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This paper aims to achieve computationally efficient and high-accuracy subpixel image registration with large displacements under the rotation–scale–translation model. This paper employs the classical phase correlation algorithm and the Lucas–Kanade (LK) algorithm in a two-stage coarse-to-fine framework, for which the motivation is from the observation that the two algorithms exhibit strong complementary property between convergence range and subpixel accuracy. In this framework, the LK algorithm will also become computationally efficient owing to the small residual displacement. On the other hand, this paper takes into account the residual model with respect to the compensation scheme explicitly, and deduces formulas for the final results combination, which is expected to be closer to the true displacement vector and thus further improve the estimation accuracy. Since the compensation can be applied to either the target image or the reference image, two algorithms are presented accordingly, and analysis as well as comparison are also performed. Finally, both simulations and real image experiments are performed to verify the motivation, and the results are consistent with the analysis. [ABSTRACT FROM PUBLISHER]

Linear spectral unmixing is the practice of decomposing the mixed pixel into a linear combination of the constituent endmembers and the estimated abundances. This paper focuses on unsupervised spectral unmixing where the endmembers are unknown a priori. Conventional approaches use either geometrical- or statistical-based approaches. In this paper, we address the challenges of spectral unmixing with unsupervised deep learning models, in specific, the autoencoder models, where the decoder serves as the endmembers and the hidden layer output serves as the abundances. In several recent attempts, part-based autoencoders have been designed to solve the unsupervised spectral unmixing problem. However, the performance has not been satisfactory. In this paper, we first discuss some important findings we make on issues with part-based autoencoders. By proof of counterexample, we show that all existing part-based autoencoder networks with nonnegative and tied encoder and decoder are inherently defective by making these inappropriate assumptions on the network structure. As a result, they are not suitable for solving the spectral unmixing problem. We propose a so-called untied denoising autoencoder with sparsity, in which the encoder and decoder of the network are independent, and only the decoder of the network is enforced to be nonnegative. Furthermore, we make two critical additions to the network design. First, since denoising is an essential step for spectral unmixing, we propose to incorporate the denoising capacity into the network optimization in the format of a denoising constraint rather than cascading another denoising preprocessor in order to avoid the introduction of additional reconstruction error. Second, to be more robust to the inaccurate estimation of a number of endmembers, we adopt an $l_{21}$ -norm on the encoder of the network to reduce the redundant endmembers while decreasing the reconstruction error simultaneously. The experimental results demonstrate that the proposed approach outperforms several state-of-the-art methods, especially for highly noisy data. [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]

This paper investigates the use of an acoustic glider to perform acoustical meteorology. This discipline consists of analyzing ocean ambient noise to infer above-surface meteorological conditions. The paper focuses on wind speed estimation, in a near-shore marine environment. In such a shallow water context, the ambient noise field is complex, with site-dependent factors and a variety of nonweather concurrent acoustic sources. A conversion relationship between sound pressure level and wind speed is proposed, taking the form of an outlier-robust nonlinear regression model learned with in situ data. This method is successfully applied to experimental data collected in Massachusetts Bay (MA, USA) during four glider surveys. An average error in wind speed estimation of 1.3 m · s−1 (i.e., average relative error of 14%) over wind speed values up to 17 m · s−1 is reported with this method, which outperformed results obtained with relationships from the literature. Quantitative results are also detailed on the dependence of wind speed error estimation on the environment characteristics, and on the classification performance of observations contaminated by acoustic sources other than wind. Passive acoustic-based weather systems are a promising solution to provide long-term in situ weather data with fine time and spatial resolutions. These data are crucial for satellite calibration and assimilation in meteorological models. From a broader perspective, this paper is the first step toward an operationalization of acoustic weather systems and their on-board embedding in underwater monitoring platforms such as gliders. [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]

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]

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]

This paper contributes to the predictive understanding of satellite precipitation estimation errors over complex terrain, which is fundamental to the development of error models for improving hydrological applications. This paper focuses on the Trentino-Alto Adige region of the eastern Italian Alps. Rainfall observations over a 10-year period (2000–2009) from a dense rain gauge network in the region are used as reference precipitation. A number of satellite precipitation error properties (probability of detection, false alarm rates, missed events, spatial correlation of the error, and hit biases) are investigated in terms of seasonality, satellite algorithm, rainfall intensity, gauge density, and temporal resolution dependencies. These error parameters are typically used in error models (e.g., SREM2D) and provide the basis for enhancing error scheme development. Three widely used satellite-based precipitation products are employed: 1) the Climate Prediction Center morphing product; 2) the precipitation estimation from remotely sensed imagery using artificial neural networks; and 3) the Tropical Rainfall Measuring Mission multisatellite precipitation analysis 3B42 near-real-time product. The three products show similar performances, with larger errors during the warm season, characterized by convective storms, and less variability in the cold season, characterized by more organized stratiform systems. Lower biases are depicted at the daily scale with respect to the 3-hourly resolution. The SREM2D error model has the ability to correct the satellite precipitation products, even though attention is needed for potential systematic errors when applying the calibrated model to independent periods or regions. [ABSTRACT FROM PUBLISHER]

Wind profilers (WPs) are a special category of ground-based radar instruments used to obtain 3-D profile of wind velocity along the altitude in atmosphere. They transmit radio wave pulses in very high frequency (VHF) or ultrahigh frequency (UHF) bands in vertical and near-vertical directions. The Doppler frequency shift and the time of flight of the echo signals from each beam are measured. These data are analyzed to get the radial velocities of atmospheric targets at all the ranges. The data must be processed from at least three noncoplanar beam directions together to estimate 3-D wind profile. The signal-to-noise ratio is very low for the higher range target echoes. Also, echoes are often contaminated by nonatmospheric signals like clutter, radio frequency interference, and so on. Determining accurate wind profile, especially at higher heights, becomes a challenging task due to these factors. This paper presents a novel algorithm for the estimation of radial velocity profile by processing the set of Doppler power spectra of all range bins. The algorithm identifies prospective atmospheric echoes components. Then it forms velocity profile trails connecting five range bins. The program rejects the profile trails that show velocity change higher than a predefined limit. The remaining trails are evaluated using a specially designed multiparameter cost function (MPCF). The trails with maximum cost are selected and then connected to construct the complete Doppler profile or the radial velocity profile. The key innovation in this method is an improvised function that is created by weighted addition of two terms. The first term is proportional to the signal power, and the second term is a nonlinear function of differential wind shear. This algorithm has been tried on multiple sets of the Indian mesosphere–stratosphere–troposphere radar data and the lower atmospheric WP radar data. The performance of this method is compared with other methods, namely, the adaptive moment estimation method and fuzzy logic approach. It has been observed that the new method shows excellent consistency in extracting the Doppler profile from the power spectral data. This method requires at least 30% lesser computations compared with other methods. The results of the MPCF method showed a very good match with the data obtained from concurrently operated Global Positioning System sonde, an independent wind profiling method. This paper also shows the results on the performance improvement using power spectra from the symmetrical beams. This algorithm shows a great promise as a tool for automatic Doppler profile tracing. [ABSTRACT FROM PUBLISHER]

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]

This paper introduces RapidScat 2.5-km ultrahigh-resolution (UHR) wind estimation and validates it in near-coastal regions. RapidScat UHR wind estimation provides finer resolution ocean wind vector fields than conventional 12.5-km level 2B (L2B) wind products at a cost of higher noise. In addition, this paper applies direction interval retrieval techniques and develops other wind processing improvements to enhance the performance of RapidScat UHR wind estimation. The new algorithm is validated with L2B wind estimates, numerical weather prediction wind products, and buoy measurements. The wind processing improvements produce more spatially consistent UHR winds that compare well with the wind products mentioned above. [ABSTRACT FROM AUTHOR]

The extended common midpoint (XCMP) method can be used on multichannel 3-D ground-penetrating radar (GPR) to estimate the asphalt pavement thickness and dielectric constant without the need for calibration by taking cores. The XCMP method requires accurate time delay determination of pavement reflection. However, for thin asphalt overlay, the range resolution of 3-D GPR signal is insufficient to resolve the overlapped pulses of asphalt concrete (AC). The objective of this paper is to use multiple signal classification (MUSIC) algorithm to increase the resolution of 3-D GPR signals, such that thin asphalt overlay thickness can be accurately estimated. An evaluation of the MUSIC algorithm at a full-scale test section and a comparison with regularized deconvolution algorithm showed the MUSIC algorithm is an effective approach for increasing the 3-D GPR signal range resolution when the XCMP method is applied on thin AC overlay. [ABSTRACT FROM AUTHOR]

The accurate estimation of within-footprint slope is very important for measuring earth’s surface characteristics using satellite light detection and ranging (LiDAR) data. Several models have previously been proposed for slope estimation; however, these models have limitations in either accuracy or applicability. Therefore, the main purpose of this paper is to explore the influence of various factors (e.g., ground vertical extent, footprint size, footprint shape, and footprint orientation) on slope estimation to better estimate the within-footprint slope using large-footprint waveform LiDAR data. The results indicated that the absolute slope error due to the coupling effect of ground vertical extent and footprint size increased with an increase in the ratio of ground vertical extent and footprint size, while the relative slope error had an opposite trend. The slope error caused by footprint shape was relatively low when the footprint eccentricity was small. However, the slope error due to footprint shape grew rapidly when the footprint eccentricity became larger; thus, it is essential to fully take into account the influence of footprint shape on within-footprint slope estimation. In addition, the results suggest that the slope error changed regularly based on the intersection angle between footprint orientation and terrain aspect. This paper also provided guidance for the determination of an easy and practical model for within-footprint slope estimation. The determination of best model is dependent on the value of intersection angle. Once the intersection value is given, the best model can easily be determined. Using the best model, the within-footprint terrain slope can be estimated with high accuracy. [ABSTRACT FROM AUTHOR]

$L_{1}$ regularization is used for finding sparse solutions to an underdetermined linear system. As sparse signals are widely expected in remote sensing, this type of regularization scheme and its extensions have been widely employed in many remote sensing problems, such as image fusion, target detection, image super-resolution, and others, and have led to promising results. However, solving such sparse reconstruction problems is computationally expensive and has limitations in its practical use. In this paper, we proposed a novel efficient algorithm for solving the complex-valued $L_{1}$ regularized least squares problem. Taking the high-dimensional tomographic synthetic aperture radar (TomoSAR) as a practical example, we carried out extensive experiments, both with the simulation data and the real data, to demonstrate that the proposed approach can retain the accuracy of the second-order methods while dramatically speeding up the processing by one or two orders. Although we have chosen TomoSAR as the example, the proposed method can be generally applied to any spectral estimation problems. [ABSTRACT FROM AUTHOR]

The Oceansat-II Scatterometer has completed two years in orbit. The instrument has been declared operational, and the normalized radar cross section (σ0) and wind products are being made routinely available to the global operational Numerical Weather Prediction community. The σ0 data from the sensor have been rigorously analyzed for the past two years. Efforts have been put to systematically correlate the biases observed in the data to the onboard functionality of the instrument and to precisely quantify these biases. These analyses have helped not only in the refinement of the ground-processing algorithm but also in the evaluation of sensor performance. This paper presents some of the analyses that have been carried out related to instrument noise calibration with reference to deep-space observations, estimation of biases in the signal bandwidth, and estimation of fixed remnant attitude biases. This paper also addresses the means for rectifying these instrument-related biases. [ABSTRACT FROM PUBLISHER]

The European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission has been providing L-band brightness temperature (BT) using its instrument, the Microwave Imaging Radiometer using Aperture Synthesis. In the measurements, the negative effect of radio frequency interference (RFI) is clearly present, deteriorating the quality of geophysical parameter retrieval. Detection and geolocation of RFI sources are essential to remove or at least mitigate the RFI impacts and ultimately improve the performance of parameter retrieval. This paper discusses a new approach to SMOS RFI source detection, based on the MUltiple SIgnal Classification (MUSIC) algorithm. Recently, the feasibility of MUSIC direction-of-arrival estimation has been shown for the RFI source detection of the synthetic aperture interferometric radiometer. This paper refines the MUSIC RFI source detection algorithm and tailors it to the SMOS scenario. To consolidate the RFI source detection procedure, several required steps are devised, including the rank estimation of the covariance matrix, local peak detection and thresholds, and multiple-snapshot processing. The developed method is tested using a number of SMOS visibility samples. In the test results, the MUSIC method shows an improvement on the accuracy and precision of the RFI source geolocation, compared with a simple detection method based on the local peaks of BT images. The MUSIC results especially outperform the SMOS BT image on the spatial resolution. [ABSTRACT FROM PUBLISHER]

The conventional spaced-receiver approach uses amplitude scintillations to estimate equatorial ionospheric irregularity drift velocities. This approach is less applicable at high latitudes where there is a lack of substantial amplitude scintillations. This paper presents a method to estimate ionosphere irregularity horizontal drift velocities based on GPS signal carrier phase measurements. Joint time–frequency analysis of the carrier phase measurements using an adaptive periodogram technique generates time-varying spectrograms of ionospheric irregularity-induced phase fluctuations. Cross correlation of the spectrograms between antenna pairs provides time lag information on propagating radio signals through the same ionospheric structure. The time lag information is combined with known positions of the receiver array, satellite orbits, and assumed irregularity altitude to infer ionospheric irregularity horizontal drift velocity. This paper presents the methodology and demonstrates its feasibility using data collected by a GPS receiver array at Gakona, Alaska. The potential error sources of this method are also analyzed. [ABSTRACT FROM PUBLISHER]

In this paper, we consider signal subspace estimation based on low-rank representation for hyperspectral imagery. It is often assumed that major signal sources occupy a low-rank subspace. Due to the mixed nature of hyperspectral remote sensing data, the underlying data structure may include multiple subspaces instead of a single subspace. Therefore, in this paper, we propose the use of low-rank subspace representation to estimate the number of subspaces in hyperspectral imagery. In particular, we develop simple estimation approaches without user-defined parameters because these parameters can be fixed as constants. Both real data experiments and computer simulations demonstrate excellent performance of the proposed approaches over those currently in the literature. [ABSTRACT FROM PUBLISHER]

Terrestrial laser scanning is a promising tool for estimating leaf angle (including leaf inclination and azimuthal angles) distribution (LAD). However, previous studies focus on the retrieval of leaf inclination angle distribution, very few studies have considered the distribution of leaf azimuthal angle due to the restriction of measurement techniques. In this paper, we developed a new method to obtain more accurate leaf inclination and azimuthal angle estimations based on leaf point cloud segmentation and filtration and then fit LAD functions using two-parameter Beta-distribution model. In addition, we constructed a new projection coefficient model with two parameters $G(\theta $ , $\varphi$) using Nilson’s algorithm based on the accurate retrieval of LAD. To assess the influence of leaf numbers on leaf inclination and azimuthal angle estimations, we modeled 160 individual leaves and 10 trees with different leaf numbers. In addition, to validate the final results, we also sampled three magnolia trees with different leaf numbers and manually measured leaf inclination and azimuthal angles of all their leaves using an angle measurement device. All results showed that the method proposed in this paper can provide accurate leaf inclination and azimuthal angles (leaf inclination angle: $R^{2} = 0.98$ , RMSE = 24° and leaf azimuthal angle: $R^{2} = 0.99$ , RMSE = 3.44°). The simulated LAD and $G(\theta $ , $\varphi$) estimations based on these leaf inclination and azimuthal angles were strongly correlated with those obtained from ground truth measurements ($P >0.05$). [ABSTRACT FROM AUTHOR]

In this paper, the motion parameters’ estimation problem in synthetic aperture radar (SAR) imaging is extended to the multidimensional case, and the azimuth signal of a moving target with velocities and accelerations in three directions is modeled as a polynomial-phase signal (PPS). To estimate the multidimensional motion parameters of the moving target from the azimuth PPS, a multi-SAR system is required. On this basis, a new method based on the cubic phase function (CPF) is proposed to estimate the Doppler phase coefficients (DPCs) of the PPS. And, all the first- and second-order motion terms can be derived from the DPC. Experimental results have shown that, using the proposed scheme, the motion parameters can be accurately estimated in high signal-to-noise ratio scenarios. [ABSTRACT FROM AUTHOR]

The concept of information fusion has gained a widespread interest in many fields due to its complementary properties. It makes systems more robust against uncertainty. This paper presents a new approach for the well-logging estimation problem by using a fusion methodology. The natural gammaray tool (NGT) is considered as an important instrument in the well logging. The NGT detects changes in natural radioactivity emerging from the variations in concentrations of micronutrients as uranium (U), thorium (Th), and potassium (K). The main goal of this paper is to have precise estimation of the concentrations of U, Th, and K. Four types of Kalman filters are designed to estimate the elements using the NGT sensor. Then, a fusion of the Kalman filters is utilized into an integrated framework by an ordered weighted averaging (OWA) operator to enhance the quality of the estimations. A real covariance of the output error based on the innovation matrix is utilized to design weighting factors for the OWA operator. The simulation studies indicate not only a reliable performance of the proposed method compared with the individual Kalman filters but also a better response in contrast with previous fusion methodologies. [ABSTRACT FROM AUTHOR]

Instant fields of surface current are retrieved from shore-based and unmanned aerial vehicle videos by an optical flow (OF) method named “Typhoon.” This computer vision algorithm estimates dense 2-D 2-component velocity fields from the observable motion of foam patterns in the surf zone. Despite challenging image data resolution and quality, comparison of OF surface current estimates with measurements by an acoustic Doppler velocimeter reveals its ability to capture both wave-to-wave fluctuations and low-frequency variations. The method is also successfully applied to the monitoring of a “flash rip” event. This paper shows clearly the high potential of this method in the nearshore, where the rapid development of webcams and drones offers a large number of applications for swimming and surfing safety, engineering and naval security, and research purpose, by providing quantitative information. [ABSTRACT FROM PUBLISHER]

This paper presents a reliable technology for GPS/Global'naya Navigatsionnaya Sputnikovaya Sistema (GLONASS) surveying under difficult observational conditions, i.e., in forest environments. The technology is based on the rapid static and Real-Time Kinematic (RTK) methods. Test surveys for the developed technology were carried out on a point in a dense forest, with a high degree of difficulty for satellite access. The coordinates of the point were determined by three GPS/GLONASS receivers positioned in a line on a special base, which were separated by a fixed distance of 0.5 m. The test surveys were conducted using Topcon HiPer Pro receivers with 10-Hz RTK positioning and gathering data at a sampling rate of 10 Hz. Practical tests for the presented technology showed that rapid static surveys based on three rover GPS/GLONASS receivers were a quite efficient technology in forest environments (unlike the RTK) and allowed for reliable centimeter accuracy of the determined positions to be obtained in 5-min observational sessions. [ABSTRACT FROM PUBLISHER]

In this paper, we discuss the structure and performance of the maximum entropy temperature-emissivity separation (MaxEnTES) algorithm for assessing land temperature and emissivity from thermal infrared hyperspectral images. This procedure derives the emissivity spectrum and the temperature of the target adopting the maximum entropy (MaxEnt) estimation approach. The main advantage of the MaxEnt statistical inference is the absence of any external hypothesis, which is, instead, the main critical point characterizing any other temperature-emissivity separation (TES) algorithm. The MaxEnTES algorithm carries out the TES task adopting a modified version of the subgradient Shor's r-algorithm adopted for numerical optimization of a MaxEnt objective function. For this purpose, we have utilized the C/C++ Solvopt code from the University of Gratz to develop a practical data processing implementation. In this paper, we discuss the mathematical structure of the MaxEnTES algorithm and analyze its performance in depth using numerical simulations and remote sensing Multispectral Infrared/Visible Imaging Spectrometer images. We show that the MaxEnTES algorithm provides improved accuracy for temperature and emissivity estimation, lowering the standard estimation error to a fraction of degree Kelvin. In agreement with previous investigations, we find that the estimation accuracy grows when increasing the number of available spectral channels. The systematic errors affecting the temperature estimates (e.g., bias) are thoroughly evaluated. We prove that the MaxEnTES algorithm retrieves the correct shape of the target emissivity spectrum even in presence of a significant temperature estimation error. [ABSTRACT FROM PUBLISHER]

Delay/Doppler altimetry (DDA) aims at reducing the measurement noise and increasing the along-track resolution in comparison with conventional pulse-limited altimetry. In a previous paper, we have proposed a semi-analytical model for DDA, which considers some simplifications as the absence of mispointing antenna. This paper first proposes a new analytical expression for the flat surface impulse response (FSIR), considering antenna mispointing angles, a circular antenna pattern, no vertical speed effect, and uniform scattering. The 2-D delay/Doppler map is then obtained by a numerical computation of the convolution between the proposed analytical function, the probability density function of the heights of the specular scatterers, and the time/frequency point target response of the radar. The approximations used to obtain the semi-analytical model are analyzed, and the associated errors are quantified by analytical bounds for these errors. The second contribution of this paper concerns the estimation of the parameters associated with the multilook semi-analytical model. Two estimation strategies based on the least squares procedure are proposed. The proposed model and algorithms are validated on both synthetic and real waveforms. The obtained results are very promising and show the accuracy of this generalized model with respect to the previous model assuming zero antenna mispointing. [ABSTRACT FROM PUBLISHER]

Pixel selection is a crucial step of all advanced Differential Interferometric Synthetic Aperture Radar (DInSAR) techniques that have a direct impact on the quality of the final DInSAR products. In this paper, a full-resolution phase quality estimator, i.e., the temporal phase coherence (TPC), is proposed for DInSAR pixel selection. The method is able to work with both distributed scatterers (DSs) and permanent scatterers (PSs). The influence of different neighboring window sizes and types of interferograms combinations [both the single-master (SM) and the multi-master (MM)] on TPC has been studied. The relationship between TPC and phase standard deviation (STD) of the selected pixels has also been derived. Together with the classical coherence and amplitude dispersion methods, the TPC pixel selection algorithm has been tested on 37 VV polarization Radarsat-2 images of Barcelona Airport. Results show the feasibility and effectiveness of TPC pixel selection algorithm. Besides obvious improvements in the number of selected pixels, the new method shows some other advantages comparing with the other classical two. The proposed pixel selection algorithm, which presents an affordable computational cost, is easy to be implemented and incorporated into any advanced DInSAR processing chain for high-quality pixels’ identification. [ABSTRACT FROM AUTHOR]

The time-consuming modeling of physical remote sensing models restricts their application to parameter estimation from satellite observations. Machine learning techniques have become highly developed in recent years and show good capacity for model fitting. Based on our previously developed coupled soil–canopy–atmosphere radiative transfer model (RTM) and a multiple parameters estimation scheme, this paper evaluates the performance of four machine learning algorithms [Gaussian process regression (GPR), back-propagation neural networks (NNs), random forest regression, and general regression NN] on emulating the coupled RTM, where the traditional lookup table (LUT) algorithm is also compared. The results show that the GPR algorithm can emulate complex RTMs with excellent accuracy and efficiency. GPR emulators of photosynthetically active radiation (PAR), fraction of absorbed PAR, and incident shortwave radiation were applied to the multi-parameter estimation scheme to replace the traditional LUT algorithm, which avoids the need to integrate over the spectra while achieving an acceleration ratio of 16. A test of the updated multi-parameter estimation scheme at the Bondville site using 18 years of clear-sky observations demonstrates that replacing the computationally expensive integration processes with GPR emulators is practical. The emulators can also be used to simulate the corresponding parameters independently, and this GPR acceleration method for complex models is universal and can be easily applied to other time-consuming models. [ABSTRACT FROM AUTHOR]

In this paper, we contribute to answer the question: How accurately can we estimate heights of buildings with flat roofs given one high-resolution single-pass interferometric synthetic aperture radar (InSAR) image pair and one aerial orthophoto? What makes this problem challenging are the different sensor geometries and the sound stochastic combination of all available elevation cues. We revisit already existing methods and develop novel approaches to determine building heights. A rigorous stochastic approach based on least squares adjustment with functionally dependent parameters is introduced to combine all height measurements per building to one robust height estimate. Observation accuracies of the stochastic model are either taken from the literature or estimated empirically. A major benefit of adjustment is that it delivers a posterior standard deviation per height, which can be interpreted as a precision indicator and is of high relevance for practical applications. Estimated heights of an urban scene are compared to ground truth acquired with airborne laser scanning, allowing us to assess height accuracies that can be achieved under nearly optimal conditions. We conduct statistical tests that validate our model and show that a weighted combination of optical and synthetic aperture radar (SAR) data with least squares adjustment delivers reliable height estimates with meter accuracy for flat-roofed buildings. Additionally, we empirically estimate a confidence interval of the estimated heights that directly tells the user the security margin to be included, for example, in case of building evacuations for an anticipated flooding event, under the condition that the data and model have the same specifications as in this paper. [ABSTRACT FROM PUBLISHER]

This paper investigates the use of polarimetry to improve the estimation of the height of buildings in high-resolution synthetic aperture radar (SAR) images. Polarimetric coherence optimization techniques solve the problem of layover effects in urban scenes by allowing a phase separation of the scatterers sharing the same resolution cell. Bare soil elevation estimation is also improved by the polarimetric phase diversity. First, we present an analysis of the statistical modeling of the generalized coherence set. A building height estimation method is then derived from this analysis. Finally, the method is tested and quantitatively validated over an X-band polarimetric interferometric SAR (PolInSAR) airborne image acquired in a single-pass mode, containing a set of 140 different buildings with ground truth. [ABSTRACT FROM PUBLISHER]

This paper addresses a specific typology of land-cover classification problems, hereinafter referred to as “targeted land-cover classification,” where the objective is the identification of only one or few specific “targeted” land-cover classes of interest, disregarding all the other potential classes present in the area under analysis. Such a challenging problem, which is common to a variety of operational information services and applications (e.g., agriculture, forestry, spatial planning, ecosystem monitoring, disaster management, habitat mapping, etc.), can be effectively solved by traditional supervised classification techniques provided that an exhaustive ground truth is available for all the land-cover classes present in the region of interest. Such a requirement is seldom satisfied and presents several practical drawbacks and limitations, both in terms of time and economic cost that may render this task difficult to achieve in most real-life cases. However, the possibility to perform an effective targeted classification using only ground-truth samples for the class(es) of interest (hence avoiding the burden and cost associated with the collection of a full and exhaustive ground-truth information) would represent a significant advantage. In this paper, we present a novel technique capable of identifying specific land-cover classes of interest by exploiting the ground truth only available for these targeted classes, while providing accuracies comparable to those of traditional fully supervised methods. The proposed technique jointly exploits both the unlabeled samples of the image under investigation and the training samples only available for the targeted classes. In particular, the expectation-maximization algorithm and Markov random fields are employed to estimate the probability density function of both the class(es) of interest and the unknown class representing the merger of all the unknown land-cover classes characterizing the study area for which no ground-truth information is available. An extensive experimental analysis and cross-comparisons with both fully supervised support vector machines and ensembles of multiple one-class support-vector-data-description classifiers on different data sets confirmed the effectiveness and the reliability of the proposed technique. [ABSTRACT FROM PUBLISHER]

The objective of synthetic aperture radar (SAR) edge detection is the identification of contours across the investigated scene, exploiting SAR complex data. Edge detectors available in the literature exploit singularly amplitude and interferometric phase information, looking for reflectivity or height difference between neighboring pixels, respectively. Recently, more performing detectors based on the joint processing of amplitude and interferometric phase data have been presented. In this paper, we propose a novel approach based on the exploitation of real and imaginary parts of single-look complex acquired data. The technique is developed in the framework of stochastic estimation theory, exploiting Markov random fields. Compared to available edge detectors, the technique proposed in this paper shows useful advantages in terms of model complexity, phase artifact robustness, and scenario applicability. Experimental results on both simulated and real TerraSAR-X and COSMO-SkyMed data show the interesting performances and the overall effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

The availability of multiangular information from the NASA Multi-angle Imaging SpectroRadiometer (MISR) instrument provides an excellent opportunity for the characterization of surface anisotropy, which can be used for improving surface albedo estimation. However, the MISR data have been reported with large uncertainties and data gaps due to inaccurate aerosol estimation and/or cloud masking limiting its otherwise broader applications. To mitigate these issues, two approaches were proposed to estimate land surface albedo directly from surface reflectance (LSA_sfc) and Top-of-Atmosphere reflectance (LSA_toa), respectively. As a further development of the traditional albedo algorithms, this is the first attempt to simultaneously utilize multispectral and multiangular information in surface albedo estimation without any prior constraining information. Validations at AmeriFlux sites show that the proposed algorithms can achieve accuracies similar to that of the MISR product with respective bias and RMSE of 0.004 and 0.032 for LSA_sfc and 0.005 and 0.032 for LSA_toa algorithms. We found that the LSA_toa algorithm can significantly reduce data gaps and provide accurate surface albedo retrievals with two to three times more valid data than the current MISR product. In addition, these approaches can be easily applied to other optical sensors to produce accurate and gap-free clear-sky surface albedo estimations. The results of this paper also highlight the importance of having two to three simultaneous observations with sufficient angular sampling, which can improve albedo accuracy and reduce data gaps. [ABSTRACT FROM PUBLISHER]

In this paper, we analyze the direction-of-arrival (DOA) of the ice-sheet data collected over Jakobshavn Glacier with the airborne Multichannel Radar Depth Sounder (MRDS) during the 2006 field season. We extracted weak ice–bed echoes buried in signals scattered by the rough surface of the fast-flowing Jakobshavn Glacier by analyzing the DOA of signals received with a five-element receive-antenna array. This allowed us to obtain ice thickness information, which is a key parameter when generating bed topography of glaciers. We also estimated ice–bed roughness and bed slope from the combined analysis of the DOA and radar waveforms. The bed slope is about 8° and the roughness in terms of rms slope is about 16°. [ABSTRACT FROM PUBLISHER]

Classical target detection schemes are usually obtained by deriving the likelihood ratio under Gaussian hypothesis and replacing the unknown background parameters by their estimates. In most applications, interference signals are assumed to be Gaussian with zero mean [or with a known mean vector (MV)] and with an unknown covariance matrix (CM). When the MV is unknown, it has to be jointly estimated with the CM. In this paper, adaptive versions of the classical matched filter (MF) and the normalized MF, as well as two versions of the Kelly detector are first derived and then analyzed for the case where the MV of the background is unknown. More precisely, theoretical closed-form expressions for false alarm (FA) regulation are derived and the constant FA rate property is pursued to allow the detector to be independent of nuisance parameters. Finally, the theoretical contributions are validated through simulations. [ABSTRACT FROM AUTHOR]

This paper presents spiking neural networks (SNNs) for remote sensing spatiotemporal analysis of image time series, which make use of the highly parallel and low-power-consuming neuromorphic hardware platforms possible. This paper illustrates this concept with the introduction of the first SNN computational model for crop yield estimation from normalized difference vegetation index image time series. It presents the development and testing of a methodological framework which utilizes the spatial accumulation of time series of Moderate Resolution Imaging Spectroradiometer 250-m resolution data and historical crop yield data to train an SNN to make timely prediction of crop yield. The research work also includes an analysis on the optimum number of features needed to optimize the results from our experimental data set. The proposed approach was applied to estimate the winter wheat (Triticum aestivum L.) yield in Shandong province, one of the main winter-wheat-growing regions of China. Our method was able to predict the yield around six weeks before harvest with a very high accuracy. Our methodology provided an average accuracy of 95.64%, with an average error of prediction of 0.236 t/ha and correlation coefficient of 0.801 based on a nine-feature model. [ABSTRACT FROM PUBLISHER]

In this paper, we consider the parameter estimation of $K$ -Gaussians, given convex combinations of their realizations. In the remote sensing literature, this setting is known as the normal compositional model (NCM) and has shown promising gains in modeling hyperspectral images. Current NCM parameter estimation techniques are based on Bayesian methodology and are computationally slow and sensitive to their prior assumptions. Here, we introduce a deterministic variant of the NCM, named DNCM, which assumes that the unknown mixing coefficients are nonrandom. This leads to a standard Gaussian model with a simple estimation procedure, which we denote by $K$ -Gaussians. Its iterations are provided in closed form and do not require any sampling schemes or simplifying structural assumptions. We illustrate the performance advantages of $K$ -Gaussians using synthetic and real images, in terms of accuracy and computational costs in comparison to state of the art. We also demonstrate the use of our algorithm in hyperspectral target detection on a real image with known targets. [ABSTRACT FROM AUTHOR]

In synthetic aperture radar (SAR), the separation of the height between the ground subsidence phase components and the atmospheric phase delay mixed in the global SAR interferometry (InSAR) phase information is an issue of primary concern in the remote sensing community. This paper describes a complete procedure to address the challenge to estimate the atmospheric phase screen and to separate the three-phase components by exploiting only one InSAR image couple. This solution has the capability to process persistent scatterers subsidence maps potentially using only two multitemporal InSAR couples observed in any atmospheric condition. The solution is obtained by emulating the atmosphere compensation technique that is largely used by the global positioning system where two frequencies are used in order to estimate and compensate the positioning errors due to atmosphere parameters’ variations. A sub-chirping and sub-Doppler algorithm for atmospheric compensation is proposed, which allows the successful separation of the height from the subsidence and the atmosphere parameters from the interferometric phase observed on one InSAR couple. The results are given processing images of two InSAR couples observed by the COSMO-SkyMed satellite system. [ABSTRACT FROM AUTHOR]

Land-cover classification with hyperspectral imagery has been an active topic in the remote sensing community. It aims at relating a unique class label to each pixel in the scene, so that it can be well defined by a given land cover type. In this paper, we explore the intrinsic characteristics of hyperspectral imagery from a subpixel-level perspective and propose a new subpixel component analysis (SCA) approach for feature extraction and land-cover classification. The core idea of SCA is that we extract a subpixel attribute component feature from the abundance maps. Compared with the abundance maps, the extracted subpixel feature image shows higher signal-to-noise level and clearer spatial distribution details. In order to deal with spectral variability, as well as obtain representative image endmember signatures and their corresponding abundance maps, we adopt a regional clustering-based spatial preprocessing (RCSPP) strategy for endmember identification, and a partial unmixing model based on mixture tuned matched filtering (MTMF) for abundance estimation. Furthermore, to highlight the spatial distribution details as well as eliminate the noise disturbance in the derived abundance maps, we perform sparse image decomposition on the obtained abundance maps, thus achieving a new subpixel feature representation for classification. Our experimental results reveal that the proposed SCA approach can obtain feature representation with explicit physical meaning, clear spatial distribution details, and better noise robustness, leading to state-of-the-art classification results. [ABSTRACT FROM AUTHOR]

Wave–current interaction is a common and important phenomenon in the ocean. As an ocean remote sensing tool, high-frequency (HF) radar can be used to measure currents and wave parameters. In this paper, the possibility of studying wave–current interaction using HF radar is investigated. The first-order spectral power (FSP) of HF radar is used to explore the effect of current on the Bragg wave. By analyzing the FSP change with current (FSP-current distribution), we find that, in deep water, the wave–current interactions mainly belong to 2-D refraction case, while, over a relatively shallow shelf, the interactions are stronger and more complicated. Based on local topography and current field data at Taiwan Strait, the simulation results obtained using the SWAN model confirm the 2-D refraction of the Bragg wave. When the wave–current interaction is stable, we compensate the FSP with radar-measured currents according to the radar extracted FSP-current distribution and achieve a more accurate wind estimation. Comparisons between the original and refined wind fields show the effectiveness and necessity of the current-based compensation. [ABSTRACT FROM AUTHOR]

This paper presents a spherically invariant random vector (SIRV)-based dual-domain filter for polarimetric synthetic aperture radar (PolSAR) speckle suppression. The SIRV model is a simple clutter decomposition model designed for heterogeneous scenes that decomposes the clutter into two independent domains: the texture domain and the polarimetric domain (also known as the speckle domain). The dual-domain filter takes full advantage of the SIRV model to decompose the task of PolSAR speckle suppression into polarimetric domain filtering and texture domain filtering. The polarimetric domain filtering involves estimating a stable state of the normalized coherency matrix with similar samples via fixed-point iteration. For this purpose, the nonlocal patch matching distance measure and the cutoff threshold in the SIRV model case are defined to select similar samples. The texture domain filtering involves reconstructing a sparse texture image without the effect of speckle. For this purpose, patch ordering-based SAR image despeckling via transform-domain filtering (SAR-POTDF), which is based on simultaneous sparse coding (SSC), is applied for the texture filtering. After the dual-domain filtering, a speckle-free PolSAR image can be reconstructed by the product of the texture and the normalized coherency matrix. The effectiveness and robustness of the proposed method was demonstrated by experiments undertaken with CETC-38 X-band, DLR F-SAR S-band, and IECAS C-band high-resolution PolSAR images. The DLR E-SAR L-band medium-resolution data were also used for comparison. The results showed that the dual-domain filtering is effective for the speckle suppression of high-resolution PolSAR images with heterogeneous and detailed scenes. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel approach for 3-D rain field reconstruction using satellite signals. It uses the estimated signal-to-noise ratio (SNR) at the ground receivers for low-earth orbit (LEO) satellites and, thus, indirectly estimates the path-integrated rain attenuation of the microwave communication links. A least-squares algorithm is employed to perform the 3-D tomographic reconstruction of the rain field. The proposed system model consists of an LEO satellite with a realistic overpass trajectory and multiple ground receivers with SNR estimators. Two synthetic rain events near the Great Barrier Reef in Australia are used to test the reconstruction outcome. Simulation results suggest that the reconstructed rain field has close agreement with the synthetic rain field. [ABSTRACT FROM AUTHOR]

This paper deals with atmospheric correction in hyperspectral data acquired in the long-wave infrared (LWIR) spectral range. Atmospheric compensation (AC) is approached from a new perspective in that it is reformulated as an estimation problem on a low-rank subspace. Specifically, a subspace-based model is exploited to represent both the atmospheric transmittance and the upwelling radiance. Taking into account the inherent correlation between these two quantities, we adopt a subspace model that constrains them to vary in a physically consistent way. Exploiting such a model, AC is formulated as a nonlinear least-squares estimation problem. An automatic procedure is proposed to estimate the unknown parameters, which is based only on the image analysis and does not need any atmospheric measurements. Results of an extensive experimental analysis carried out on simulated data are used to discuss the performance of the algorithm with respect to different atmospheric conditions. Finally, experimental evidence over a real LWIR hyperspectral image is provided. [ABSTRACT FROM AUTHOR]

In this paper, we propose a fast and efficient multitemporal despeckling method. The key idea of the proposed approach is the use of the ratio image, provided by the ratio between an image and the temporal mean of the stack. This ratio image is easier to denoise than a single image thanks to its improved stationarity. Besides, temporally stable thin structures are well preserved thanks to the multitemporal mean. The proposed approach can be divided into three steps: 1) estimation of a “superimage” by temporal averaging and possibly spatial denoising; 2) denoising of the ratio between the noisy image of interest and the “superimage”; and 3) computation of the denoised image by remultiplying the denoised ratio by the “superimage.” Because of the improved spatial stationarity of the ratio images, denoising these ratio images with a speckle-reduction method is more effective than denoising images from the original multitemporal stack. The amount of data that is jointly processed is also reduced compared to other methods through the use of the “superimage” that sums up the temporal stack. The comparison with several state-of-the-art reference methods shows better results numerically (peak signal-noise-ratio and structure similarity index) as well as visually on simulated and synthetic aperture radar (SAR) time series. The proposed ratio-based denoising framework successfully extends single-image SAR denoising methods to time series by exploiting the persistence of many geometrical structures. [ABSTRACT FROM AUTHOR]

The absence of systematic estimates of vertical transport of water by convective storms is one of the main causes of uncertainties in weather forecasting in the tropics, in severe weather forecasting, and in the climate-scale analysis and prediction of global circulation. The difficulty of providing such estimates lies in that the only instruments capable of providing such estimates at present, radiometers onboard satellites, operate at short wavelengths, and that makes difficult the retrievals. This paper describes a method capable of providing robust estimates of the maximum height of precipitation and the 3-D structure of condensed water in convective systems using a constellation of radiometers. Using hurricane Winston and the Global Precipitation Measurement (GPM) mission Core Observatory satellite dual precipitation radar as a reference, it is shown that the method is suitable to improve the parameterization of convection in numerical models. [ABSTRACT FROM AUTHOR]

In this paper, the synthetic aperture radar (SAR) azimuth cutoff method is thoroughly revised and a new and general implementation is proposed. The key roles of the pixel spacing, the size of the image box, and the texture of the SAR scene are analyzed and optimized in terms of azimuth cutoff ($\lambda _{\mathrm{ c}}$) estimation. The reliability of the $\lambda _{\mathrm{ c}}$ estimation is analyzed by measuring the distance between the measured and fitted autocorrelation functions. This analysis shows that it is of paramount importance to filter unfeasible/unreliable $\lambda _{\mathrm{ c}}$ values. To identify those values in an objective way, a criterion that is based on the $\chi _{2}$ test performed over a large data set of Sentinel-1 SAR imagery is defined and proven to be effective. The new robust implementation of the $\lambda _{\mathrm{ c}}$ estimation at about 1-km grid spacing is then used to produce averaged $\lambda _{\mathrm{ c}}$ at about 10-km grid spacing. The performance of the new estimation procedure, analyzed using a $\lambda _{\mathrm{ c}}$ -to-wind-speed forward model, is shown to provide improved wind speed retrievals, with a root-mean-square error of 1.8–2 m/s when verified against independent numerical weather prediction model output and scatterometer winds. [ABSTRACT FROM AUTHOR]

Radio frequency interference (RFI) causes serious problems to ultrawideband (UWB) radar operations due to severely degrading radar imaging capability and target detection performance. This paper formulates proper data models and proposes novel methods for effective RFI mitigation. We first apply the single-snapshot Sparse Iterative Covariance-based Estimation (SPICE) algorithm to data from each pulse repetition interval for RFI mitigation and discuss the connection of SPICE to the $l_{1}$ -penalized least absolute deviation ($l_{1}$ -PLAD) approach. Then, we devise a modified group SPICE algorithm and we prove that it is equivalent to a special case of the $l_{1,2}$ -PLAD method. The modified group SPICE algorithm can be applied to data from a coherent processing interval for effective RFI mitigation. Both the single-snapshot SPICE and the modified group SPICE methods simultaneously exploit the sparsity properties of both RFI spectrum and UWB radar target echoes. Unlike the existing sparsity-based RFI suppression methods, such as the robust principal component analysis algorithm, the proposed methods are hyperparameter-free and therefore easier to use in practical applications. Furthermore, the fast implementation of the SPICE methods is considered by exploiting the special structures of both single-snapshot and multiple-snapshot covariance matrices. Finally, the results obtained from applying the SPICE methods to simulated data as well as measured data collected by the U.S. Army Research Laboratory synthetic aperture radar system are presented to demonstrate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Partial least-squares (PLS) regression is a popular method for modeling chemical constituents from spectroscopic data and has been widely applied to retrieve leaf chemical components via hyperspectral remote sensing. However, one persistent challenge for applying the PLS regression is the selection of informative spectral bands among the vast array of acquired spectra. No consensus has been reached yet on how to select informative bands regardless of many techniques being proposed. In this paper, we have composited four individual data sets containing a total of 598 leaf samples from various species to evaluate four different band elimination/selection methods. Results revealed that the stepwise-PLS approach was optimal to estimate leaf chlorophyll content even under different spectral resolutions, from which informative bands were identified. Informative bands, in general, include bands inside the near-infrared (NIR), and in addition, one within the blue range and one within the red range. With such combinations, the PLS regression models meet the requirement for accurate leaf chlorophyll estimation. For most PLS regression models, their accuracies decreased with the reduction of spectral resolution, but the stepwise-PLS approach could consistently estimate the chlorophyll content at different spectral resolutions (with ${R}^{2} \ge 0.77$ for resolutions < 20 nm). The findings, hence, provide valuable insights for selecting informative spectral bands for PLS analysis and lay a strong foundation for retrieving foliar biochemical content using hyperspectral remote sensing data. [ABSTRACT FROM AUTHOR]

Tomographic synthetic aperture radar (TomoSAR) inversion of urban areas is an inherently sparse reconstruction problem and, hence, can be solved using compressive sensing (CS) algorithms. This paper proposes solutions for two notorious problems in this field. First, TomoSAR requires a high number of data sets, which makes the technique expensive. However, it can be shown that the number of acquisitions and the signal-to-noise ratio (SNR) can be traded off against each other, because it is asymptotically only the product of the number of acquisitions and SNR that determines the reconstruction quality. We propose to increase SNR by integrating nonlocal (NL) estimation into the inversion and show that a reasonable reconstruction of buildings from only seven interferograms is feasible. Second, CS-based inversion is computationally expensive and therefore, barely suitable for large-scale applications. We introduce a new fast and accurate algorithm for solving the NL L1-L2-minimization problem, central to CS-based reconstruction algorithms. The applicability of the algorithm is demonstrated using simulated data and TerraSAR-X high-resolution spotlight images over an area in Munich, Germany. [ABSTRACT FROM AUTHOR]

Information on soil available nutrient (SAN) at key crop growth stages is critical to generate prescription maps for implementing variable rate fertilization (VRF). Our previous study showed that integrating time-series remote sensing (RS) data with the modified World Food Studies (WOFOST) crop model provides a useful approach (preliminary RS-WOFOST-based method) to acquiring information on field SAN; however, the estimation accuracy was low for VRF application. In this paper, three steps were proposed to further improve the SAN estimation accuracy. At the first step, the rapid-nutrient assimilation (RNA) method was used to optimize the crop growth simulation process. Compared with the ensemble Kalman filter (EnKF) method, the RNA method showed an improved performance in estimating soil available nitrogen (N), phosphorus (P), and potassium (K) content [EnKF: $R^{2} = 0.48$ (N), 0.37 (P), 0.15 (K); RNA: $R^{2} = 0.59$ (N), 0.46 (P), and 0.18 (K)]. The improved K estimation at the first step was clearly lower than that of N and P; hence, the K content estimation was further optimized at the second step and the accuracy was improved ($R^{2} = 0.27$) by using the estimated N as an input variable during the K estimation. At the third step, an iteration algorithm was implemented based on the first two steps, and the final $R^{2} = 0.71$ (N), 0.58 (P), 0.49 (K); root-mean-square error = 14.35 (N), 3.70 (P), and 14.87 (K). In general, the optimized approach can overcome the limitations of the preliminary RS-WOFOST-based method and improve the SAN estimation accuracy. [ABSTRACT FROM AUTHOR]