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Secondary craters (i.e., secondaries) formed by the landing of impact ejecta are an important crater population in planetary geology. Typical secondaries on planetary surfaces occur in chains and clusters, and they have irregular-shaped crater rims and/or floors. Background secondaries are a special type of secondaries that were formed by more-dispersed impact ejecta, and they have similarly circular morphology and dispersed spatial distribution with same-sized primary craters (i.e., primaries) that are formed by asteroids and comets. While individual background secondaries have been recognized at different planetary bodies, there is no reliable method to differentiate populations of background secondaries from those of primaries. In this work, we design a novel method to extract background secondaries based on multiscale and multifacies topography characteristics of impact craters. To characterize the complex topography fluctuations across continuous ejecta deposits of secondaries, we design a multiwavelet method to present their full-wavelength topography variations. Selecting three populations of secondaries formed by the Orientale basin and a population of primaries, we investigate and compare their crater interior slopes and roughness of continuous ejecta deposits, determining the threshold ranges that can differentiate background secondaries from same-sized and coeval primaries. The two sets of threshold values are then fused together to create a standard T-score terrain indicator, which are combined with our modeled theoretical spatial distribution of secondaries, and abundant previously unrecognized Orientale background secondaries were discovered. Our results demonstrate that the production of background secondaries is much more efficient than manifested by the observed spatial density of obvious secondaries in chains and clusters. This study establishes a promising method to detect populations of background secondaries, which can be extended further to smaller primary craters on various airless planetary bodies. [ABSTRACT FROM AUTHOR]

Predicting wildfire spread is critical for land management and disaster preparedness. To this end, we present “Next Day Wildfire Spread,” a curated, large-scale, multivariate dataset of historical wildfires aggregating nearly a decade of remote-sensing data across the United States. In contrast to existing fire datasets based on Earth observation satellites, our dataset combines 2-D fire data with multiple explanatory variables (e.g., topography, vegetation, weather, drought index, and population density) aligned over 2-D regions, providing a feature-rich dataset for machine learning. To demonstrate the usefulness of this dataset, we implement a neural network that takes advantage of the spatial information of these data to predict wildfire spread. We compare the performance of the neural network with other machine learning models: logistic regression and random forest. This dataset can be used as a benchmark for developing wildfire propagation models based on remote-sensing data for a lead time of one day. [ABSTRACT FROM AUTHOR]

High spectral dimensionality of hyperspectral image (HSI) has brought great redundancy for data processing. Band selection (BS), as one of the most commonly used dimension reduction (DR) techniques, attempts to remove the redundant spectral bands, while maintaining good classification or detection rate for later applications. Gray wolf optimizer (GWO) algorithm is a meta-heuristic algorithm, and it is used for HSI BS. However, the convergence factor of the basic GWO is linearly decreased, leading to a slower convergence speed and increasing the probability of falling into local optimality. This article proposes a new hybrid gray wolf optimizer (HGWO) algorithm for HSI BS, which uses adaptive decreasing convergence factor instead of linear convergence factor to improve GWO convergence rate and combines category separability for initialization to avoid local optimality. Five nonlinear functions are used to test the convergence of the proposed HGWO algorithm, compared with the state-of-the-art optimization algorithms. Finally, the experimentations are performed on three widely used real hyperspectral datasets for HSI classification, and the experimental results show that band subsets selected by the proposed HGWO algorithm can obtain better classification accuracy compared with other global optimization algorithms. [ABSTRACT FROM AUTHOR]

A new hybrid inversion methodology for petrophysical properties (porosity, water saturation, and volume of clay) based on partially stacked seismic angle gathers was presented. The innovation of this method is that it combines the advantages of high efficiency of analytical and semianalytical multistep petrophysical estimation with the advantages of spatial continuity and high resolution of geostatistical direct inversion of reservoir parameters. A trace-by-trace linearized amplitude versus offset (AVO) inversion and semianalytical petrophysical estimation approach could efficiently achieve a preliminary estimation of reservoir parameters under some assumptions and approximations. These laterally unconstrained preliminary estimation results are able to provide effective information for finer-resolution geostatistical simulation and improve the computational efficiency of stochastic optimization. The 3-D fast Fourier transform moving average (FFT-MA) geostatistical cosimulation and the stochastic optimization by gradual deformation method (GDM) improve the resolution and spatial continuity of the preliminary estimation results. At the same time, stochastic inversion overcomes the assumptions in the analytical inversion algorithm that the parameters need to conform to the logarithmic Gaussian distribution and the linear approximation of the Zoeppritz equation. The method was tested on a synthetic case and a real case. The results show that compared with the analytical and semianalytical methods, this hybrid inversion can obtain results with finer resolution and better spatial continuity, and the computational efficiency is significantly improved compared with the geostatistical direct inversion of reservoir parameters. Uncertainty evaluation can be obtained by performing statistical analysis on the multiple realizations of stochastic inversions. [ABSTRACT FROM AUTHOR]

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

Passive operation is an important operational concept for future airborne radar platforms. Successful target detection requires an understanding of the underlying clutter statistics in order to set the detection threshold accurately. In addition, clutter statistics are important for modeling of passive radar performance and simulation of passive bistatic radar to aid in the development of new target detection algorithms. In this article, the clutter statistics of data from an airborne platform are studied over a range of bistatic geometries and receive polarizations. The illuminator is a digital video broadcast-terrestrial (DVB-T) station and the collected data spans both ground and maritime regions. The key contributions include a study of amplitude statistics using a number of common distributions and understanding the impact of the reference signal quality in terms of the clutter statistics. [ABSTRACT FROM AUTHOR]

With the ability to locate subtle trace objects in the large-scale region, coherent change detection (CCD) has been vital research for a synthetic aperture radar (SAR) system. Finding the difference between repeat-pass repeat-geometry SAR image pair and extracting impressive trace pixels from difference image, the SAR CCD methods consist of a difference generation module and a difference analysis module. The previous CCD methods mainly pay attention to devising a sophisticated working system or an appropriate statistic model to generalize a well difference image. In this article, we introduce the deep learning method into the CCD algorithm and propose a novel trace detection paradigm, which works by hierarchically fusing the unsupervised coherent statistics model and supervised deep learning model. To be specific, the complex reflectance change detection estimator is introduced to generate a difference image and reduce the false alarm in the low clutter-to-noise region. Since the low correlation in a difference image caused by the natural factors severely affects the detection performance, the multiple statistics based on intensity summation and intensity difference are, respectively, proposed to extract water region and vegetation region and suppress the corresponding false alarm. Then the construction of the coarse-to-fine image makes use of land cover information and trace features while the compressed Unet improves the utilization efficiency of trace samples. Meanwhile, the inductive transfer learning based on unsupervised pretraining and few labeled trace samples helps to train a well detection model. Experiments on measured SAR data demonstrate the effectiveness of proposed methods. [ABSTRACT FROM AUTHOR]

We consider time-lapse seismic amplitude versus offset (AVO) inversion for the reservoir properties porosity and water saturation, with a focus on monitoring the evolution of water saturation in a dynamic setting of ongoing production. We operate in a Bayesian framework based on the integration of seismic AVO modeling and rock physics relations. One major challenge in the inversion of seismic data for reservoir properties is the multimodality of these properties. Fluid saturation is generally bimodal due to the gravity effect, and often distinctly so, with abrupt spatial mode transitions. The novelty of the proposed approach is the assumption of a selection Gaussian random field (S-GRF) for the prior spatial model of porosity and water saturation, which can represent the multimodal characteristics of these reservoir properties. The likelihood model is Gauss-linear and based on linearized seismic and rock physics models, which entails that the associated posterior model is also an S-GRF, with analytically assessable parameters. Hence, the posterior model is capable of representing multimodality and abrupt spatial mode transitions. Two realistic case studies are considered; the production of an oil reservoir in the North Sea, and the injection of CO2 into a subsurface potential CO2 storage unit. Focus is on the movement of the oil–water-contacts along a vertical profile in the first case, and on the expansion of the CO2 region in a cross section in the other, both of which can be inferred from the changes in the water saturations. The results are considered to be very encouraging and the proposed statistical formulation appears to be particularly well suited for fluid monitoring problems of the described type. [ABSTRACT FROM AUTHOR]

In this article, we present an innovative space–time adaptive multilooking technique that operates on a sequence of multitemporal, differential synthetic aperture radar interferograms. The developed approach relies on the application of the fundamentals of directional statistics theory. At variance with other methods that identify the set of statistically homogenous pixels (SHPs) within a multilooking (complex averaging) window based on the statistics of the single-look-complex (SLC) SAR images, the proposed method is exclusively based on the analysis of the multitemporal sequence of full resolution DInSAR interferograms. The SHPs are then used to generate spatially adaptive multilooked interferograms both at the native, full-scale grid of the SLC images and at the multilooked resolution scale. The algorithm is effective and simple to implement, only requiring the availability of a sequence of full-scale differential SAR interferometry (DInSAR) interferograms. The interferograms can then be used to generate ground displacement time-series through advanced multitemporal interferometric SAR (MTInSAR) approaches. Experimental results obtained by applying the adopted technique to two SAR data sets acquired at X- and L-band, respectively, demonstrate the validity of the developed method. [ABSTRACT FROM AUTHOR]

Increasing amounts of polarimetric synthetic aperture radar (PolSAR) images from different sensors covering different scenes are available, but limited labeled samples and trained models can hardly work well in these cross-domain data interpretations. Fortunately, domain adaptation (DA) can transfer knowledge in existing images to new yet related images. DA shows attractive potential for PolSAR classification, and it is still challenging due to more complex domain shifts caused by different sensors, imaging conditions, and distributions. Inspired by the widely applicable polarimetric scattering mechanisms and DA ability of subspace alignment (SA), this article is devoted to constructing a robust unsupervised cross-domain PolSAR classification framework, by exploring scattering and statistical characteristics mapping between the source and target domains. First, classical scattering components of both source and target data were extracted, and Wishart clustering was adopted to derive the statistical information of scattering components at patch level. Second, the intrinsic polarimetric scattering components were estimated and extracted, which were called statistical scattering components (SSCs). Third, by applying SA, the source SSC was aligned with target SSC, and domain shift was further reduced. Finally, the target PolSAR image was classified based on labeled samples from source domain, and unsupervised cross-domain classification was achieved by SSC-based SA (SSC-SA). The unsupervised cross-domain experiments are conducted on 49 units among 11 data sets, including Radarsat-2, Gaofen-3, AIRSAR, and Pi-SAR images. With randomly selected labeled samples (about 2%–10%) from source domain, the accuracies of the proposed cross-domain classifications range between 80.20% and 95.64%. Also, the proposed SSC feature pattern is proved extensible for other polarimetric basis and decompositions. [ABSTRACT FROM AUTHOR]

Using the Global Navigation Satellite System (GNSS) differenced total electron content (dTEC) series, the traveling ionosphere disturbances (TIDs) of 22 typhoons registered in Taiwan/Japan between 2013 and 2016 were studied. The horizontal speed of the first TID during a typhoon landing can be estimated by a two-station method with the ionosphere anomaly indicator in total electron count units (TECUs) (|dTEC| ≥ 0.15 TECU). The horizontal speed of the TIDs was from 155 to 210 m/s and with an average speed of 168.70 m/s. The estimated TID speeds of Typhoons Soudelor (205.93 m/s) and Megi (158.47 m/s) are not consistent with each other, even though they had very similar trajectories when cross through Taiwan Island. Moreover, the propagation velocity of the typhoon ionospheric anomaly showed a significant positive correlation (r = 0.78 , α = 0.05) with the change rate of the typhoon central air pressure and a negative correlation (r = −0.52 , α = 0.05) with the central pressure before landing. Gravity waves were generated by land friction, terrain blocking, and strong wind shear transport energy into the atmosphere from the near surface to the mesosphere and thermosphere, which is the main cause of ionosphere disturbances during typhoon landing. [ABSTRACT FROM AUTHOR]

Existing 3-D object extraction methods on terrestrial laser point clouds are further developed through filtering and labeling. However, such predefined features are heuristically designed to process generic object point clouds. Thus, existing abilities are insufficient to handle specific rock-mass point clouds. Given the complexity and diversity of terrestrial environments, the effective removal of vegetation points from rock-mass point clouds is particularly challenging. To address such problems, this study presents a novel approach for 3-D rock-mass point clouds labeling by using convolutional feature learning based on distribution priors with multiple scales and views. First, to extract discriminative features of each point for classification, we propose novel multiview supporting planes to analyze the spatial distribution and structure of its neighboring points for each category. Second, we define the multiscale spatial distribution matrix on a grid representation (e.g., the number of points projected into each cell). Last, the statistical information of points is nonlinearly combined and hierarchically compressed to generate a compact and effective convolutional feature representation for classification. The effectiveness of the proposed method is evaluated via experiments on rock-mass point clouds from different scenes. Compared with existing extraction approaches, experimental results indicate the superiority of the proposed method in terms of the precision and recall. [ABSTRACT FROM AUTHOR]

Endmember extraction (EE) plays a crucial part in the hyperspectral unmixing (HU) process. To obtain satisfactory EE results, the EE can be considered as the multiobjective optimization problem to optimize the volume maximization (VM) and root-mean-square error (RMSE) simultaneously. However, it is often quite challenging to balance the conflict of these objectives. In order to tackle the challenges of multiobjective EE, we present a $({\mu + \lambda })$ multiobjective differential evolution algorithm ($({\mu +\lambda })$ -MODE) based on ranking multiple mutations. In the $({\mu + \lambda })$ -MODE algorithm, ranking multiple mutations are adopted to create the mutant vectors via the scaling factor pool to enhance the population diversity. Moreover, mutant vectors employ the binary crossover operator to generate the trial vectors through a crossover control parameter pool in $({\mu + \lambda })$ -MODE to take advantage of the good information of the population. In addition, $({\mu + \lambda })$ -MODE utilizes the fast nondominated sorting approach to sort the parent and trial vectors, and then selects the elitism offspring as the next population via the $({\mu + \lambda })$ selection strategy. Eventually, experimental comparative results in three real HSIs reveal that our proposed $({\mu + \lambda })$ -MODE is superior to other EE methods. [ABSTRACT FROM AUTHOR]

When adding new tasks/classes in an incremental learning scenario, the previous recognition capabilities trained on the previous training data can be lost. In the real-life application of automatic target recognition (ATR), part of the previous samples may be able to be used. Most incremental learning methods have not considered how to save the previous key samples. In this article, the class boundary exemplar selection-based incremental learning (CBesIL) is proposed to save the previous recognition capabilities in the form of the class boundary exemplars. For exemplar selection, the class boundary selection method based on local geometrical and statistical information is proposed. And when adding new classes continually, a class-boundary-based data reconstruction method is introduced to update the exemplar set. Thus, when adding new classes, the previous class boundaries could be kept complete. Experimental results demonstrate that the proposed CBesIL outperforms the other state of the art on the accuracy of multiclass recognition and class-incremental recognition. [ABSTRACT FROM AUTHOR]

In this article, a statistical methodology to estimate wind speed from CYGNSS observables is proposed and implemented. The approach uses the cumulative distribution function (cdf) of the observable and of the ground-truth reference winds. It depends only on the statistical distributions of the CYGNSS data and the wind speed, and therefore, is simpler to implement than alternative approaches requiring coincident matchups between the data and the ground truth. This cdf matching method produces retrieved winds with a probability density function that is very close to that of the ground-truth winds. When compared to the current CYGNSS baseline winds for fully developed seas, the cdf matching winds show better behavior and agreement with reference wind speeds over the low to medium wind speed range, which constitutes the majority of the wind population that drives the statistics used by the algorithm. The performance is robust with respect to measurement geometry and transmitter and receiver hardware parameters, with the exception of a dependence of the error on the GPS satellite identifier (ID), probably due to uncorrected variations in GPS equivalent isotropically radiated power (EIRP). Validation using modeled winds and winds measured by other satellites reveals that CYGNSS winds behave in a very similar manner as the winds modeled by the Global Data Assimilation System (GDAS). [ABSTRACT FROM AUTHOR]

This article proposes an unsupervised multiregion segmentation method for fully polarimetric synthetic aperture radar (polSAR) images based on the improved fuzzy active contour model. Different from most of the active contour models that are based on the utilization of only statistical information, the proposed method makes better use of information from polarimetric data. In addition to the statistical information, an edge detector modified from the ratio of exponentially weighted averages (ROEWA) operator, a sliding window algorithm for the total received power, and a ratio operator with respect to scattering mechanisms are integrated to the proposed active contour model. We then present a layer-based fuzzy active contour framework to solve our model. The general fuzzy active contour framework is computationally much more efficient compared with the level set-based framework; however, it cannot be applied to the multiregion segmentation of SAR images due to its low robustness to strong noise. The proposed approach includes the advantages of the general fuzzy active contour framework and has good robustness. Using two fully polSAR images demonstrates that the proposed method can achieve higher efficiency and a better segmentation performance in comparison with the commonly used active contour methods. [ABSTRACT FROM AUTHOR]

Sparse unmixing has become a popular tool for hyperspectral imagery interpretation. It refers to finding the optimal subset of a spectral library to reconstruct the image data and further estimate the proportions of different materials. Recently, multi-objective based sparse unmixing methods have presented promising performance because of their advantages in addressing combinatorial problems. A spectral and multi-objective based sparse unmixing (SMoSU) algorithm was proposed in our previous work, which solves the decision-making problem well. However, it does not show outstanding advantages in strong noise cases. To solve the problem, in this paper, SMoSU is improved based on the estimation of distribution algorithms (EDAs). The machine learning based EDAs have been a reliable approach in solving multi-objective problems. However, most of them are for special problems and relatively weak in theoretical foundations. Thus, it is unreliable to extend it directly to sparse unmixing. Here, we improve EDA on the basis of classification and propose a classification-based model for individual generating under the framework of SMoSU (CM-MoSU). In CM-MoSU, the whole population is divided to be positive and negative. Then, the macroinformation of positive individuals is used to guide the generation of new individuals. Therefore, the optimization task could pay more attention to the feasible space with high quality. Moreover, some theoretical analyses are presented to prove the reliability of CM-MoSU. In experiments, several state-of-the-art sparse unmixing algorithms are compared. Both synthetic and real-world experiments demonstrate the effectiveness of CM-MoSU. [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]

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

Location-based social media have been extensively utilized in the concept of “social sensing” to exploit dynamic information about human activities, yet joint uses of social sensing and remote sensing images are underdeveloped at present. In this paper, the close relationship between the number of Twitter users and brightness of nighttime lights (NTL) over the contiguous United States is calculated and geotagged tweets are then used to upsample a stable light image for 2013. An associated outcome of the upsampling process is the solution of two major problems existing in the NTL image, pixel saturation, and blooming effects. Compared with the original stable light image, digital number (DN) values of the upsampled stable light image have larger correlation coefficients with gridded population (0.47 versus 0.09) and DN values of the new generation NTL image product (0.56 versus 0.52), i.e., the Visible Infrared Imaging Radiometer Suite day/night band image composite. In addition, total personal incomes of states are disaggregated to each pixel in proportion to the DN value of the pixel in the NTL images and then aggregate by counties. Personal incomes distributed by the upsampled NTL image are closer to the official demographic data than those distributed by the original stable light image. All of these results explore the potential of geotagged tweets to improve the quality of NTL images for more accurately estimating or mapping socioeconomic factors. [ABSTRACT FROM AUTHOR]

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

The ages of terrains on other planetary bodies are chiefly determined using crater size–frequency distributions. However, primary impacts can generate numerous secondary craters that can affect the crater population. Classifying impact craters as primary or secondary is commonly done via time-consuming manual inspection, which limits the areas that can be analyzed at high resolution. We present a parametric model for characterizing small (100–600 m diameter) impact craters, where the model parameters have implications for describing the physical processes involved in their formation and modification. We infer these parameters from craters in images captured by the high-resolution imaging science experiment (HiRISE) camera onboard the Mars Reconnaissance Orbiter. For each crater within the appropriate size range, our algorithm creates a 3-D surface for a parametrically modeled crater and a 2-D rendering using illumination metadata, including emission, phase, and solar incidence angles at the time when the image was captured. A function describes the likelihood of each set of model parameters in terms of the geometry of craters in a given HiRISE image. These values are then optimized using a Metropolis–Hasting Markov chain Monte Carlo sampler. We evaluated three different prior probability distributions over the parameter space and two different likelihoods: one for digital terrain models and the other for images. We show that after applying t-distributed stochastic neighbor embedding (t-SNE) over the inferred crater parameters, t-SNE is able to project the multidimensional crater parameters into a 2-D space where secondary craters cluster together and are separable from primary craters. [ABSTRACT FROM AUTHOR]

The detection of meteorological targets using ground-based weather radars usually suffers from ground clutter and beam blockage. These nonmeteorological or weakened signals should be identified so quality control should be implemented before weather radar data can be used. Conventional quality control methods aim at differentiating between echo structures of ground clutter and meteorological targets, and use terrain information to calculate beam blockage regions based upon standard atmospheric refraction. However, it is difficult to achieve the goal for long-term large data sets by conventional methods due to the complexity and diversity of weather radar echoes. In this paper, regions of ground clutter and beam blockage are first identified through the statistics on spatial distribution of reflectivity and fuzzy logic classification, and then they are used as masks to remove data from the scan. The new method is applied to data of the Nanjing weather radar in China. By the aid of a proposed evaluation scheme and the visual recognition, quality control results of the new method are compared with those of the conventional methods. It is found that the new method can provide better identification of ground clutter or beam blockage and thus better quality control results. The new scheme has a good prospect in operational service for its principle advantages, easy applicable conditions, and better performance compared with conventional methods. [ABSTRACT FROM AUTHOR]

A new intercalibration method for two polarorbiting satellite instruments or two instrument constellations' Fundamental Climate Data Records (FCDRs) is presented. It is based on statistical fitting of reflectance data from the two instruments covering the same area during the same period, but not simultaneously. A Deming regression with iterative weights is used. The accuracy of the intercalibration method itself was better than 0.5% for the Moderate Resolution Imaging Spectroradiometer (MODIS) versus MODIS and Advanced Very High Resolution Radiometer (AVHRR) versus AVHRR test data sets. The intercalibration of an AVHRR FCDR generated by NOAA versus a combined MODIS Terra and Aqua data set of red and near-infrared (NIR) channels was carried out and showed a difference in the reflectance values of about 2% (red) and 6% (NIR). The presented intercalibration method can be used for checking the calibration of two instruments or FCDRs in all viewing angles used separately. [ABSTRACT FROM AUTHOR]

Urban spatial structure affects many aspects of urban functions and has implications for accessibility, environmental sustainability, and public expenditures. During the urbanization process, a careful and efficient examination of the urban spatial structure is crucial. Different from the traditional approach that relies on population or employment census data, this research exploits the nighttime light (NTL) intensity of the earth surface recorded by satellite sensors. The NTL intensity is represented as a continuous mathematical surface of human activities, and the elemental features of urban structures are identified by analogy with earth’s topography. We use a topographical metaphor of a mount to identify an urban center or subcenter and the surface slope to indicate an urban land-use intensity gradient. An urban center can be defined as a continuous area with higher concentration or density of employments and human activities. We successfully identified 33 urban centers, delimited their corresponding boundaries, and determined their spatial relations for Shanghai metropolitan area, by developing a localized contour tree method. In addition, several useful properties of the urban centers have been derived, such as 9% of Shanghai administrative area has become urban centers. We believe that this method is applicable to other metropolitan regions at different spatial scales. [ABSTRACT FROM PUBLISHER]

This paper develops a probabilistic model to predict the tidal current for modeling the prediction uncertainty, and thereby the forecast error. This requires the extension of the deterministic models from a point-by-point forecast to the probabilistic models with prediction intervals (PIs). The proposed model uses PIs to construct the bandwidth, which models the uncertainty of tidal current prediction properly. It uses the lower upper bound estimation method to train the neural network (NN) without making any assumption about the distribution of the forecast error. In order to adjust the weighting and biasing factors of NN, firefly algorithm with a new two-phase modification method is developed to search the problem space globally. Two benchmarks are used to show the search ability of the algorithm. The high accuracy of the proposed model is examined on the practical tidal data collected from the Bay of Fundy, NS, Canada. [ABSTRACT FROM AUTHOR]

This paper introduces a method to reduce false alarms in wavelength-resolution synthetic aperture radar (SAR) change detection and aims at very high frequency-band systems like the Coherent All Radio Band System (CARABAS). The false alarms are usually caused by the elongated structures, such as power lines and fences, which stand out from the background. The responses of elongated structures are sensitive to flight path. The introduced method aims at minimizing the false alarms caused by the elongated structures and is based on the well-known adaptive processing mechanism, i.e., the so-called adaptive noise canceler (ANC) where a separate reference signal is required. The changes between measurements are considered by the input signal of ANC while the separate reference signal comes from the measurements without change. Hence, the method requires three SAR images associated with three measurements, with no changes between two of them. The reference data for the study are provided by CARABAS. The experimental results indicate that the method can reduce false alarms significantly and provide high probability of detection (≥98%). The experimental results also show that the method still works well even in the case where the flight tracks of the SAR system in the change detection measurements are slightly different. [ABSTRACT FROM PUBLISHER]

Displacements of the Earth's surface can be estimated using differential interferometric synthetic aperture radar. The estimates are derived from the phase difference between two radar acquisitions. When at least three such acquisitions are available, one can compute the displacement between the first and the third acquisition and compare it with the sum of the two intermediate displacements. These two are expected to be equal for a piston-like spatially uniform deformation. However, this is not necessarily the case in measured data. Such lack of phase closure can be due to decorrelation noise alone. It has also been attributed to complex scattering processes such as soil moisture changes or multiple scattering sources. However, the nature of these nonrandom effects is only poorly understood in cold regions, as the role of snow and freeze/thaw processes has not been studied to date. To distinguish the noise-like and the systematic effects, an asymptotic Wald significance test is proposed. It detects situations when the observed closure error cannot solely be explained by noise. Such situations with $p < 0.05$ are observed at the Ku-band during snow metamorphism and melt and following a summer precipitation event in Sodankylä, Finland. They can also be prevalent (> 25%) in the X-band observations of ice-rich permafrost regions in the Lena Delta, Russia, indicating the presence of processes that can have systematic and deleterious impacts on the estimation of surface movements. Satellite-based monitoring of these displacements is thus possibly subject to complex error sources in high-latitude regions. [ABSTRACT FROM AUTHOR]

Seismic inversion problems often involve nonlinear relationships between data and model and usually have many local minima. Linearized inversion methods have been widely used to solve such problems. However, these kinds of methods often strongly depend on the initial model and are easily trapped in a local minimum. Global optimization methods, on the other hand, do not require a very good initial model and can approach a global minimum. However, global optimization methods are exhaustive search techniques that can be very time consuming. When the model dimension or the search space becomes large, these methods can be very slow to converge. In this paper, we propose a new global optimization algorithm by incorporating a new multimutation scheme into a differential evolution algorithm. Because mutation operation with the new multimutation scheme can generate better mutant vectors, the new global optimization algorithm has a very good ability of exploring the search space and can converge very fast. We apply the proposed algorithm to both synthetic and field data to test its performance. The results have clearly indicated that the new global optimization algorithm provides faster convergence and yields better results compared with the conventional global optimization methods in seismic inversion. [ABSTRACT FROM AUTHOR]

This paper introduces the use of single-layer and deep convolutional networks for remote sensing data analysis. Direct application to multi- and hyperspectral imagery of supervised (shallow or deep) convolutional networks is very challenging given the high input data dimensionality and the relatively small amount of available labeled data. Therefore, we propose the use of greedy layerwise unsupervised pretraining coupled with a highly efficient algorithm for unsupervised learning of sparse features. The algorithm is rooted on sparse representations and enforces both population and lifetime sparsity of the extracted features, simultaneously. We successfully illustrate the expressive power of the extracted representations in several scenarios: classification of aerial scenes, as well as land-use classification in very high resolution or land-cover classification from multi- and hyperspectral images. The proposed algorithm clearly outperforms standard principal component analysis (PCA) and its kernel counterpart (kPCA), as well as current state-of-the-art algorithms of aerial classification, while being extremely computationally efficient at learning representations of data. Results show that single-layer convolutional networks can extract powerful discriminative features only when the receptive field accounts for neighboring pixels and are preferred when the classification requires high resolution and detailed results. However, deep architectures significantly outperform single-layer variants, capturing increasing levels of abstraction and complexity throughout the feature hierarchy. [ABSTRACT FROM PUBLISHER]

Target detection is an important application in the hyperspectral image processing field, and several detection algorithms have been proposed in the past decades. Some traditional detectors are built based on the statistical information of the target and background spectra, and their performances tend to be affected by the spectral quality. Some previous methods cope with this problem by refining the target spectra to make the detector robust. In this paper, instead of doing similar to this, we propose a new hierarchical method to suppress the backgrounds while preserving the target spectra, with the purpose of boosting the performance of traditional hyperspectral target detector. The proposed method consists of different layers of classical constrained energy minimization (CEM) detectors. In each layer of detection, the CEM's output of each spectrum is transformed by a nonlinear suppression function and then considered as a coefficient to impose on this spectrum for the next round of iteration. To our knowledge, such hierarchical structure is proposed for the first time. Theoretically, we prove the convergence of the proposed algorithm, and we also give a theoretical explanation on why we can obtain the gradually increasing detection performance through the hierarchical suppression process. Experimental results on two real hyperspectral images and one synthetic image suggest that our method significantly improves the performance of the original CEM detection algorithm and also outperforms other classical and recently proposed hyperspectral target detection algorithms. [ABSTRACT FROM AUTHOR]

Coherent change detection using paired synthetic aperture radar (SAR) images is often performed using a classical coherence estimator that is invariant to the true variances of the populations underlying each paired sample. While attractive, this estimator is biased and requires a significant number of samples to yield good performance. Increasing sample size often results in decreased image resolution. Thus, we propose the use of Berger's coherence estimate because, with the same number of pixels, the estimator effectively doubles the sample support without sacrificing resolution when the underlying population variances are equal or near equal. A potential drawback of this approach is that it is not invariant since its distribution depends on the pixel pair population variances. While Berger's estimator is inherently sensitive to the inequality of population variances, we propose a method of insulating the detector from this acuity. A two-stage change statistic is introduced to combine a noncoherent intensity change statistic given by the sample variance ratio, followed by the alternative Berger estimator, which assumes equal population variances. The first-stage detector identifies pixel pairs that have nonequal variances as changes caused by the displacement of sizeable object. The pixel pairs that are identified to have equal or near-equal variances in the first stage are used as an input to the second stage. The second-stage test uses the alternative Berger coherence estimator to detect subtle changes such as tire tracks and footprints. We show experimentally that the proposed method yields higher contrast SAR change detection images than the classical coherent change detector (state of the art), the alternative coherent change detector, and the intensity change detector. Experimental results are presented to show the effectiveness and robustness of the proposed algorithm for SAR change detection. [ABSTRACT FROM PUBLISHER]

Finding solutions for the mitigation of atmospheric phase delay patterns from differential synthetic aperture radar interferometry (d-InSAR) observations is currently one of the most active research topics in radar remote sensing. Recently, many studies have analyzed the performance of regional numerical weather prediction (NWP) models for this task; however, despite the significant efforts made to optimize model parameterizations, most of these studies have concluded that current regional NWPs are not able to robustly reproduce the atmospheric phase delay structures that affect SAR interferograms. Despite these previous findings, we have revisited the application of NWPs for atmospheric correction using a different analysis strategy. In contrast to earlier studies, which assessed the quality of NWP-derived phase screen data, we have studied NWPs from a statistical angle by analyzing whether they are able to provide realistic information about the statistical properties of atmospheric phase signals in d-InSAR data. We have determined that NWP forecasts can provide relevant statistical information about the atmospheric phase screen captured in d-InSAR data. Based on this, this study presents a new atmospheric phase filtering approach that is using statistical atmospheric information as a prior in order to optimize the choice of unknown filter parameters. The mathematical concept of the prior-driven filtering approach is outlined, and its implementation is explained. We have determined the performance of this new filter concept and have shown that it comes very close to a filter optimum. [ABSTRACT FROM PUBLISHER]

The Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) nighttime lights imagery has proven to be a powerful remote sensing tool to monitor urbanization and assess socioeconomic activities at large scales. However, the existence of incompatible digital number (DN) values and geometric errors severely limit application of nighttime light image data on multiyear quantitative research. In this paper, we extend and improve previous studies on intercalibrating nighttime lights image data to obtain more compatible and reliable nighttime lights time-series (NLT) image data for China and the U.S. through four steps, namely, intercalibration, geometric correction, steady-increase adjustment, and population data correction. We then use gross domestic product (GDP) data to test the processed NLT image data indirectly and find that sum light (summed DN value of pixels in a nighttime light image) maintains apparent increase trends with relatively large GDP growth rates but does not increase or decrease with relatively small GDP growth rates. As nighttime light is a sensitive indicator for economic activity, the temporally consistent trends between sum light and GDP growth rate imply that brightness of nighttime lights on the ground is correctly represented by the processed NLT image data. Finally, through analyzing the corrected NLT image data from 1992 to 2008, we find that China experienced apparent nighttime lights development in 1992-1997 and 2001-2008, respectively, and the U.S. showed nighttime lights decay in large areas after 2001. [ABSTRACT FROM PUBLISHER]

A time or spatial series of drop counts is but one realization of a multiple stochastic process. In this paper, a method is presented that extracts more of the information contained in the time series of 1-min Joss-Waldvogel disdrometer counts in rain than a simple analysis of the magnitudes of the counts would provide. This is done by greatly increasing the size of a data set using a Bayesian analysis of drop count measurements in 17 size bins. Using the empirical copula statistical technique of probability density function transformations, a 1391-min time series of drop counts was expanded to the equivalent of 40 000 min. This dramatic increase in sample size permits a deeper characterization of the rain. Using this single disdrometer, it also allows one to translate these counts into a 200 × 200 grid filled at each point with drop size distributions of mean drop concentrations consistent with the observed statistical properties of the rain. Such a field can be used for remote sensing studies of the effect of partial beam filling and for algorithm development. Moreover, since there is nothing unique to this set of drop counts, this approach can be applied to any other set of count data, including snow and clouds. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel covariation approach for seismic wavelet estimation under the assumption that a real seismic signal follows non-Gaussian α-stable distributions. Since the non-Gaussian α-stable signals do not have finite second or higher order moments, the traditional methods of Gaussian distribution may not get a suitable solution. Based on the principle of fractional lower order statistics, the covariation approach deconvolution objective function matrix was given, and the details of wavelet estimation with the covariation approach were presented. Furthermore, computer simulation experiments on theoretical synthetic data and real seismic data were conducted. In the experiments, the effect of moments was considered. Among the estimated wavelets with different moments, the best wavelet should be the one with moment less than characteristic but close to characteristic. To verify the correctness and effectiveness of the proposed method, the extracted real wavelet was applied in real seismic acoustic impedance inversion. The result from the inversion of the 2-D real data set is consistent with the well log interpretation very well. [ABSTRACT FROM PUBLISHER]

Image-subspace-based approach for speckle noise removal from synthetic aperture radar (SAR) images is proposed. The underlying principle is to apply homomorphic framework in order to convert multiplicative speckle noise into additive and then to decompose the vector space of the noisy image into signal and noise subspaces. Enhancement is performed by nulling the noise subspace and estimating the clean image from the remaining signal subspace. Linear estimator minimizing image distortion while maintaining the residual noise energy below some given threshold is used to estimate the clean image. Experiments are carried out using synthetically generated data set with controlled statistics and real SAR image of Selangor area in Malaysia. The performance of the proposed technique is compared with Lee and homomorphic wavelet in terms of noise variance reduction and preservation of radiometric edges. The results indicate moderate noise reduction by the proposed filter in comparison to Lee but with a significantly less blurry effect and a comparable performance in terms of noise reduction to wavelet but with less artifacts. The results also show better preservation of edges, texture, and point targets by the proposed filter than both Lee and wavelet and less required computational time. [ABSTRACT FROM PUBLISHER]

Multisatellite measurements of altimeter-derived sea surface height (SSH) and sea surface temperature (SST) provide a wealth of information about ocean circulation, particularly mesoscale ocean dynamics which may involve strong spatiotemporal relationships between SSH and SST fields. Within an observation-driven framework, we investigate the extent to which mesoscale ocean dynamics may be decomposed into a mixture of dynamical modes, characterized by different local regressions between SSH and SST fields. Formally, we develop a novel latent class regression model to identify dynamical modes from joint SSH and SST observation series. Applied to the highly dynamical Agulhas region, we demonstrate and discuss the geophysical relevance of the proposed mixture model to achieve a spatiotemporal segmentation of the upper ocean dynamics. [ABSTRACT FROM PUBLISHER]

An indicator kriging (IK) approach for remote sensing image classification is proposed. By introducing indicator variables for categorical data, the work of image classification is transformed into estimation of class-dependent probabilities in feature space using ordinary kriging. Individual pixels are then assigned to the class with maximum class probability. The approach is distribution free and yields perfect classification accuracies for training data provided that collocated data in feature space do not exist. Technical considerations regarding implementation of IK such as indicator semivariogram modeling and handling of collocated data in feature space are also described. The IK, Gaussian-based maximum likelihood, nearest neighbor, and support vector machine (SVM) classifiers were applied to study areas within the Shimen reservoir watershed (case A: FORMOSAT-2) and Taipei city (case B: SPOT 4). The results show that the overall accuracies of the proposed IK classifier and SVM can achieve higher than 97% for training data and 81% for testing data. (The overall accuracies of IK are a little higher than those of SVM.) IK and SVM are found to be superior to the other two classifiers in terms of overall accuracies for both training and testing data. The proposed IK classifier has the following advantages: 1) It can deal with anisotropic problem in feature space; 2) it is a nonparametric method and needs not to know the type of probability distribution; and 3) it yields 100% classification accuracy for the training data provided that collocated data in feature space do not exist. [ABSTRACT FROM PUBLISHER]

The polarimetric information contained in polarimetric synthetic aperture radar (SAR) images represents great potential for characterization of natural and urban surfaces. However, it is still challenging to identify different land cover classes with polarimetric data. Most of the classification algorithms presented earlier have used a fixed value of polarimetric indexes for segregation of a particular land cover type from other classes. However, the value of these polarimetric indexes may change accordingly with change in observation site, temporal acquisition, environmental conditions, and calibration differences among various systems. Thus, the value of polarimetric indexes for segregation of each land cover type has to be tuned in order to cope with these changes. Therefore, in this paper, a decision-tree-based adaptive land cover classification technique has been proposed for labeling of different clusters to their own classes. The proposed method uses spatial-statistics-based expressions (i.e., median “ M” and standard deviation “ S”) of best-selected polarimetric indexes on the basis of a separability index criterion for creating the decision boundary among various classes. In order to make the system adaptive in nature, unknown terms have been included in the expressions. Due to the dependence of a developed nonlinear relationship of overall classification accuracy (OA) on large number of unknowns, a genetic algorithm (GA) approach has been used, which provides optimum values of considered polarimetric indexes for automatic segregation of different classes. The proposed algorithm is successfully tested and validated on ALOS PALSAR quad-pol data. [ABSTRACT FROM AUTHOR]

The scaled complex Wishart distribution is a widely used model for multilook full polarimetric synthetic aperture radar data whose adequacy is attested in this paper. Classification, segmentation, and image analysis techniques that depend on this model are devised, and many of them employ some type of dissimilarity measure. In this paper, we derive analytic expressions for four stochastic distances between relaxed scaled complex Wishart distributions in their most general form and in important particular cases. Using these distances, inequalities are obtained that lead to new ways of deriving the Bartlett and revised Wishart distances. The expressiveness of the four analytic distances is assessed with respect to the variation of parameters. Such distances are then used for deriving new tests statistics, which are proved to have asymptotic chi-square distribution. Adopting the test size as a comparison criterion, a sensitivity study is performed by means of Monte Carlo experiments suggesting that the Bhattacharyya statistic outperforms all the others. The power of the tests is also assessed. Applications to actual data illustrate the discrimination and homogeneity identification capabilities of these distances. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) tomography is a 3-D imaging modality that is commonly tackled by spectral estimation techniques. Thus, the backscattered power along the cross-range direction can be readily obtained by computing the Fourier spectrum of a stack of multibaseline measurements. In addition, recent work has addressed the tomographic inversion under the framework of compressed sensing, thereby recovering sparse cross-range profiles from a reduced set of measurements. This paper differs from previous publications, in that it focuses on sparse expansions in the wavelet domain while working with the second-order statistics of the corresponding multibaseline measurements. In this regard, we elaborate on the conditions under which this perspective is applicable to forested areas and discuss the possibility of optimizing the acquisition geometry. Finally, we compare this approach with traditional nonparametric ones and validate it by using fully polarimetric L-band data acquired by the Experimental SAR (E-SAR) sensor of the German Aerospace Center (DLR). [ABSTRACT FROM AUTHOR]

This paper focuses on the tracking of mesoscale convective systems (MCS) from geostationary satellite infrared data in the tropical regions. In the past, several automatic tracking algorithms have been elaborated to tackle this problem. However, these techniques suffer from limitations in describing convection at the “true” scale and in depicting coherent MCS life cycles (split and merge artifacts). To overcome these issues, a new algorithm called Tracking Of Organized Convection Algorithm through a 3-D segmentatioN has been developed and is presented in this paper. This method operates in a time sequence of infrared images to identify and track MCS and is based on an iterative process of 3-D segmentation of the volume of infrared images. The objective of the new tracking algorithm is to associate the convective core of an MCS to its anvil cloud in the spatiotemporal domain. The technique is applied on various case studies over West Africa, Bay of Bengal, and South America. The efficiency of the new algorithm is established from an analysis of the case studies and via a statistical analysis showing that the cold cloud shield defined by a 235-K threshold in the spatiotemporal domain is decomposed into realistic MCSs. In comparison with an overlap-based tracking algorithm, the analysis reveals that MCSs are detected earlier in life cycle and later in their dissipation stages. Moreover, MCSs identified are not anymore affected by split and merge events along their life cycles, allowing a better characterization of their morphological parameters along their life cycles. [ABSTRACT FROM AUTHOR]

The intent of this paper is to propose new methods for the reconstruction of areas obscured by clouds. They are based on compressive sensing (CS) theory, which allows finding sparse signal representations in underdetermined linear equation systems. In particular, two common CS solutions are adopted for our reconstruction problem: the basis pursuit and the orthogonal matching pursuit methods. A novel alternative CS solution is also proposed through a formulation within a multiobjective genetic optimization scheme. To illustrate the performances of the proposed methods, a thorough experimental analysis on FORMOsa SATellite-2 and Satellite Pour l'Observation de la Terre-5 multispectral images is reported and discussed. It includes a detailed simulation study that aims at assessing the accuracy of the methods in different qualitative and quantitative cloud-contamination conditions. Compared with state-of-the-art techniques for cloud removal, the proposed methods show a clear superiority, which makes them a promising tool in cleaning images in the presence of clouds. [ABSTRACT FROM AUTHOR]

Morphological and attribute profiles have been proven to be effective tools to fuse spectral and spatial information for classification of remote sensing data. A wide range of filters (i.e., number of levels in the profiles) is usually necessary in order to properly model the spatial information in a remote sensing scene. A dense sampling of the values of the parameters of the filters generates profiles that have both a very large dimensionality (leading to the Hughes phenomenon in classification) and a high redundancy. In this paper, a novel iterative technique based on genetic algorithms (GAs) is proposed to automatically optimize the selection of the optimal features from the profiles. The selection of the filtered images that compose the profile is performed by dividing them into three classes corresponding to high, medium, and low importance. We propose to measure the importance (modeled in terms of discriminative power in the classification task) using a random forest classifier, which provides a rank for each feature with its model. Only the set of images associated with the highest importance is selected, i.e., preserved for classification. The proposed technique is applied to the features labeled with medium importance, whereas the images with the lowest importance are removed from the profile. This method is employed to classify three hyperspectral data sets achieving significantly high classification accuracy values. A parallel computing implementation has been developed in order to significantly reduce the time required for the run of the GAs. [ABSTRACT FROM AUTHOR]

This paper generalizes the well-known displaced- phase-center antenna (DPCA) method for efficient ground moving target indication (GMTI) with a two-channel synthetic aperture radar (SAR) to any multichannel SAR/GMTI radars independent of the number of receive channels. This processing method called extended DPCA (EDPCA) is derived in this paper and is applied to data acquired with the Canadian RADARSAT-2 satellite. The expected GMTI performance of RADARSAT-2 after EDPCA processing is compared to results achieved with measured RADARSAT-2 data recorded during several trials in order to validate the developed theory. [ABSTRACT FROM PUBLISHER]

At present, several papers discuss the accuracy and precision of terrestrial laser scanners (TLSs), but the research continues to focus on the behavior of the TLSs. The purpose of this paper is to propose a method to evaluate the uncertainty of a TLS (FARO Photon 80). A rigid and transportable aluminum structure with 28 black-and-white targets was designed for this purpose. The structure was scanned 12 times at several distances from 2 to 70 m, and the x, y, and z coordinates of the center of the targets were automatically identified. Data were analyzed by means of circular and spherical statistics using R modules programmed in our research group. Analysis reveals that 3-D spatial distribution has a stratified pattern in the Z-axis. Regardless of the scanner status, these results indicate that these analyses should be performed periodically because they can have an impact on some studies. The proposed methodology is robust and simple and can be performed with free software such as the R modules used in this work. [ABSTRACT FROM AUTHOR]

A novel method for moving-target tracking using single-channel synthetic aperture radar (SAR) with a large antenna beamwidth is introduced and evaluated using a field experiment and real SAR data. The presented approach is based on subaperture SAR processing, image statistics, and multitarget unscented Kalman filtering. The method is capable of robustly detecting and tracking moving objects over time, providing information not only about the existence of moving targets but also about their trajectories in the image space while illuminated by the radar beam. We have successfully applied the method on an experimental data set using miniature SAR to accurately characterize the movement of vehicles on a highway section in the radar image space. [ABSTRACT FROM AUTHOR]

Radar sounders operating on satellite platforms (e.g., radar sounding missions at Mars) provide a huge amount of data that currently are mostly analyzed by means of manual investigations. This calls for the development of novel techniques for the automatic extraction of information from sounder signals that could greatly support the scientific community. Such a topic has not been addressed sufficiently in the literature. This paper provides a contribution to fill this gap by presenting both 1) a study of the theoretical statistical properties of radar sounder signals, and 2) two novel techniques for the automatic analysis of sounder radargrams. The main goal of the study is the identification of statistical distributions that can accurately model the amplitude fluctuations of different subsurface targets. This is fundamental for the understanding of signal properties and for the definition of automatic data analysis techniques. The results of such a study drive the development of two novel techniques for 1) the generation of subsurface feature maps, and 2) the automatic detection of the deepest scattering areas visible in the radargrams. The former produces for each radargram a map showing which areas have high probability to contain relevant subsurface features. The latter exploits a region-growing approach properly defined for the analysis of radargrams to identify and compose the basal scattering areas. Experimental results obtained on Shallow Radar data acquired on Mars confirm the effectiveness of the proposed techniques. [ABSTRACT FROM AUTHOR]

A combined analysis of ocean surface wind vector measurements by the European Advanced Scatterometer (ASCAT) and the National Aeronautics and Space Administration QuikSCAT (QS) scatterometer using buoy measurements, numerical weather prediction model analyses, and spectral decomposition reveals significant statistical differences between the two data sets. While QS wind speeds agree better with buoy wind speeds than ASCAT above 15 m s-1, ASCAT wind directions agree better with buoy directions overall than QS. In contrast, it is shown that sea-level pressure (SLP) fields derived from ASCAT and QS measurements compare better with each other than the winds in a statistical sense, even though ASCAT bulk pressure gradients (BPGs) are slightly weaker than buoy pressure gradients and have slightly lower spectral energy than QS. Weaker BPGs in ASCAT are consistent with the low bias in ASCAT wind speeds. Thus, it is proposed that scatterometer-derived SLP fields can be used as a filter to improve the wind directions. This improves the QS wind directions but has less effect on the more accurate ASCAT wind directions. The unfiltered ASCAT wind vector statistics compare well with the statistics of the direction-filtered QS winds. It is suggested that this methodology might provide a basis for minimizing the discrepancies between various satellite wind measurement data sets in view of producing a long-term record of satellite-derived SLP fields and ocean surface wind vectors. [ABSTRACT FROM AUTHOR]