quite different. For the linear polarization, HH exhibits a stronger angular dependence; VV reduces in the forward region and enhances notably in the backward region; and HV decreases but VH increases. For circular polarizations, the cross-polarized LR increases in the backward region but decreases in the forward region, and the copolarized RR enhances on the whole scattering plane. With the increasing surface roughness, the scattering coefficient becomes more evenly distributed over the entire scattering plane. [ABSTRACT FROM AUTHOR]

This paper examines the polarized backscattering of spatially anisotropic rough surfaces. To better explore the physical mechanisms that control the azimuthal dependence of the backscattering from anisotropic surfaces, the effects of surface roughness [correlation length and root-mean-square (rms) height], dielectric constant, and radar parameters from anisotropic surfaces are studied. The advanced integral equation model (AIEM) is used to simulate both co- and cross-polarized backscattering coefficients, including the single and multiple scattering. Numerical results suggest that the multiple scattering exhibits a stronger azimuthal dependence for HH than VV polarization, especially more so at a larger incident angle. For weakly anisotropic surface, the azimuthal variation of backscattering tends to be a sinusoidal-like pattern. However, with the enhancement of anisotropy, such a scattering pattern is distorted, and the sharp dip appears at up/down direction. As the rms height and dielectric constant increase, the scattering is enhanced on the whole. The HH/VV ratio at lower dielectric constant is greater than that at higher one. In comparison, scattering shows stronger dependence on anisotropy at lower dielectric constant, especially at a larger incident angle. As an application example, we compare the model predictions with reported measurements from two different sites. Preliminary results are quite encouraging, and thus, the analysis presented in this paper is potentially useful to predict and interpret backscattering from crop field surface, where strong anisotropic surfaces commonly present due to plowing or raking practice. [ABSTRACT FROM AUTHOR]

The Canadian Department of National Defence (DND) is developing an experimental over-the-horizon radar (OTHR) with the potential for surveillance of Canada. Because of dynamically changing ionospheric conditions in the Earth’s high-latitude and polar regions, the operating OTHR transmission frequency and elevation angle need to be adjusted regularly to maintain constant illumination of downrange targets. In this paper, the feasible operating frequency and elevation angle radar parameters are determined for short- and long-range OTHR operation using 3-D ionosphere ray-tracing simulations. Together, the collection of all feasible radar configurations forms a characteristic profile which shifts and deforms as factors such as the time of day, season, and solar activity are varied. The range of operating frequencies and elevation angles obtained from this paper will aid developing the transmitter and receiver antenna layouts for experimental OTHR configurations in the poorly understood high-latitude and polar regions. These methods will also help to form the basis of the frequency monitoring systems (FMS) that will control the configuration of these polar OTHR systems in real time. [ABSTRACT FROM AUTHOR]

The design of the Moderate-Resolution Imaging Spectroradiometer (MODIS) instrument was driven by the scientific community’s desire to have near-daily global coverage at moderate resolution (~1km) with comprehensive spectral coverage from visible to long-wave infrared wavelengths. Since their launches in 1999 and 2002, respectively, the Terra and Aqua MODIS instruments have made continuous global observations and generated numerous data products to help users worldwide with their studies of the Earth’s system and its short- and long-term changes. The 20 reflective solar bands (RSBs) with wavelengths from 0.41 to $2.2~\mu \text{m}$ collect data at three nadir spatial resolutions: 250 m, 500 m, and 1 km. The solar diffuser (SD) coupled with the SD stability monitor (SDSM) provides a reflectance-based calibration on-orbit. In addition, lunar observations and response trends from pseudoinvariant desert sites are used to characterize the response versus scan-angle changes on-orbit. This paper provides a brief overview of MODIS RSB calibration algorithms, as implemented in the latest Level 1B version 6.1, operational activities, on-orbit performance, remaining challenges, and potential improvements. Results from the SD and SDSM measurements show a wavelength and mirror-side-dependent degradation in RSB responses, with the largest degradation at the shortest wavelengths, particularly for Terra MODIS. Aqua MODIS has experienced far less degradation of its optics and on-board calibrators compared with Terra MODIS, resulting in an overall better performance. With the exception of Aqua band 6, there have been no new noisy or inoperable detectors in the RSB of either instrument during postlaunch operations. As the instruments age and continue to endure the space environment, the detectors and the optical systems degrade. The challenges associated with incorporating these on-orbit changes to ensure a production of high-quality calibrated L1B data products are also discussed in this paper. [ABSTRACT FROM AUTHOR]

Spectral data offer a means of estimating the critical parameters of sediments, including sediment composition, moisture content, surface roughness, density, and grain-size distribution. Macroscopic surface roughness in particular has a substantial impact on the structure of the bidirectional reflectance factor (BRF) and the angular distribution of scattered light. In developing the models to invert the properties of the surface beyond just surface composition, roughness must also be accounted for in order to achieve reliable and repeatable results. This paper outlines laboratory studies in which the BRF and surface digital elevation measurements were performed on dry clay sediments. The results were used to explore the suitability of various roughness metrics to account for the radiometric effect of surface roughness. The metrics that are specifically addressed in this paper include random roughness and sill variance. Relative accuracy and tradeoffs between these metrics are described. We find that spectral variability, especially near spectral absorption features, correlates strongly with the quantified measures of surface roughness. We also find that spectral variability is sensitive to the sensor fore-optic size. The results suggest that roughness parameters might be directly determined from the spectrum itself. The relationship between spectral variability and macroscopic surface roughness was particularly strong in some broad spectral ranges of the visible, near infrared, and shortwave infrared, including the near-infrared region between 600 and 850 nm. [ABSTRACT FROM AUTHOR]

Spatiotemporal image fusion is considered as a promising way to provide Earth observations with both high spatial resolution and frequent coverage, and recently, learning-based solutions have been receiving broad attention. However, these algorithms treating spatiotemporal fusion as a single image super-resolution problem, generally suffers from the significant spatial information loss in coarse images, due to the large upscaling factors in real applications. To address this issue, in this paper, we exploit temporal information in fine image sequences and solve the spatiotemporal fusion problem with a two-stream convolutional neural network called StfNet. The novelty of this paper is twofold. First, considering the temporal dependence among image sequences, we incorporate the fine image acquired at the neighboring date to super-resolve the coarse image at the prediction date. In this way, our network predicts a fine image not only from the structural similarity between coarse and fine image pairs but also by exploiting abundant texture information in the available neighboring fine images. Second, instead of estimating each output fine image independently, we consider the temporal relations among time-series images and formulate a temporal constraint. This temporal constraint aiming to guarantee the uniqueness of the fusion result and encourages temporal consistent predictions in learning and thus leads to more realistic final results. We evaluate the performance of the StfNet using two actual data sets of Landsat-Moderate Resolution Imaging Spectroradiometer (MODIS) acquisitions, and both visual and quantitative evaluations demonstrate that our algorithm achieves state-of-the-art performance. [ABSTRACT FROM AUTHOR]

The growing need for high-performance imaging tools for terrorist threat detection and medical diagnosis has led to the development of new active architectures in the microwave and millimeter range. Notably, multiple-input multiple-output systems can meet the resolution constraints imposed by these applications by creating large, synthetic radiating apertures with a limited number of antennas used independently in transmitting and receiving signals. However, the implementation of such systems is coupled with strong constraints in the software layer, requiring the development of reconstruction techniques capable of interrogating the observed scene by optimizing both the resolution of images reconstructed in two or three dimensions and the associated computation times. In this paper, we first review the formalisms and constraints associated with each application by taking stock of efficient processing techniques based on spectral decompositions, and then, we present a new technique called the transverse spectrum deconvolution range migration algorithm allowing us to carry out reconstructions that are both faster and more accurate than with conventional Fourier domain processing techniques. This paper is particularly relevant to the development of new computational imaging tools that require, even more pronouncedly than in the case of conventional architectures, fast image computing techniques despite a very large number of radiating elements interrogating the scene to be imaged. [ABSTRACT FROM AUTHOR]

This paper develops a new approach to band selection for multiple-target detection, called constrained-target band selection (CTBS). Its idea is derived from the concept of constrained energy minimization (CEM) by constraining a target of interest, while minimizing the variance resulting from the background (BKG). By taking advantage of CEM, the variance produced by a target of interest can be further used as a measure of prioritizing bands as well as a means of selecting bands for this particular target. As a result, two CTBS-based band prioritization (BP) criteria, called minimal variance-based BP (MinV-BP) and maximal variance-based BP (MaxV-BP), and two CTBS-based BS methods, called sequential forward CTBS (SF-CTBS) and sequential backward CTBS (SB-CTBS), can be derived for multiple-target detection. Since the bands selected by CTBS vary with targets of interest used to constrain CEM, in order for CTBS to be applied to multiple targets, a new fusion technique, called band fusion selection (BFS), is further developed for CTBS to integrate bands selected by different targets so that CTBS can work for all targets. Unlike most BS methods for target detection which generally simultaneously select a fixed set of bands for all targets of interest, the ideas of constraining multiple-target detection and using BFS are novelty of this paper. Experimental results show that CTBS performs well for multiple-target detection. [ABSTRACT FROM AUTHOR]

The knowledge of the water vapor (WV) distribution in the Earth’s atmosphere is of great importance for weather prediction. Meteorological models, in particular, the so-called limited area models, can assimilate humidity measurements, increasing the reliability of the simulated atmospheric dynamics. An important improvement can be achieved, for instance, if we are able to provide the total column with a sufficient precision and accuracy. In this paper, the novel normalized differential spectral attenuation (NDSA) approach is applied to retrieve the vertical profile of WV—and thus the total column—from measurements of differential attenuation signals at microwaves. A forward model (FM) has been used to simulate the ray-tracing of a microwave signal from a transmitter to a receiver in the atmosphere by using the 3-D atmospheric parameters as provided by a numerical weather prediction (NWP) model. From the NDSA measurement, the integrated WV (IWV) content can be directly derived. A further retrieval code is able to invert the measurements of IWV along the path length, providing the vertical humidity profile, which is directly related to the total vertical column assimilated by weather prediction models. In this paper, we show that the values of the total column can be retrieved with a precision and accuracy up to about 0.6% and 2.1%, respectively, which could have a positive impact on NWP models at short time scale. [ABSTRACT FROM AUTHOR]

This paper addresses the exploitation of global navigation satellite systems as opportunistic sources for the joint detection and localization of vessels at sea in a passive multistatic radar system. A single receiver mounted on a proper platform (e.g., a moored buoy) can collect the signals emitted by multiple navigation satellites and reflected from ship targets of interest. This paper puts forward a single-stage approach to jointly detect and localize the ship targets by making use of long integration times (tens of seconds) and properly exploiting the spatial diversity offered by such a configuration. A proper strategy is defined to form a long-time and multistatic range and Doppler (RD) map, where the total target power can be reinforced with respect to, in turn, the case in which the RD map is obtained over a short dwell and the case in which a single transmitter is employed. The exploitation of both the long integration time and the multiple transmitters can greatly enhance the performance of the system, allowing counteracting the low-power budget provided by the considered sources representing the main bottleneck of this technology. Moreover, the proposed single-stage approach can reach superior detection performance than a conventional two-stage process where peripheral decisions are taken at each bistatic link and subsequently the localization is achieved by multilateration methods. Theoretical and simulated performance analysis is proposed and also validated by means of experimental results considering Galileo transmitters and different types of targets of opportunity in different scenarios. Obtained results prove the effectiveness of the proposed method to provide detection and localization of ship targets of interest. [ABSTRACT FROM AUTHOR]

Being capable of providing weather-independent, day-and-night, forward-looking, and high-resolution images, bistatic forward-looking synthetic aperture radar (BFSAR) is a promising sensing technique in applications such as the scene-matching-aided navigation for recently emerging high-speed maneuvering platforms (HMPs). Because of the high speed and the great maneuverability of HMPs and the bistatic forward-looking configuration, conventional image formation algorithms, such as polar format algorithm (PFA), are no longer suitable for HMP-borne BFSAR (HMP-BFSAR). Hence, in this paper, we propose a novel PFA for HMP-BFSAR image formation. In the proposed PFA, a range model, termed as quasi-continuous -move range model, is established by taking the maneuvers of the receiver during pulse propagation into account instead of adopting stop-and-go approximation. Moreover, to take advantage of the collected $k$ -set efficiently, an affine mapping, termed as $k$ -set affine mapping, is conceived to transform the parallelogram-shaped $k$ -set support region to a horizontal and quasi-rectangular one. Furthermore, to compensate for the defocus effect induced by wavefront curvature, a closed-form refocus filter based on the implicit function theorem is derived. Both point target simulation and distributed target simulation are presented in this paper. The simulation results show that the proposed PFA significantly outperforms the conventional PFA in terms of focusing quality and computational efficiency when applied to HMP-BFSAR image formation. [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]

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]

This paper presents the real-time monitoring of a grapevine’s water content that flows up through the xylem tissue by means of a frequency-modulated continuous-wave radar. The application of an optimization process, based on the super-resolution multiple signal classification algorithm, has enabled the reduction of the bandwidth required to discern the xylem water content and, thus, the operating frequency, achieving a depth resolution of at least 3 mm. This design advantage has resulted in a significant step forward toward a real life application, allowing the use of fully-integrated off-the-shelf components in order to implement a completely portable low-power cost-effective radar at 23.1 GHz with a 3.4-GHz bandwidth. The sensor performance has been evaluated by means of three different experiments: irrigation cycles, day/night cycles, and comparison between irrigation cycles at different temperatures. From the experimental results, it is possible to assert that the contactless sensor presented in this paper is very sensitive to changes in the plant’s water content, differentiating between daytime and nighttime. In addition, it has been proven that temperature has a noticeable influence over the evapotranspiration, observing negative drying slopes of 5.62 and 6.28 mV/cycle at 23 °C and 26 °C, respectively. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the effect of spatial and spectral compression on the performance of statistically based retrieval. Although the quality of the information is not completely preserved during the coding process, experiments reveal that a certain amount of compression may yield a positive impact on the accuracy of retrievals. We unveil two strategies, both with interesting benefits: either to apply a very high compression, which still maintains the same retrieval performance as that obtained for uncompressed data; or to apply a moderate to high compression, which improves the performance. As a second contribution of this paper, we focus on the origins of these benefits. On the one hand, we show that a certain amount of noise is removed during the compression stage, which benefits the retrievals performance. On the other hand, we analyze the effect of compression on spectral/spatial regularization (smoothing). We quantify the amount of information shared among the spatial neighbors for the different methods and compression ratios. We also propose a simple strategy to specifically exploit spectral and spatial relations and find that, when these relations are taken into account beforehand, the benefits of compression are reduced. These experiments suggest that compression can be understood as an indirect way to regularize the data and exploit spatial neighbors information, which improves the performance of pixelwise statistics-based retrieval algorithms. [ABSTRACT FROM AUTHOR]

Craters are among the most abundant features on the surface of many planets with great importance for planetary scientists. They reveal chronology information about planets and may be used for autonomous spacecraft navigation and landing. Although numerous research efforts have been carried out in the field of crater detection, existing crater detection algorithms (CDAs) are only helpful in a limited number of applications. A promising crater detection approach involves two main steps: 1) hypothesis generation (HG) and 2) hypothesis verification (HV). During HG, potential crater locations are detected. The validity of the hypothesized crater locations is then tested in a HV step. In this context, we discuss some commonly used algorithms for HG such as highlight-shadow region detection and Hough transform as well as our novel and enhanced algorithms based on interest point detection and convex grouping. A key objective of this paper is to analyze their performance while paying special attention to how they affect the accuracy of the verification step. To deal with different size craters, we focus on multiscale HG. For HV, we have chosen convolutional neural networks which have recently achieved state-of-the-art performance in many computer vision applications. Due to the variation of test sets in the literature, it is often challenging to compare the performance of different CDAs in a fair way. In this paper, we present a comprehensive performance evaluation and comparison of CDAs. Each algorithm has been trained/tested using common data sets generated by a systematic approach. [ABSTRACT FROM AUTHOR]

While many microwave studies related to tree emission have been undertaken, a few have considered the effect of phenological change on the emission from coniferous trees. The permittivity of vegetation tissue is known to be influenced by water content, while the water content and phase is sensitive to temperature in particular at temperatures below freezing. In addition to temperature, canopy-intercepted snow might also modify the tree emission and transmissivity in the microwave range. In this paper, a season-long experiment was designed to quantify the effect of snow accumulation and temperature on the observed microwave transmissivity from tree. A ground-based, upward-pointing multifrequency radiometer was used to monitor the microwave emissivity of a single coniferous tree at a site in Northern Finland. Radiometer measurements were combined with measurements of the canopy-intercepted snow cover and tree skin temperature. This paper presents two important findings. First, the tree transmissivity was strongly correlated with tree skin temperature under subzero temperature conditions, but uncorrelated with skin temperature changes above freezing. Second, although the tree transmissivity was slightly affected by the snow accumulation on the tree canopy, the overall influence on tree emission was statistically insignificant in this paper. [ABSTRACT FROM AUTHOR]

This paper investigated the spectral characteristics of heavy metal contaminated soils of a hydrothermal ore deposit developed in carbonate host rock associated with heavy metal concentration and mineral composition. The results showed that spectral response of heavy metal contaminated soils was statistically correlated with zinc, cadmium, and lead concentrations. Empirical equations for predicting zinc, cadmium, and lead concentrations were derived. Spectral characteristics of the soils were expressed by smectite, chlorite, tremolite, and talc which resulted from hydrothermal alteration and weathering products of the parent rocks. The stepwise multiple linear regression (SMLR) model of zinc, cadmium, and lead was statistically satisfactory with $R^{2}$ greater than 0.7. The SMLR results indicated that the spectral response to cadmium and zinc concentration was sensitive to reflectance at 1850 nm and first derivative at ~950 and 2154 nm corresponding to the smectite absorption features. On the other hand, lead concentration is closely related to first derivatives at 1453, 2316, and 2337 nm, which are absorption features of chlorite, tremolite, and talc. These results revealed that the spectral bands sensitive to the heavy metal concentration varied with the geochemical absorption mechanism between specific minerals and heavy metal elements. Therefore, the geological setting of the soils is one of the major controlling factors associated with spectral response to heavy metal contamination. Given the fact that a hydrothermal ore deposit is one of the most widely distributed types, the laboratory result of this paper may be applied to the real-world cases with similar geological environments. [ABSTRACT FROM AUTHOR]

Soil Moisture and Ocean Salinity (SMOS) mission has successfully contributed to global soil moisture products since 2009. Validation and calibration activities were conducted worldwide, yet some of the validation results do not fulfill the targeted accuracy of ±0.04 $\text{m}^{3}\text{m}^{-3}$. This paper presented the site-specific calibration of the V620 retrieval algorithm with in situ data collected at selected agricultural sites in the humid tropical regions, Malaysia. This set of data has been validated where low accuracy of SMOS soil moisture products was found. To improve the SMOS soil moisture retrieval, calibration of SMOS soil moisture retrieval algorithm based on the L-band Microwave Emission and Biosphere model and SMOS Level 1C $\text{T}_{\mathrm {B}}$ products, considering the local parameters was conducted. The calibration proves that these site-specific parameters improve the product’s accuracy. Validation of SMOS Level 2 product with in situ data showed bias, root-mean-square error (RMSE), and unbiased RMSE (ubRMSE) ranging from 0.050 to 0.118 $\text{m}^{3}\text{m}^{-3}$ , 0.068 to 0.142 $\text{m}^{3}\text{m}^{-3}$ , and 0.069 to 0.103 $\text{m}^{3}\text{m}^{-3}$ , respectively. The soil moisture retrieval based on the calibrated model showed an improved bias of 0.020–0.056 $\text{m}^{3}\text {m}^{-3}$ and RMSE of 0.026–0.065 $\text{m}^{3}\text{m}^{-3}$. The ubRMSE ranges from 0.017 to 0.034 $\text{m}^{3}\text{m}^{-3}$. Recently released SMOS-IC V105 product was also validated, where small improvements were noticed when compared to the accuracy of SMOS Level 2. This paper shows the importance of local parameters in retrieving soil moisture with higher accuracy compared to the use of global generalized parameters that are used in the original SMOS soil moisture retrieval algorithm. [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]

For the quantitative applications of the Suomi National Polar-orbiting Partnership (SNPP) Advanced Technology Microwave Sounder (ATMS), the geolocation accuracy of its sensor data records must be quantified during its on-orbit operation. In this paper, a refined coastline inflection point method is used to evaluate the on-orbit geolocation accuracy of SNPP ATMS. It is disclosed that for SNPP ATMS, the static error term with scan-angle-dependent feature is a dominant part among all the geolocation error sources. A mathematical model is then developed to convert the in-track and cross-track geolocation errors to the beam pointing Euler angles defined in the spacecraft coordinate system, which can be further used to construct the correction matrix for on-orbit geolocation process. By using the correction matrix built in this paper, the geolocation error is obviously reduced both at nadir and at the edge of the scan. The total geolocation error at nadir before/after correction is 3.8/0.8 km at K-band, 5.6/0.8 km at Ka-band, 3.3/0.4 km at V-band, and 1.5/0.1 km at W-band. The geolocation bias at the edge of the scan line before/after correction is 4.6/1.3 km at K-band, 9.4/1.8 km at Ka-band, 4.4/2.4 km at V-band, and 3.2/0.8 km at W-band. After correction, the scan-angle-dependent feature in geolocation error is also largely reduced. [ABSTRACT FROM AUTHOR]

Dense stereo image matching in remotely sensed images is a challenging problem, though it has been studied for more than two decades, due to occlusions, discontinuities, geometric, and radiometric distortions. A novel multiobjective fitness function-based dense stereo matching approach using genetic algorithms (GAs) is proposed in this paper. The proposed method is useful for estimating dense disparity map with an improved number of inliers for a stereo image pair, despite the constraint of finding correct disparity at depth discontinuities. In this paper, the steps of GA, such as initialization of the population, fitness function, and crossover and mutation operation, are designed and implemented to effectively deal with the problem of dense stereo image matching. To initialize the population, a Scale Invariant Feature Transform (SIFT) descriptor is computed for each pixel and multiple-size window-based matching is performed, using the similarity measures: 1) Euclidean distance and 2) spectral angle mapper. The generated disparity maps are pruned to choose a suitable subset using the designed fitness functions, considering the constraints related to stereo image pair, such as epipolar constraint, which encodes the epipolar geometry and the similarity measure that is useful to decide accuracy of the correspondences. The two objective functions are the number of inliers computed using the fundamental matrix and an energy minimization function, considering discontinuities and occlusions. The usefulness of this approach for remotely sensed stereo image pairs is demonstrated by improving the number of inliers and favorably comparing with state-of-the-art dense stereo image matching methods. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach to interpreting the satellite attitude based on inverse synthetic aperture radar (ISAR) images. In the conventional viewpoint, quadratic and higher order phase terms of ISAR imagery are regarded as negative factors causing the defocusing phenomenon. In this paper, we introduce how to apply quadratic phase coefficients to estimate target attitude from the ISAR imagery. A geometric projection model of ISAR imaging is built according to radar line of sight, and an explicit expression is also derived to connect target attitude parameters and the image defocusing property. With the accommodation of Broyden–Fletcher–Goldfarb–Shanno algorithm, spatial-variant quadratic phase coefficients together with attitude parameters are determined by an image contrast maximization. We also extend the proposed algorithm to multistatic ISAR applications, where the quadratic phase information lying in simultaneous multistatic ISAR images can be mined to enhance the performance of target attitude estimation. Experimental results illustrate the feasibility of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The cross correlation power spectrum of multiple signal sequences in the singularity domain is studied in this paper. With theoretical derivation and quantitative analysis, the cross correlation singularity power spectrum (CSPS) distribution theory is proposed. Developed from correlation function (CF), spectrum CF (SCF), singularity power spectrum (SPS), and multifractal cross correlation analysis, the CSPS can be applied for the correlation analysis of multiple fractal time series. In this paper, the CSPS is rigorously derived based on SPS and SCF, and is verified with classical multifractal time series. Furthermore, a target detection method based on the proposed CSPS method is also proposed. The proposed methodology is tested on sea clutters, both with and without target, from the Ice Multiparameter Imaging X-Band radar data set. The simulation results indicate that the target detection based on CSPS performs better than conventional multifractal spectrum methods, and can achieve almost 100% detection probability of detecting low-observable targets within sea clutters. [ABSTRACT FROM AUTHOR]

Recently, researchers have shown the powerful ability of deep methods with multilayers to extract high-level features and to obtain better performance for hyperspectral image classification. However, a common problem of traditional deep models is that the learned deep models might be suboptimal because of the limited number of training samples, especially for the image with large intraclass variance and low interclass variance. In this paper, novel convolutional neural networks (CNNs) with multiscale convolution (MS-CNNs) are proposed to address this problem by extracting deep multiscale features from the hyperspectral image. Moreover, deep metrics usually accompany with MS-CNNs to improve the representational ability for the hyperspectral image. However, the usual metric learning would make the metric parameters in the learned model tend to behave similarly. This similarity leads to obvious model’s redundancy and, thus, shows negative effects on the description ability of the deep metrics. Traditionally, determinantal point process (DPP) priors, which encourage the learned factors to repulse from one another, can be imposed over these factors to diversify them. Taking advantage of both the MS-CNNs and DPP-based diversity-promoting deep metrics, this paper develops a CNN with multiscale convolution and diversified metric to obtain discriminative features for hyperspectral image classification. Experiments are conducted over four real-world hyperspectral image data sets to show the effectiveness and applicability of the proposed method. Experimental results show that our method is better than original deep models and can produce comparable or even better classification performance in different hyperspectral image data sets with respect to spectral and spectral–spatial features. [ABSTRACT FROM AUTHOR]

Remotely sensed sea-surface temperature (SST) retrievals with a significant positive bias during the occurrence of upwelling phenomena along the southeastern coast of Brazil were reported in our companion paper. As a result, this paper proposes an automated bias correction algorithm to improve the MODIS long-wave multichannel SST (MCSST) retrievals during the abovementioned conditions in this region. In this paper, MODIS daytime SST data (SSTMODIS) and differences between brightness temperatures in MODIS channels 31 and 32 (BT31 − BT32) are analyzed simultaneously with hourly wind surface conditions, in situ SST at 0.3 and 10 m in depth (SSTbuoy03 and SSTbuoy10), and sensible and latent heat fluxes from the Cabo Frio buoy data (at 23° S, 42° W) during 2014. The obtained results show that some upwelling events present air temperature ($T_{\mathrm {air}}$) greater than SSTbuoy03 and low-atmospheric water vapor content. A simultaneous occurrence of these factors during upwelling conditions may lead to a warm-skin layer effect and may cause BT31 to be greater than SSTbuoy03 and BT31 − BT32 to be small (−0.18 °C ± 0.22 °C), affecting the MCSST performance. The proposed bias correction algorithm uses a least-squares curve between SSTbuoy03 and SSTMODIS retrievals when BT31 − BT32 ≤ 0.5 °C (i.e., dry atmospheric conditions). The bias correction algorithm has significantly improved the SSTMODIS bias (RMSE) from 1.43 °C to −0.2 °C (1.60 °C to 0.58 °C) when applied to 22 cloud-free pixels of MODIS during January–March of 2015. [ABSTRACT FROM AUTHOR]