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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]

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]

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]

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]

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

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]

To advance the utility of polarimetric synthetic aperture radar observations for extracting biophysical information about vegetation canopies, it is important to develop good understanding of the scattering mechanisms that give rise to the polarimetric scattering response and to apply it toward the development of effective decomposition models of the polarimetric covariance matrices. In this paper, we introduce a more rigorous approach to characterizing the volume scattering component of the three-component scattering model developed by Freeman and Durden. The improved rigor has two aspects: 1) the use of a T-matrix model for computing the polarimetric scattering by an inclined dielectric cylinder and 2) the incorporation of the change in local incidence angle as a function of the orientation of the vegetation cylinders. The improvements address some of the current limitations of the Freeman–Durden model. This paper also provides a sensitivity analysis of the various components of the covariance matrix as a function of several physical parameters including cylinder size, its dielectric constant, and the orientation distribution of cylinders. Such an analysis is a precursor to the development of improved inversion algorithms. [ABSTRACT FROM AUTHOR]

Accurate and high throughput extraction of crop phenotypic traits, as a crucial step of molecular breeding, is of great importance for yield increasing. However, automatic stem–leaf segmentation as a prerequisite of many precise phenotypic trait extractions is still a big challenge. Current works focus on the study of the 2-D image-based segmentation, which are sensitive to illumination and occlusion. Light detection and ranging (LiDAR) can obtain accurate 3-D information with its active laser scanning and strong penetration ability, which breaks through phenotyping from 2-D to 3-D. However, few researches have addressed the problem of the LiDAR-based stem–leaf segmentation. In this paper, we proposed a median normalized-vector growth (MNVG) algorithm, which can segment stem and leaf with four steps, i.e., preprocessing, stem growth, leaf growth, and postprocessing. The MNVG method was tested by 30 maize samples with different heights, compactness, leaf numbers, and densities from three growing stages. Moreover, phenotypic traits at leaf, stem, and individual levels were extracted with the truly segmented instances. The mean accuracy of segmentation at point level in terms of the recall, precision, F-score, and overall accuracy were 0.92, 0.93, 0.92, and 0.93, respectively. The accuracy of phenotypic trait extraction in leaf, stem, and individual levels ranged from 0.81 to 0.95, 0.64 to 0.97, and 0.96 to 1, respectively. To our knowledge, this paper proposed the first LiDAR-based stem–leaf segmentation and phenotypic trait extraction method in agriculture field, which may contribute to the study of LiDAR-based plant phonemics and precise agriculture. [ABSTRACT FROM AUTHOR]

Surface roughness is a land-surface parameter that is widely used in terrain analysis. Some typical roughness details, which have important effects on surface analysis, fail to be characterized on previous roughness maps. The objective of this paper is to provide a more accurate small-to-large scale roughness overview. The new roughness method is designed based on a complete orthogonal system called the V-system. The V-system roughness utilizes the special functions to detect and extract the roughness characteristics from high-resolution digital elevation models (DEMs). In this paper, Lunar Orbiter Laser Altimeterderived DEMs are used as the source data for the roughness calculation. Compared with the global root-mean-square slope and Fourier-based roughness maps, the V-system roughness maps show that more typical roughness details have been added to clearly indicate the small roughness variations on the large map. Furthermore, the reliability and practicability of V-system roughness are demonstrated based on the multiresolution DEMs. As an example, the statistical parameters of the roughness characteristics in the lunar Maria and highlands identify the fact that the highlands are rougher at all scales than the Maria. And this difference corresponds to the basic roughness property. [ABSTRACT FROM AUTHOR]

This paper continues an overview of the validation of operational profile retrievals from the Suomi National Polar-Orbiting Partnership (SNPP), with focus here given to the infrared (IR) ozone profile environmental data record (EDR) product. The SNPP IR ozone profile EDR is retrieved using the cross-track IR sounder (CrIS), a Fourier transform spectrometer that measures high-resolution IR earth radiance spectra containing atmospheric state information, namely, vertical profiles of temperature, moisture, and trace gas constituents. The SNPP CrIS serves as the U.S. low earth orbit (LEO) satellite IR sounding system and will be featured on future Joint Polar Satellite System (JPSS) LEO satellites. The operational sounding algorithm is the National Oceanic and Atmospheric Administration-Unique Combined Atmospheric Processing System (NUCAPS), a legacy sounder science team algorithm that retrieves atmospheric profile EDR products, including ozone and carbon trace gases, with optimal vertical resolution under nonprecipitating (clear to partly cloudy) conditions. The NUCAPS ozone profile product is assessed in this paper using extensive global in situ truth data sets, namely, ozonesonde observations launched from ground-based networks and from ocean-based intensive field campaigns, along with numerical weather prediction model output. Based upon rigorous statistical analyses using these data sets, the NUCAPS ozone profile EDRs are determined to meet the JPSS Level 1 global performance requirements. [ABSTRACT FROM AUTHOR]

The automatic detection of buried threats in ground penetrating radar (GPR) data is an active area of research due to GPR's ability to detect both metal and nonmetal subsurface objects. Recent work on algorithms designed to distinguish between threats and nonthreats in GPR data has utilized computer vision methods to advance the state-of-the-art detection and discrimination performance. Feature extractors, or descriptors, from the computer vision literature have exhibited excellent performance in representing 2-D GPR image patches and allow for robust classification of threats from nonthreats. This paper aims to perform a broad study of feature extraction methods in order to identify characteristics that lead to improved classification performance under controlled conditions. The results presented in this paper show that gradient-based features, such as the edge histogram descriptor and the scale invariant feature transform, provide the most robust performance across a large and varied data set. These results indicate that various techniques from the computer vision literature can be successfully applied to target detection in GPR data and that more advanced techniques from the computer vision literature may provide further performance improvements. [ABSTRACT FROM PUBLISHER]

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

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]

In conventional polar format algorithm (PFA), the effective imaging scene is bounded to a limited region near the reference point unless postprocessing is utilized. In a previous paper, refocusing and zoom-in PFA (RZPFA) for curvilinear spotlight SAR imaging were proposed to produce a refocused image for an arbitrary region of interest (ROI) with constant elevation. However, for certain applications, the residual distortion and defocus caused by rugged terrain could not be ignored. In this article, RZPFA is adapted to incorporate the known digital elevation model (DEM) into the imaging process, which is named as orthorectified PFA (OPFA). With just little additional computations than RZPFA, OPFA can realize georeferenced orthorectified imaging via a nonuniform fast Fourier transform of type 3 (NuFFT-3) without the need of postprocessing. The quantitative metrics for the residual DEM distortion and residual DEM defocus were also derived to determine the effective imaging extent of OPFA. Within the effective extent, the proposed OPFA can obtain an orthorectified image efficiently, and the image has a very high quality comparable to backprojection (BP). The imaging results of measured echo and DEM data demonstrated the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

High-resolution thermal infrared (TIR) remote sensing has a wide range of applications. In this paper, we describe the different applications and requirements identified in a literature review and during a consultation meeting with researcher experts in different fields. As a result, more than 30 applications were identified within three different fields: 1) land and solid Earth; 2) health and hazards; and 3) security and surveillance. A complete set of requirements (spatial, temporal, and radiometric resolution, algorithms used, and supporting data, among others) for each application is also provided. The results presented in this paper provide useful information to enhance the importance of high-resolution TIR data for civil applications and may serve as a reference document for future TIR mission concepts. [ABSTRACT FROM AUTHOR]

This paper introduces robust algorithms for extracting the ground points in laser scanning 3-D point cloud data. Global polynomial functions have been used for filtering algorithms for point cloud data; however, it is not suitable as it may lead to bias for the filtering algorithms and can cause misclassification errors when many different objects are present. In this paper, robust statistical approaches are coupled with locally weighted 2-D regression that fits without any predefined global function for the variables of interest. Algorithms are performed iteratively on 2-D profiles: $x-z$ and $y-z$. The $z$ (elevation) values are robustly down weighted based on the residuals for the fitted points. The new set of down-weighted $z$ values, along with the corresponding $x$ (or $y$) values, is used to get a new fit for the lower surface level. The process of fitting and down weighting continues until the difference between two consecutive fits is insignificant. The final fit is the required ground level, and the ground surface points are those that fall within the ground level and the level after adding some threshold value with the ground level for $z$ values. Experimental results are compared with the recently proposed segmentation method through simulated and real mobile laser scanning point clouds from urban areas that include many objects that appear in road scenes such as short walls, large buildings, electric poles, signposts, and cars. Results show that the proposed robust methods efficiently extract ground surface points with better than 97% accuracy. [ABSTRACT FROM PUBLISHER]

This paper deals with signal reconstruction for multichannel high-resolution wide-swath synthetic aperture radar (HRWS-SAR) systems. One of the commonly used algorithms for processing HRWS-SAR data is Krieger's digital beamforming (DBF) algorithm. This algorithm works quite well for cases with uniform space–time sampling as well as moderately nonuniform sampling. However, for cases with highly nonuniform sampling, Krieger's DBF algorithm in its original form suffers from a severe degradation in the signal-to-noise ratio, and for cases with coinciding sampling, this algorithm even fails to function. In this paper, we will solve these problems such that an improved DBF (IDBF) algorithm is developed. The key of the IDBF algorithm consists of two procedures: The first one implements an operation called ambiguity-index screening, which is based on two novel quantities, termed the equivalent sampling spacing and the effective reconstructed Doppler bandwidth; and the other key procedure implements a Doppler-spectrum weighting based on a newly defined parameter that is termed the maximum-to-minimum ratio of the noise spectrum. Simulation results show that the newly proposed IDBF algorithm shares a pronounced improvement in the imaging performance over Krieger's DBF algorithm. Moreover, simulation results also demonstrate that the IDBF algorithm exhibits a more robust and consistent overall performance than several other existing reconstruction algorithms. [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]

This paper describes a clutter suppression approach and the corresponding moving target imaging algorithm for a multichannel in azimuth high-resolution and wide-swath (MC-HRWS) synthetic aperture radar (SAR) system. Incorporated with digital beamforming processing, MC-HRWS SAR systems are able to suppress the Doppler ambiguities to allow for HRWS SAR imaging and null the clutter directions to suppress clutter for ground moving target indication. In this paper, the degrees of freedom in azimuth for the multichannel SAR systems are employed to implement clutter suppression. First, the clutter and moving target echoes are transformed into the range compression and azimuth chirp Fourier transform frequency domain, i.e., coarse-focused images formation, when the clutter echoes are with azimuth Doppler ambiguity. Considering that moving targets are sparse in the imaging scene and that there is a difference between clutter and a moving target in the spatial domain, a series of spatial domain filters are constructed to extract moving target echoes. Then, using an extracted moving target echo, two groups of signals are formed, and slant-range velocity of a moving target can be estimated based on baseband Doppler centroid estimation algorithm and multilook cross-correlation Doppler centroid ambiguity number resolving approach. After the linear range cell migration correction and azimuth focus processing, a well-focused moving target image can be obtained. In addition, the proposed clutter suppression and imaging approach is not only adapted for uniformly displaced phase center sampling but also for the nonuniform sampling cases. Some simulation experiments are taken to demonstrate our proposed algorithms. Finally, some real measured data results are presented to validate the theoretical investigations and the proposed approaches. [ABSTRACT FROM PUBLISHER]

Radio-frequency systems, such as TV and cellular networks, severely interfere with synthetic aperture radar (SAR) systems. Narrowband radio-frequency interference (RFI) has a special low-rank property in the received signal matrix, because it performs like a sinusoid with nearly invariant frequency as the slow time proceeds. Exploiting this special property, in this paper, we divide the received signal matrix into several small matrices, in each of which the RFI is also low rank. Without losing the connection between these small matrices, we stack them into a three-mode tensor to separate the low-rank RFI tensor and recover the informative signal tensor. Previous studies employed the nuclear norm to regularize the low-rank RFI, which is not a good choice. Hence, we propose two reweighted algorithms, the reweighted tensor nuclear norm (RTNN) and the reweighted tensor Frobenius norm (RTFN) algorithms, to approximate the rank function in a tensor and accurately extract the low-rank RFI tensor from the received signal tensor. As a result, the introduction of the tensor structure dramatically decreases the computational cost. Furthermore, the reweighted scheme helps suppressing the RFI and recovering the useful signal with excellent performance. Finally, real SAR data with measured RFI is employed to demonstrate the effectiveness of the proposed methods for RFI mitigation. [ABSTRACT FROM AUTHOR]

A squinted observation geometry along with long integration time significantly aggravates the range walk and spatial variation of a medium-earth-orbit (MEO) synthetic aperture radar (SAR) signal. Variable pulse repeating frequency (PRF) is recommended to avoid the blockage in echo recording and save storage space. The existing wavenumber algorithms cannot handle the nonlinear and range–azimuth-coupled spatial variation (RACSP) over a large scene. In this paper, we propose a modified Stolt mapping method along with a modified joint time and Doppler resampling (JTDR) for highly squinted MEO SAR data processing. An azimuth timescale transformation is used to deal with the nonlinear spatial variation of the azimuth frequency-modulation (FM) rate. An extended Omega-K is used to linearize the range frequency and achieve range cell migration correction (RCMC). To address the RACSP, the Doppler is linearized in the range-Doppler domain using a range-dependent Doppler scale transformation. The computational complexity and geometry distortion correction (GDC) are also discussed. Simulation results are shown to verify the effectiveness of the developed focusing approaches. [ABSTRACT FROM AUTHOR]

Wildfires are one of the most destructive disasters on the planet. They also significantly impact the land surface. Satellite data have been widely used to detect the outbreak and monitor the expansion of fire incidents for damage assessment and disaster management. Polar-orbiting satellite data have been used for several decades but data from geostationary satellites, which can provide observations with a high temporal resolution, have received much less attention. This paper utilizes data from FengYun-2G, a Chinese geostationary satellite, to detect wildfires in two selected research regions in January 2016. The detection algorithm systemizes image-based analysis to filter out obvious nonfire pixels and temporal analysis to confirm the true detections. Fire detection is based on comparisons between predicted and observed values. The results show that the proposed method has some advantages compared with the use of polar-orbiting satellite data, including early detection and continuous observation. The validation work is conducted based on the collection 6.1 Global Monthly Fire Location Product generated from fire detections by Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. The average accuracy within the target time is 56%, while the omission error rate is over 78%. In detail, the algorithm has a lower omission error rate in Australia while it fails in detecting most of the fire pixels in India. The dominance of small fire incidents, as well as low spatial resolution greatly limit the detection ability. Many small fires were beyond the ability of Stretched Visible and Infrared Spin Scan Radiometer (S-VISSR) data when no significant fire characteristics could be captured. Future development of the algorithm will focus on improving the results by enhancing the adaption to different regions, as well as, including multisource data sets. [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]

The shortage of training samples remains one of the main obstacles in applying the neural networks to the hyperspectral images classification. To fuse the spatial and spectral information, pixel patches are often utilized to train a model, which may further aggregate this problem. In the existing works, an artificial neural network (ANN) model supervised by centerloss (ANNC) was introduced. Training merely with spectral information, the ANNC yields discriminative spectral features suitable for the subsequent classification tasks. In this paper, we propose a novel convolutional neural network (CNN)-based spatial feature fusion (CSFF) algorithm, which allows a smart integration of spatial information to the spectral features extracted by ANNC. As a critical part of CSFF, a CNN-based discriminant model is introduced to estimate whether two pixels belong to the same class. At the testing stage, by applying the discriminant model to the pixel pairs generated by a test pixel and each of its neighbors, the local structure is estimated and represented as a customized convolutional kernel. The spectral–spatial feature is generated by a convolutional operation between the estimated kernel and the corresponding spectral features within a local region. The final label is determined by classifying the resulting spectral–spatial feature. Without increasing the number of training samples or involving pixel patches at the training stage, the CSFF framework achieves the state of the art by declining 20%–50% classification failures in experiments on three well-known hyperspectral images. [ABSTRACT FROM AUTHOR]

On May 31, 2003, the scan line corrector (SLC) of the Enhanced Thematic Mapper Plus (ETM+) on-board the Landsat-7 satellite failed, resulting in strips of data lost in all ETM+ images acquired since then. In this paper, we proposed a novel inpainting algorithm for recovering the ETM+ SLC-off images. The two slopes of the boundaries of each missing stripe were extracted through the Hough transform, ignoring the slope of the edge of the strip that overlaps the edge of the image. An adaptive dictionary was then developed and trained using ETM+ SLC-on images acquired before May 31, 2003 so that the physical characteristics and geometric features of the ground coverage of the data-missing strips can be considered during recovery. To make the algorithm computationally efficient, data-missing strips were repaired along their slope directions by using the logdet $\left ({\cdot }\right)$ low-rank nonconvex model along with the dictionary. The algorithm was tested using the simulated ETM+ SLC-off images created from a multiband ETM+ SLC-on image file and compared to the high accuracy low-rank tensor completion (HaLRTC), logDet, and tensor nuclear norm (TNN) algorithms. The results show that the ETM+ images restored using the new algorithm have lower RMSE, higher PSNR and structure similarity (SSIM) values, and better visualization. These results indicate that the new algorithm performs better than the other three algorithms and can efficiently and accurately restore the data-missing stripes. [ABSTRACT FROM AUTHOR]

For different applications with different requirements, many synthetic aperture radar (SAR) modes have been developed in the literature, such as, Terrain Observation by Progressive Scans (TOPS) SAR and sliding spotlight SAR. In this paper, we call TOPS SAR, sliding spotlight SAR, and spotlight SAR as beam steering SAR (BS-SAR for short). Comparing with stripmap SAR, BS-SAR can obtain a wide diversity of resolutions by increasing or reducing the azimuth synthetic time. Traditional polar formation algorithm (PFA) is an efficient algorithm which is mainly developed for spotlight SAR. The PFA has been validated to obtain well-focused results of raw data. In this paper, we extend the traditional PFA to process sliding spotlight SAR and TOPS SAR data, and we call it generalized PFA (GPFA). Comparing with the traditional PFA, GPFA contains a different azimuth deramping function and an additional azimuth scaling operation. The simulated and real data are used to validate the effectiveness of this method. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the constant false-alarm rate (CFAR) property of the RX anomaly detector which is widely used for the analysis of hyperspectral data. The RX detector relies on an adaptive scheme where the mean vector and the covariance matrix of the background are locally estimated from the image pixels themselves. First, demeaning is accomplished by removing the estimated local background mean value, and then, the covariance matrix is estimated in a homogeneous neighborhood of each pixel. In principle, if the local mean is perfectly removed and the covariance matrix is estimated from background pixels sharing the same covariance matrix, the RX algorithm has the CFAR property, which is highly desirable in practical applications. The CFAR behavior of the algorithm also requires the spatial stationarity of the random noise affecting the hyperspectral image. In data collected by new-generation sensors, such an assumption is not valid because photon noise contribution, which depends on the spatially varying signal level, is not negligible. This has motivated us to analyze the behavior of the RX algorithm with respect to the CFAR property in data affected by signal-dependent (SD) noise. In this paper, we show both theoretically and experimentally that the SD noise is one of the causes of the non-CFAR behavior of the RX detector that we have experienced in many practical situations. We propose a strategy to enhance the robustness of the anomaly detection scheme with respect to the CFAR property based on an adaptive nonlinear transform aimed at reducing the dependence of the noise on the signal level. Experiments on simulated data and real data collected by a new hyperspectral camera are also presented and discussed. [ABSTRACT FROM AUTHOR]

In this paper, a novel nonlinear technique for hyperspectral image (HSI) classification is proposed. Our approach relies on sparsely representing a test sample in terms of all of the training samples in a feature space induced by a kernel function. For each test pixel in the feature space, a sparse representation vector is obtained by decomposing the test pixel over a training dictionary, also in the same feature space, by using a kernel-based greedy pursuit algorithm. The recovered sparse representation vector is then used directly to determine the class label of the test pixel. Projecting the samples into a high-dimensional feature space and kernelizing the sparse representation improve the data separability between different classes, providing a higher classification accuracy compared to the more conventional linear sparsity-based classification algorithms. Moreover, the spatial coherency across neighboring pixels is also incorporated through a kernelized joint sparsity model, where all of the pixels within a small neighborhood are jointly represented in the feature space by selecting a few common training samples. Kernel greedy optimization algorithms are suggested in this paper to solve the kernel versions of the single-pixel and multi-pixel joint sparsity-based recovery problems. Experimental results on several HSIs show that the proposed technique outperforms the linear sparsity-based classification technique, as well as the classical support vector machines and sparse kernel logistic regression classifiers. [ABSTRACT FROM AUTHOR]

This paper presents the results of a study designed to evaluate the performance of attenuation correction algorithms due to heavy precipitation at operational C-band weather radar frequencies and using differential phase measurements. This paper proposes the extension of two attenuation correction algorithms and shows a methodology to validate attenuation-corrected reflectivity measurements by using a nearby radar as opposed to the traditional use of rain gauge or disdrometer measurements. The results showed that the the adaptive attenuation correction techniques have a higher performance than the nonadaptive ones when comparing the measured and computed ΦDP profiles and also when comparing with reflectivity measurements from a nearby radar. The results also showed that the difference in the performance of the adaptive attenuation correction algorithms is minimal and optimizing a single parameter (e.g., α in the BRI algorithm) rather than three unknown parameters (e.g., a, b, and α in the AHB or AFV algorithms) is a more robust procedure. [ABSTRACT FROM AUTHOR]

In this paper, a fast a priori knowledge-based ground moving target indication and parameter estimation algorithm applicable to single- as well as to multichannel synthetic aperture airborne radar data is presented. The algorithm operates directly on range-compressed data. Only the intersection points of the moving vehicle signals with the a priori known road axes, which are mapped into the range-compressed data array, are evaluated. For moving vehicle detection and parameter estimation, basically only a single 1-D fast Fourier transformation has to be performed for each considered road point. Hence, the required computational power is low, and the algorithm is well suited for real-time traffic monitoring applications. The proposed algorithm enables the estimation of the position and velocity vectors of detected moving vehicles independent of the number of channels. A single-channel synthetic aperture radar system may be sufficient in case of fast moving vehicles. The paper includes a detailed performance assessment together with experimental results that demonstrate the applicability in a real-world scenario. [ABSTRACT FROM AUTHOR]

For stepped frequency continuous wave ground penetrating radar (SFCW GPR), the image of buried targets is usually reconstructed by a combination of point-like scatters whose number is much smaller than that of pixels of target space image. The intrinsic sparseness of target space offers a migration imaging method to make the high-quality image of underground region based on compressive sensing (CS) theory. In this paper, the effects of air/ground interface and antenna beamwidth on CS-based SFCW GPR migration imaging are presented and analyzed. It is shown that the presence of the strong air/ground interface reflection and finite antenna beamwidth usually challenges the robust CS migration imaging algorithm in practice. To overcome this problem in the context of CS migration imaging, an improved CS migration imaging method for SFCW GPR system is proposed in this paper. Experimental results show that the approach is robust to deliver a high-quality image of underground region. [ABSTRACT FROM PUBLISHER]

In this paper, a real-time algorithm that operates on data provided by a network of weather radars in the form of moments is presented. The algorithm implementation, performance, and abilities are presented and discussed. As part of this paper, a methodology for mapping from native spherical coordinates to a common network grid is developed and demonstrated. Metrics which gauge the quality of mapping and quality of data are derived, and their use from a network point of view is demonstrated. The main product of the algorithm, which is an implementation of a vector velocity estimation technique based on a maximum likelihood approach, is shown, and its results are displayed, compared, and discussed. The application of a methodology for velocity unfolding and reflectivity display enhancement is presented as well. [ABSTRACT FROM AUTHOR]

Following calibration, hyperspectral images remain affected by spatial dimension nonuniformity, i.e., stripe noise, due to stray light interference, slit contamination, and instrument instability. The full spectrum airborne hyperspectral imager (FAHI) is a Chinese next-generation pushbroom sensor with a spectral range covering the visible near-infrared, shortwave-infrared (SWIR), and thermal infrared regions with spectral sampling intervals of 2.34, 3, and 32 nm, respectively. However, the residual stripe noise remains in FAHI images after relative radiometric correction based on the laboratory calibration, especially in low signal-to-noise ratio bands. To solve this problem, a new technique combining image statistics and spatial filtering algorithms has been developed for FAHI image correction. In this method, image statistics are obtained to calculate the gain and offset of each pixel for image nonuniformity correction. Then, a spatial filter removes the residual stripes. This paper presents the principles of this method along with details of validation experiments and results. To validate the effectiveness of the proposed method, comparison with two destriping methods and quantitative analyses are carried out. Moreover, the method is applied to an SWIR hyperspectral image from the TianGong-1 spacecraft, yielding good results. The experimental results suggest that the proposed method is convenient and practical for improving the relative radiometric accuracy of airborne/spaceborne hyperspectral images. [ABSTRACT FROM AUTHOR]

In this paper, we propose a long-time coherent integration method for a maneuvering target with the first-, second-, and third-order range migrations, and the complex Doppler frequency migration. In this method, after range compression, the echo signal is first transformed into the range-frequency and Doppler domain based on series reversion, and then an azimuth matched filtering procedure is implemented in the 2-D frequency domain. It can eliminate the coupling effects between range and azimuth jointly caused by the radial velocity, radial acceleration, and radial acceleration rate of a moving target. Due to the linear transform property, the proposed method can work well under low signal-to-clutter and noise ratio. In addition, the Doppler ambiguities, when target azimuth spectrum either is within a pulse repetition frequency (PRF) or spans over neighboring PRF bands, can be well solved. Both simulated and real synthetic aperture radar data processing results are provided to validate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Hyperspectral unmixing has attracted much attention in recent years. Single sparse unmixing assumes that a pixel in a hyperspectral image consists of a relatively small number of spectral signatures from large, ever-growing, and available spectral libraries. Joint-sparsity (or row-sparsity) model typically enforces all pixels in a neighborhood to share the same set of spectral signatures. The two sparse models are widely used in the literature. In this paper, we propose a joint-sparsity-blocks model for abundance estimation problem. Namely, the abundance matrix of size $m\times n$ is partitioned to have one row block and $s$ column blocks and each column block itself is joint-sparse. It generalizes both the single (i.e., $s=n$) and the joint (i.e., $s=1$) sparsities. Moreover, concatenating the proposed joint-sparsity-blocks structure and low rankness assumption on the abundance coefficients, we develop a new algorithm called joint-sparse-blocks and low-rank unmixing. In particular, for the joint-sparse-blocks regression problem, we develop a two-level reweighting strategy to enhance the sparsity along the rows within each block. Simulated and real-data experiments demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

A newly designed grounded electrical-source airborne transient electromagnetics (GREATEM) system was introduced recently. Detailed data preprocessing techniques to acquire the high-precision measured magnetic field are discussed here. Different from the previous work in which the reconstruction of the underground structure is performed in 1-D, we interpret the GREATEM data in 3-D by the volume integral equation (VIE) method in the frequency domain. Therefore, the VIE in the forward electromagnetic scattering model is formulated in the low-frequency regime. It is solved by using the stabilized biconjugate gradient fast Fourier transform (BCGS-FFT) method. In the nonlinear inversion, the Born iterative method (BIM) and the conjugate gradient method are adopted to minimize the cost function. A synthetic model of GREATEM survey is used to validate the proposed 3-D forward and inversion algorithms. Then, the field data from two GREATEM surveys are used to test the effectiveness and accuracy of the proposed inversion algorithm. The reconstructed conductivity structures are consistent with geological drilling results, confirming the potential of our method for solving the 3-D GREATEM inversion problems in geophysical engineering applications. This paper represents the first application of the BCGS-FFT and BIM algorithms to a GREATEM system. [ABSTRACT FROM AUTHOR]

With the rapid increase of remote sensing images in temporal, spectral, and spatial resolutions, it is urgent to develop effective techniques for joint interpretation of spatial-temporal images. Multitype change detection (CD) is a significant research topic in multitemporal remote sensing image analysis, and its core is to effectively measure the difference degree and represent the difference among the multitemporal images. In this paper, we propose a novel difference representation learning (DRL) network and present an unsupervised learning framework for multitype CD task. Deep neural networks work well in representation learning but rely too much on labeled data, while clustering is a widely used classification technique free from supervision. However, the distribution of real remote sensing data is often not very friendly for clustering. To better highlight the changes and distinguish different types of changes, we combine difference measurement, DRL, and unsupervised clustering into a unified model, which can be driven to learn Gaussian-distributed and discriminative difference representations for nonchange and different types of changes. Furthermore, the proposed model is extended into an iterative framework to imitate the bottom-up aggregative clustering procedure, in which similar change types are gradually merged into the same classes. At the same time, the training samples are updated and reused to ensure that it converges to a stable solution. The experimental studies on four pairs of multispectral data sets demonstrate the effectiveness and superiority of the proposed model on multitype CD. [ABSTRACT FROM AUTHOR]

This paper presents a new method based on recent optimization technique to detect slow-moving landslides (<150m/year) in time series of displacement field generated by satellite images. Sparse optimization is applied simultaneously on the 3-D data set in space as well as in time. The proposed method takes into account the distinctive signal physical properties in space and time, by enforcing a sparse representation by blocks in space, but a continuing and monotonous representation in time of the landslides. As a result, we show that a mixed $\ell _{1,2}$ -norm is the most suitable norm for this detection problem, compared to pure $\ell _{1}$ -norm or $\ell _{2}$ -norm. Moreover, an outlier estimation step is included that sets apart the Gaussian noise from locally sparse processing errors in the data. The performance of this approach is tested by applying it both on synthetic data and on a time series of displacements fields over 16 dates in the Colca Valley, Peru. This detection presents commission and omission errors for landslides of 29% and 14%, respectively, using a medium resolution (10 m) data set of optical satellite images. It detects all important landslides, already known from field investigations. Moreover, it also points out other smaller or unknown landslides, increasing the existing slow-moving landslide inventory by +50%. [ABSTRACT FROM AUTHOR]

A rain-gauge-adjusted algorithm for global satellite mapping of precipitation (GSMaP) that estimates the surface precipitation rate with resolutions of 0.1° and 1 h over the globe is described herein. Precipitation is one of the most important parameters of the earth’s system, and its global distribution and changes are essential data for modeling the water cycle, maintaining ecosystems, increasing agricultural production, improving weather forecasting precision, and implementing flood warning systems. In the Global Precipitation Measurement project, integrated products of high-resolution mapping of precipitation, obtained from microwave measurements made by a constellation satellite and infrared radiometers in geostationary orbit, are developed and supplied to the public. However, these high-resolution products, such as GSMaP_MVK, sometimes underestimate surface precipitation, introducing large errors into hydrological modeling. This paper combines the global gauge data set with GSMaP_MVK, using a new algorithm [gauge-adjusted GSMaP (GSMaP_Gauge)], described and evaluated herein using local radar and rain-gauge data sets. This algorithm outperforms other GSMaP products in all validation tests. [ABSTRACT FROM AUTHOR]

Despeckling is a fundamental preprocessing step for applications using polarimetric synthetic aperture radar data in most cases. In this paper, a guided filter with nonlinear weight kernels and adaptive filtering windows is presented for PolSAR image despeckling, in which the guidance image is constructed by a weighted average using the statistical traits of the speckled image. The output result is then estimated by another weighted average, with the aid of the fully polarimetric information from both the guidance image and the speckled image. In the experimental part, the filtering results obtained with both simulated and real PolSAR images reveal the positive performance of the proposed method in both reducing speckle and retaining details, when compared with some of the state-of-the-art algorithms. Furthermore, the relatively low computational complexity is another strength of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the temperature and emissivity separation (TES) problem from hyperspectral data acquired in the long-wave infrared region (LWIR) of the electromagnetic spectrum. We derive a general class of TES algorithms [subspace-based TES (SBTES)] relying on the assumption that the emissivity spectra of natural and man-made materials can be well represented in a given subspace of the original data space. Specifically, by exploiting the subspace representation and the Gaussian model for the noise affecting LWIR hyperspectral data, we approach TES under a statistical perspective by obtaining the maximum likelihood estimates of both the temperature and the spectral emissivity. The proposed approach originates several algorithms whose specific form depends on the particular basis matrix adopted to address the emissivity subspace. We study the performance of the presented class of algorithms and derive theoretical bounds on the accuracy of the temperature and emissivity estimators. Furthermore, by specifying two basis matrices for the emissivity subspace, we propose two different algorithms within the SBTES class. Finally, we present the results of an extensive experimental analysis carried out over simulated data to assess and compare the performance of the two presented algorithms. [ABSTRACT FROM AUTHOR]

Band selection is an effective approach to mitigate the “Hughes phenomenon” of hyperspectral image (HSI) classification. Recently, sparse representation (SR) theory has been successfully introduced to HSI band selection, and many SR-based methods have been developed and shown great potential and superiority. However, due to the inherent limitations of the SR scheme, i.e., individually representing each band with only a few other bands from the same subspace, the SR-based methods cannot effectively capture the global structures of the data, which limit the band selection performance. In this paper, to overcome this obstacle, the novel Laplacian-regularized low-rank subspace clustering (LLRSC) algorithm is proposed for HSI band selection. On the one hand, the low-rank subspace clustering model is introduced to capture the global structure information for the learned representation coefficient matrix and deal with the HSI band selection task in the clustering framework. On the other hand, considering the high correlation between adjacent bands, 1-D Laplacian regularization is utilized to incorporate the neighboring band information and further reduce the representation bias. Lastly, an eigenvalue analysis algorithm based on band mutation information is utilized to estimate the appropriate size of the band subset. The experimental results indicate that the proposed LLRSC algorithm outperforms the other state-of-the-art methods and achieves a very competitive band selection performance for HSIs. [ABSTRACT FROM AUTHOR]

Establishing causal relations between random variables from observational data is perhaps the most important challenge in today’s science. In remote sensing and geosciences, this is of special relevance to better understand the earth’s system and the complex interactions between the governing processes. In this paper, we focus on an observational causal inference, and thus, we try to estimate the correct direction of causation using a finite set of empirical data. In addition, we focus on the more complex bivariate scenario that requires strong assumptions and no conditional independence tests can be used. In particular, we explore the framework of (nondeterministic) additive noise models, which relies on the principle of independence between the cause and the generating mechanism. A practical algorithmic instantiation of such principle only requires: 1) two regression models in the forward and backward directions and 2) the estimation of statistical independence between the obtained residuals and the observations. The direction leading to more independent residuals is decided to be the cause. We instead propose a criterion that uses the sensitivity (derivative) of the dependence estimator, the sensitivity criterion allows to identify samples most affecting the dependence measure, and hence, the criterion is robust to spurious detections. We illustrate the performance in a collection of 28 geoscience causal inference problems, a database of radiative transfer models simulations and machine learning emulators in vegetation parameter modeling involving 182 problems, and assessing the impact of different regression models in a carbon cycle problem. The criterion achieves the state-of-the-art detection rates in all cases, and it is generally robust to noise sources and distortions. The presented approach confirms the validity in observational bivariate problems in the earth sciences. [ABSTRACT FROM AUTHOR]

Relationships between vegetation indices (VIs) and leaf area index (LAI) tend to saturate in the nadir direction, and vary with crop canopy structure and view zenith angles (VZAs). The objective of this paper was to improve the monitoring accuracy and angular stability of VIs for estimating LAI using multiangular remote sensing data. The relationship between LAI and ground-based hyperspectral spectral reflectance was quantified in winter wheat (Triticum aestivum L.) exhibiting erectophile and planophile growth habits. To further reduce the saturation, species specificity, and angular sensitivity, we developed a saturation factor (SF), based on near-infrared and green bands. Multiplying all VIs by SF greatly improved the association with LAI across all VZAs ($R^{2} = 0.73{-}0.82$). Most VI $\times$ SF values, particularly optimized soil-adjusted VI $\times$ SF, were able to construct universal algorithms across VZAs for accurate estimation of LAI due to the sensitivity of SF to LAI in a dense canopy, and the insensitivity of SF to view effects with larger VZAs. This approach is also promising for the exploitation of multiangular satellite data for the design and calibration of nonview-angle-corrected spectral reflectance, for which the sensor is only deployed at fixed observation direction. [ABSTRACT FROM AUTHOR]

Multitemporal airborne and satellite imagery data with frequent repeat coverage provide great capability for change detection (CD). When comparing two images taken at different times of day or in different seasons for CD, the variation of topographic shades and shadows caused by the change of sunlight angle can be so significant that it overwhelms the real object and environmental changes, making automatic detection unreliable. An effective CD algorithm, therefore, has to be robust to the illumination variation. In this paper, the robustness of phase correlation (PC) to shadow effects is proven via mathematical analysis, and then, an illumination-invariant change detection (IICD) metric is proposed based on pixel-wise dense PC matching. In the proposed IICD method, a graph-based visual saliency map is introduced for the initial CD followed by an active contour-based segmentation to precisely quantize the change region. Compared to the state-of-the-art CD algorithms, experiments using daily images of a landscape model and Landsat satellite images demonstrate that only the proposed method can effectively detect and precisely segment appearance changes under daily and seasonal sunlight changes. [ABSTRACT FROM AUTHOR]

The advanced Himawari imager (AHI) aboard the Himawari-8 geostationary satellite provides high-frequency observations with broad coverage, multiple spectral channels, and high spatial resolution. In this paper, AHI data were used to develop an algorithm for joint retrieval of aerosol optical depth (AOD) over land and land surface bidirectional reflectance. Instead of performing surface reflectance estimation before calculating AOD, the AOD and surface bidirectional reflectance were retrieved simultaneously using an optimal estimation method. The algorithm uses an atmospheric radiative transfer model coupled with a surface bidirectional reflectance factor (BRF) model. Based on the assumption that the surface bidirectional reflective properties are invariant during a short time period (i.e., a day), multiple temporal AHI observations were combined to calculate the AOD and surface BRF. The algorithm was tested over East Asia for year 2016, and the AOD retrieval results were validated against the aerosol robotic network (AERONET) sites observation and compared with the Moderate Resolution Imaging Spectroradiometer Collection 6.0 AOD product. The validation of the retrieved AOD with AERONET measurements using 14 713 colocation points in 2016 over East Asia shows a high correlation coefficient: $R = 0.88$ , root-mean-square error = 0.17, and approximately 69.9% AOD retrieval results within the expected error of $\pm 0.2\cdot {\mathrm {AOD}}_{\mathrm {AERONET}}\pm 0.05$. A brief comparison between our retrieval and AOD product provided by Japan Meteorological Agency is also presented. The comparison and validation demonstrates that the algorithm has the ability to estimate AOD with considerable accuracy over land. [ABSTRACT FROM AUTHOR]

This paper presents a processing framework to separate moving targets from the clutter, under multichannel synthetic aperture radar (SAR) scenarios, and addresses the moving target imaging and velocity estimation problems for ground moving target indication (GMTI) applications. A practical implementation is introduced to break the SAR/GMTI problem into two processing stages, and the sparsity of the moving targets in the observed scene is exploited throughout the stages. The two-stage process extracts the moving targets from the monitored region via a sparsity-based iterative decomposition algorithm and subsequently estimates the complete velocity vectors of moving targets by enforcing sparsity constraints. The model is sufficiently versatile to incorporate digital elevation map information, which further improves the moving target relocation accuracy. The effectiveness of the presented framework is demonstrated using the Air Force Research Laboratory Gotcha GMTI challenge data. [ABSTRACT FROM AUTHOR]

This paper presents an in-depth study concerning the development of a sea state bias (SSB) model designed with three parameters exclusively derived from altimeter data and globally applied to all reference altimeter missions. The proposed technique, first tested for the Jason-1 mission, proves to have a good performance for a wide range of ocean conditions when compared with the state-of-the-art SSB corrections currently in use. In addition to the significant wave height ($H_{s}$) and wind speed ($U~_{{10}}$), a third predictor acting as a mediator parameter gathered by the mean wave period ($T_{z}$) has been used. Two different empirical algorithms for altimeter ocean wave period have been tested and implemented, improving the SSB model performance in some ocean regions. The methodology relies on nonparametric modulation and statistical techniques based on smoothing splines embedded in a generalized additive model. This SSB modeling approach shows good performance when applied to all reference missions, in particular to TOPEX and Jason-2 missions, slightly reducing the explained variance of sea-level anomaly (SLA) when compared with the established SSB models. The approach is computationally efficient, capable of generating a stable SSB model using a small training data set when little information is available, as is the case with the recent Jason-3 mission. Model performance is assessed by comparison with existing SSB corrections for each reference mission, intercomparisons during the period of the tandem phases, and by SLA variance analysis, providing a consistent set of SSB corrections for the four reference missions. [ABSTRACT FROM AUTHOR]

The airborne Operation IceBridge and spaceborne CryoSat-2 missions observe polar sea ice at different spatial and temporal scales as well as with different sensor suites. Comparison of data products from IceBridge and CryoSat-2 is complicated by the fact that they use different geophysical corrections: reference ellipsoid, geoid model, tide model, and atmospheric corrections to derive surface elevation and sea-ice freeboard. In this paper, we compare sea-ice surface elevation and freeboard using eight coincident CryoSat-2, Airborne Topographic Mapper (ATM), and Land, Vegetation, and Ice Sensor (LVIS) observations with direct IceBridge underflights of CryoSat-2 ground tracks. We apply identical geophysical corrections to CryoSat-2 and IceBridge data to eliminate elevation biases due to these differences and focus on differences due to retracker performance. The IceBridge ATM and LVIS elevation and freeboard and Snow Radar snow depth data sets are averaged to each CryoSat-2 footprint for comparison. With snow depth measurements, we are able to compare elevations and freeboards at the snow/ice interface for five different CryoSat-2 retrackers (ESA, GSFCv1, AWI, JPL, and GSFCv2) and IceBridge. The overall mean of freeboard differences between GSFCv2, ESA, AWI, JPL retrackers, and ATM are in agreement within 0.05 m. However, the five different CryoSat-2 retrackers show distinct differences in mean elevation over leads and over floes. This suggests that the physical interpretation of the different retrackers needs to be considered depending on usage, for example, elevations from CryoSat-2 retrackers need to be carefully calibrated before comparing with elevation from other satellites for long-term surface elevation trends. [ABSTRACT FROM AUTHOR]

Investigations in data quality and uncertainty modeling are becoming key topics in geoinformation science. This paper models a collection of outcomes from a standard segmentation algorithm as a random set. It quantifies extensional uncertainties of extracted objects using statistical characteristics of random sets. The approach is applied to a synthetic data set and vegetation patches in the Poyang Lake area in China. These patches are of interest as they have both sharp and vague boundaries. Results show that random sets provide useful spatial information on uncertainties using their basic parameters like the mean, level sets, and variance. The number of iterations to achieve a stable covering function and the sum of the variances are good indicators of boundary sharpness. The coefficient of variation has a positive relation with the degree of uncertainty. An asymmetry ratio reflects the uneven gradual changes along different directions where broad boundaries exist. This paper shows that several characteristics of extensional uncertainty of segmented objects can be quantified numerically and spatially by random sets. [ABSTRACT FROM AUTHOR]