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The Soil Moisture Active Passive (SMAP) satellite mission launched in January 2015 provides worldwide soil moisture (SM) monitoring based on L-band brightness temperature ($T_{B}^{\mathrm{ p}}$) measurements at horizontal ($\text{T}_{B}^{\mathrm{ H}}$) and vertical ($T_{B}^{\mathrm{ V}}$) polarizations. This paper presents a performance assessment of SMAP soil emission model and SM retrieval algorithms for a Tibetan desert ecosystem. It is found that the SMAP emission model largely underestimates the SMAP measured $T_{B}^{\mathrm{ H}}$ (≈ 15 K), and the $T_{B}^{\mathrm{ V}}$ is underestimated during dry-down episodes. A cold bias is noted for the SMAP effective temperature due to underestimation of soil temperature, leading to the $T_{B}^{\mathrm{ p}}$ underestimation (>5 K). The remaining $T_{B}^{\mathrm{ H}}$ underestimation is found to be related to the surface roughness parameterization that underestimates its effect on modulating the $T_{B}^{\mathrm{ p}}$ measurements. Further, the topography and uncertainty of soil information are found to have minor impacts on the $T_{B}^{\mathrm{ p}}$ simulations. The SMAP baseline SM products produced by single-channel algorithm (SCA) using the $T_{B}^{\mathrm{ V}}$ measurements capture the measured SM dynamics well, while an underestimation is noted for the dry-down periods because of $T_{B}^{\mathrm{ V}}$ underestimation. The products based on the SCA with $T_{B}^{\mathrm{ H}}$ measurements underestimate the SM due to underestimation of $T_{B}^{\mathrm{ H}}$ , and the dual-channel algorithm overestimates the SM. After implementing a new surface roughness parameterization and improving the soil temperature and texture information, the deficiencies noted above in $T_{B}^{\mathrm{ p}}$ simulation and SM retrieval are greatly resolved. This indicates that the SMAP SM retrievals can be enhanced by improving both surface roughness and adopted soil temperature and texture information for Tibetan desert ecosystem. [ABSTRACT FROM AUTHOR]

Many hyperspectral anomaly detectors are designed based on the traditional Mahalanobis distance-based RX algorithm, which is usually considered as an inverse operation of the principal components analysis. Such detectors include the uniform target detector (UTD) algorithm, RX-UTD algorithm, and so on. However, the possibility of background statistical contamination caused by anomalies still exists. In order to alleviate this problem, in this paper, we propose a spectral matched filter (background-anomaly component projection and separation optimized filter) to minimize the average output energy of separate image components and the output values of the weighted background regular term for hyperspectral image anomaly detection, which could strengthen the separation between anomalies and backgrounds. By calculating the optimal solution to the background-anomaly component projection and separation function, we obtain the optimal projection, where we can effectively suppress the background while highlighting the anomalies. Proposed algorithm has the following research advantages: 1) it creates a novel collaborative component projection and robust background optimization function to separate the background and anomalies and 2) it analyzes the intrinsic statistical distribution of pixels and applies appropriate iterative shrinkage-thresholding algorithm to solve the $\ell _{1}$ -min problem. Experiments were conducted on three real hyperspectral data sets. The detection results demonstrate that the proposed algorithm is superior to other state-of-the-art anomaly detection algorithms. [ABSTRACT FROM AUTHOR]

The spatial modulation coincidence imaging (SMCI), as a novel kind of microwave coincidence imaging method, is proposed in this paper. The SMCI system provides a new way to produce the time-space independent signal instead of multitransmitter architecture with wideband randomly modulated signal in radar coincidence imaging. Due to some special features, metamaterial plate is utilized as the reflective surface to modulate the incident signal to construct random radiation field. The resolution of SMCI system is analyzed under large viewing angle with two different transmitting signals. Reflective surface is nonuniformly divided to derive the expression of resolution. The analysis results show that the resolution of SMCI system is mainly determined by the size of reflective surface and center frequency, which is similar to the traditional aperture. The SMCI system is low cost and flexible in design. Simultaneously, it can avoid the synchronization problem between subsources. Moreover, the SMCI system can achieve the resolution of space target through single transmitter–single receiver radar system. High-resolution image can be reconstructed since the tests are nonlinear. Finally, a series of simulation experiments is presented based on the nondirect-viewing scene we proposed. Using the algorithm based on a compressed sensing theory, we reconstructed the target image with high resolution. [ABSTRACT FROM AUTHOR]

Conventional classification algorithms have shown great success for balanced classes. In remote sensing applications, it is often the case that classes are imbalanced. This paper proposes a novel solution to solve the problem of imbalanced training samples in hyperspectral image classification. It consists of two parts: one is for large-size sample sets and the other is for small-size sets. Specifically, an algorithm based on the orthogonal complement subspace projection (OCSP) is proposed to select samples from large-size classes, and an algorithm also based on OCSP is proposed to create artificial samples for small-size ones. The impact on representation-based classifiers, i.e., sparse and collaborative representation classifiers and traditional classifiers (e.g., support vector machine), is investigated. Experimental results demonstrate that the proposed solution can outperform other existing solutions in the literature. [ABSTRACT FROM AUTHOR]

This paper describes the adaptation of a Bayesian sea ice detection algorithm for the scatterometer on-board the European Remote Sensing (ERS) satellites (ERS-1 and ERS-2). The algorithm is based on statistics of distances to ocean wind and sea ice geophysical model functions (GMFs) and its performance is validated against coincident active and passive microwave data. We furthermore propose a new model for sea ice backscatter at the C-band in vertical polarization based on the sea ice GMFs derived from ERS and advanced scatterometer data. The model characterizes the dependence of sea ice backscatter on the incidence angle and the sea ice type, allowing a more precise incidence angle correction than afforded by the usual linear transformation. The resulting agreement between the ERS, QuikSCAT, and special sensor microwave imager sea ice extents during the year 2000 is high during the fall and winter seasons, with an estimated ice edge accuracy of about 20 km, but shows persistent biases between scatterometer and radiometer extents during the melting period, with scatterometers being more sensitive to summer (lower concentration and rotten) sea ice types. [ABSTRACT FROM AUTHOR]

A two-stage sparse structure representation algorithm which can preserve the manifold structure of the data is proposed for synthetic aperture radar target configuration recognition in this paper. Manifold structure of the data is preserved by two stages. In the training stage, taking advantage of both the sparse representation (SR) and manifold learning, local structure of the data is preserved in the reconstruction space, where SR-based recognition is realized. In the testing stage, two structure preserving factors based on the testing samples are embedded into the SR model to enhance structure preserving performance. The first one is constructed to preserve the local structure of the testing samples, which can guarantee the samples that are close to each other in the original space will also be close to each other in the sparse space. And the second one is established to preserve the distant structure of the testing samples, which can ensure the samples that are far from each other in the original space will also be far from each other in the sparse space. Manifold structure of the data is well captured and preserved by two stages. Experimental results on the moving and stationary target acquisition and recognition database demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a new damage-mapping algorithm based on coherence images estimated from multitemporal polarimetric-interferometric synthetic aperture radar (SAR) data. The interferometric coherence has been restricted in the conventional damage-mapping approaches because the decorrelation sources are too complicated to interpret accurately and temporal decorrelation effects caused by slowly occurring natural changes and disaster events are often coupled together. To overcome these limitations, we formulate a coherence model that accounts for temporal decorrelation in two simplified layers, ground and volume layers, for long-temporal repeat-pass scenarios with zero spatial baseline. The model parameters include: 1) ground-to-volume ratio, a factor to determine the relative scattering contribution of ground and volume layers; 2) temporally correlated change, which captures the exponentially decaying behavior of coherence with time; and 3) temporally uncorrelated change, which is associated with random temporal changes. We estimate the model parameters in three steps: coherence optimization, interferometric pair-invariant parameter estimation, and interferometric pair-variant parameter estimation. To isolate the effects of disaster events from background natural changes, we calculate the probability density functions of historical change pixel by pixel and produce a probability map of damage. We tested the algorithm with uninhabited aerial vehicle data acquired from 2009 to 2015 for mapping the area damaged by the 2015 Lake Fire in California. Based on performance evaluation using receiver operating characteristic curves for optimized coherences and averaged probability maps, the proposed method reduced the false alarm from 0.25 to 0.07 when the probability of detection was 0.85 compared to coherence products alone. [ABSTRACT FROM AUTHOR]

This paper presents multiple kernel learning (MKL) in the context of remote sensing (RS) image classification problems by illustrating main characteristics of different MKL algorithms and analyzing their properties in RS domain. A categorization of different MKL algorithms is initially introduced, and some promising MKL algorithms for each category are presented. In particular, MKL algorithms presented only in machine learning are introduced in RS. Then, the investigated MKL algorithms are theoretically compared in terms of their: 1) computational complexities; 2) accuracy with different qualities of kernels; and 3) accuracy with different numbers of kernels. After the theoretical comparison, experimental analyses are carried out to compare differentMKL algorithms in terms of: 1) model selection and 2) feature fusion problems. On the basis of the theoretical and experimental analyses of MKL algorithms, some guidelines for a proper selection of the MKL algorithms are derived. [ABSTRACT FROM AUTHOR]

The newest soil moisture-dedicated satellite, the Soil Moisture Active Passive (SMAP) mission, provides global maps of soil moisture using concurrent L-band radar and radiometer acquisitions. To support the ongoing validation activities of SMAP soil moisture products, in this paper, we examined the retrieval accuracy of four SMAP soil moisture products by using well-calibrated and dense in situ measurements from the Little Washita Watershed network, one of the SMAP core validation sites with intensive ground sampling. The four SMAP products include the active (3 km), passive (36 km), active-passive (9 km), and the enhanced passive product which is a newly released soil moisture data set with a grid resolution of 9 km. Efforts on identifying the possible error sources of these products were also made for the purpose of improving the SMAP soil moisture algorithms. The results show that the passive and active-passive products can well capture the temporal dynamic of ground soil moisture with overall unbiased root-mean-square error (ubRMSE) values of 0.032 and 0.041 m3· m-3, respectively, which generally meet their mission requirement of 0.04 m3· m-3. In contrast, some irregular fluctuations exist in the active product, leading to an overall wet bias, which makes its accuracy a little poorer than its expected retrieval accuracy of 0.06 m3· m-3. The new enhanced passive product shows the lowest ubRMSE value of 0.026 m3 · m-3 though it underestimates in situ measurements with a bias of 0.059 m3 · m-3, revealing its great potential to substitute the active-passive product to provide global soil moisture measurements at a medium resolution of 9 km. The underestimation of SMAP surface temperature data may be one of the reasons that contribute to the dry bias of SMAP passive, active-passive, and enhanced passive products. The microwave polarization difference index and HV-polarized backscatter show good response to in situ soil moisture and may be considered in SMAP algorithms to further improve the accuracy of soil moisture retrievals. We expect that our findings can be fed back to improve the SMAP soil moisture algorithms and thus promote the application of SMAP soil moisture products in terrestrial water, energy, and carbon cycles. [ABSTRACT FROM AUTHOR]

In civil engineering, ground penetrating radar is a common technique for evaluating the structure and quality of road pavement. This paper focuses on the estimation of the time delay and interface roughness of civil engineering structure, like pavements. The influence of interface roughness is taken into account in the signal model. A modified estimation of signal parameters via rotational invariance technique (ESPRIT) algorithm combined with an interpolated spatial smoothing technique is proposed. It allows us to jointly and efficiently estimate the time delay and interface roughness by ultrawideband radar (the upper frequency up to 8-10 GHz) with low computational complexity. The proposed algorithm is tested on both numerical and experimental data. Simulation and experimental results show the good performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Anomaly detection (AD) is an important technique in hyperspectral data analysis that permits to distinguish rare objects with unknown spectral signatures that are particularly not abundant in a scene. In this paper, a novel algorithm for an accurate detection of anomalies in hyperspectral images with a low computational complexity, named ADALOC2, is proposed. It is based on two main processing stages. First, a set of characteristic pixels that best represent both anomaly and background classes are extracted applying orthogonal projection techniques. Second, the abundancemaps associated to these pixels are estimated. Under the assumption that the anomaly class is composed of a scarce group of image pixels, rare targets can be identified from abundance maps characterized by a representation coefficient matrix with a large amount of almost zero elements. Unlike the other algorithms of the state of the art, the ADALOC2 algorithm has been specially designed for being efficiently implemented into parallel hardware devices for applications under real-time constraints. To achieve this, the ADALOC2 algorithm uses simple and highly parallelized operations, avoiding to perform complex matrix operations such as the computation of an inverse matrix or the extraction of eigenvalues and eigenvectors. An extensive set of simulations using the most representative state-of-the-art AD algorithms and both real and synthetic hyperspectral data sets have been conducted. Moreover, extra assessment metrics apart from classical receiver operating characteristic curves have been defined in order to make deeper comparisons. The obtained results clearly support the benefits of our proposal, both in terms of the accuracy of the detection results and the processing power demanded. [ABSTRACT FROM AUTHOR]

The spaceborne stripmap range sweep synthetic aperture radar (SS-RSSAR) is a new concept spaceborne SAR system that images the region of interest (ROI) with ROI-orientated strips, which, unlike the traditional spaceborne SAR, are allowed to be not parallel with the satellite orbit. The SS-RSSAR imaging is a challenging problem because echoes of a wide region have strong spatial varieties, especially in high-squint geometries, and are hard to be focused by a single swath. The traditional imaging algorithms could solve this problem by cost-ineffectively dividing an ROI into many subswaths for separate processing. In this paper, a new wide nonlinear chirp scaling (W-NLCS) algorithm is proposed to efficiently image the SS-RSSAR data in a single swath. Comparing with the traditional nonlinear chirp scaling algorithm, the W-NLCS algorithm is superior in three major aspects: the nonlinear bulk range migration compensation (RMC), the interpolation-based residual RMC, and the modified azimuth frequency perturbation. Specifically, the interpolation for the residual RMC, the most significant step in achieving the wide-swath imaging performance, is made innovatively in the time domain. The derivation of the W-NLCS algorithm, as well as the performance analyses of the W-NLCS algorithm in aspects of the azimuth resolution, accuracy, and complexity, are all provided. The presented approach is evaluated by the point target simulations. [ABSTRACT FROM PUBLISHER]

The Cloude and Pottier H/α feature space is one of the most employed methods for unsupervised polarimetric synthetic aperture radar (PolSAR) data classification based on incoherent target decomposition (ICTD). The method can be split in two stages: the retrieval of the canonical scattering mechanisms present in an image cell and their parameterization. The association of the coherence matrix eigenvectors to the most dominant scattering mechanisms in the analyzed pixel introduces unfeasible regions in the H/α plane. This constraint can compromise the performance of detection, classification, and geophysical parameter inversion algorithms that are based on the investigation of this feature space. The independent component analysis (ICA), recently proposed as an alternative to eigenvector decomposition, provides promising new information to better interpret non-Gaussian heterogeneous clutter (inherent to highresolution SAR systems) in the frame of polarimetric ICTDs. Not constrained to any orthogonality between the estimated scattering mechanisms that compose the clutter under analysis, ICA does not introduce any unfeasible region in the H/α plane, increasing the range of possible natural phenomena depicted in the aforementioned feature space. This paper addresses the potential of the new information provided by the ICA as an ICTD method with respect to Cloude and Pottier H/α feature space. A PolSAR data set acquired in October 2006 by the E-SAR system over the upper part of the Tacul glacier from the Chamonix Mont Blanc test site, France, and a RAMSES X-band image acquired over Brétigny, France, are taken into consideration to investigate the characteristics of pixels that may fall outside the feasible regions in the H/α plane that arise when the eigenvector approach is employed. [ABSTRACT FROM PUBLISHER]

Following the great success of deep convolutional neural networks (CNNs) in computer vision, this paper proposes a complex-valued CNN (CV-CNN) specifically for synthetic aperture radar (SAR) image interpretation. It utilizes both amplitude and phase information of complex SAR imagery. All elements of CNN including input-output layer, convolution layer, activation function, and pooling layer are extended to the complex domain. Moreover, a complex backpropagation algorithm based on stochastic gradient descent is derived for CV-CNN training. The proposed CV-CNN is then tested on the typical polarimetric SAR image classification task which classifies each pixel into known terrain types via supervised training. Experiments with the benchmark data sets of Flevoland and Oberpfaffenhofen show that the classification error can be further reduced if employing CV-CNN instead of conventional real-valued CNN with the same degrees of freedom. The performance of CV-CNN is comparable to that of existing state-of-the-art methods in terms of overall classification accuracy. [ABSTRACT FROM PUBLISHER]

Hyperspectral images (HSIs) have evident advantages in image understanding due to enormous spectral bands, and rich spatial information. Hundreds of spectral bands, however, actually play different roles in contributing to the class-specific classification. Then, treating each band equally may lead to the underuse or overuse of them. To address this issue, this paper introduces class-specific band contributions (BCs) into the spectral space, and proposes a weighted spectral-spatial classification method for HSIs. In the method, by incorporating BC characterized by F-measure into the distance-based posterior probability, a weighted spectral posterior probability (WSP) model is established. Furthermore, to exploit the spatial information, WSP is then combined with the spatial consistency constraint via an adaptive tradeoff parameter. Additionally, aimed at obtaining the class-dependent F-measures of each band, a semisupervised F-measure prediction method is also developed. Experiments on four hyperspectral data sets are conducted. Experimental results show the superiority of our proposed method over several state-of-the-art methods in terms of three widely used indexes. [ABSTRACT FROM PUBLISHER]

This paper presents a novel topologically aware 2.5-D building modeling methodology from airborne laser scanning point clouds. The building reconstruction process consists of three main steps: primitive clustering, boundary representation, and geometric modeling. In primitive clustering, we propose an enhanced probability density clustering algorithm to cluster the rooftop primitives by taking into account the topological consistency among primitives. In the second step, we employ a novel Voronoi subgraph-based algorithm to seamlessly trace the primitive boundaries. This algorithm guarantees the production of geometric models without crack defects among adjacent primitives. The primitive boundaries are further divided into multiple linear segments, from which the key points are generated. These key points help to form a hybrid representation of the boundary by combining the projected points with part of the original boundary points. The model representation by the hybrid key points is flexible and well captures the rooftop details to generate lightweight and highly regular building models. Finally, we assemble the primitive boundaries to form the topologically correct entities, which are regarded as the basic units for primitive triangulation. The reconstructed models not only have accurate geometry and correct topology but more importantly have abundant semantics, by which five levels of building models can be generated in real time. The proposed reconstruction method has been comprehensively evaluated on Toronto data set in terms of model compactness, multilevel model representation, and geometric accuracy. [ABSTRACT FROM PUBLISHER]

There are two challenges for efficient processing of both the sliding spotlight and terrain observation by progressive scans (TOPS) data using full-aperture algorithms. First, to overcome the Doppler spectrum aliasing, zero-padding is required for azimuth up sampling, increasing the computation burden; second, the azimuth deramp operation for avoiding synthetic aperture radar (SAR) image folding leads to azimuth time shift along the range dimension, and in turn the appearance of ghost targets and azimuth resolution reduction at the scene edge, especially in the wide-swath case. In this paper, a novel three-step algorithm is proposed for processing the sliding spotlight and TOPS data. In the first step, a modified derotation is derived in detail based on the chirp z-transform (CZT), avoiding zero-padding; then, the chirp scaling algorithm kernel is adopted for precise focusing in the second step; and in the third step, instead of the traditional range-independent deramp, a range-dependent deramp is applied to compensate for the time shift. Moreover, the SAR image geometry distortion caused by range-dependent deramp is corrected by employing a range-dependent CZT. Experimental results based on both simulated data and real data are provided to validate the proposed algorithm. [ABSTRACT FROM PUBLISHER]

For circular trace scanning synthetic aperture radar (CTSSAR) with a circular track, the conventional hyperbolic equation becomes inadequate to express the range history of a point target accurately, and when it comes to the wide swath observation and imaging, the range variance makes it even harder to focus the target on the edge of the scene. Thus, an expression with high-order terms is needed to approximate the range history and the range variance should also be considered in the imaging algorithm. In this paper, based on the method of series reversion, a fourth-order approximated range model is established for the CTSSAR processing and the 2-D spectrum is derived for the echo signal in CTSSAR with circular trajectory. At the same time, in order to deal with the range-variant range cell migration problem in large-area CTSSAR imaging, a modified chirp scaling algorithm is proposed to realize precise wide swath CTSSAR focusing. Experiments and analyses are performed to validate the effectiveness of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

Because of flexible geometric configuration and trajectory designation, time-domain algorithms become popular for unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) applications. In this paper, a new quasi-polar-coordinate-based fast factorized back-projection (FFBP) algorithm combined with data-driven motion compensation is proposed for miniature UAV-SAR imaging. By utilizing wavenumber decomposition, the analytical spectrum of a quasi-polar grid image is obtained, where the phase errors arising from the trajectory deviations can be conveniently investigated and the phase autofocusing can be compatibly incorporated. Different from the conventional FFBP based on a polar coordinate system, the proposed algorithm operates in a quasi-polar coordinate system, where the phase errors become spacial invariant and can be accurately estimated and easily compensated. Moreover, the relationship between phase errors and nonsystematic range cell migration (NsRCM) is revealed according to the analytical image spectrum, based on which the NsRCM correction is developed to further improve the image focusing quality for high-resolution SAR applications. Promising experimental results from the raw data experiments of miniature UAV-SAR test bed are presented and analyzed to validate the advantages of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

Multiview stereo (MVS) is a crucial process in image-based automatic 3-D reconstruction and mapping applications. In a dense matching process, the matching cost is generally computed between image pairs, making the efficiency low due to the large number of stereo pairs. This paper presents a novel object-based MVS algorithm using structural similarity (SSIM) index matching cost in a coarse-to-fine workflow. As far as we know, this is the first time SSIM index is introduced to calculate the matching cost of MVS applications. In contrast to classical stereo methods, the proposed object-based structural similarity (OSSIM) method computes only a depth map for each image. Thus, the efficiency can be greatly improved when the overlap between images is large. To obtain an optimized depth map, the winner-take-all and semi-global matching strategies are implemented. Moreover, an object-based multiview consistency checking strategy is also proposed to eliminate wrong matches and perform pixelwise view selection. The proposed method was successfully applied on a close-range Fountain-P11 data set provided by EPFL and aerial data sets of Vaihingen and Zürich by the ISPRS. Experimental results demonstrate that the proposed method can deliver matches at high completeness and accuracy. For the Vaihingen data set, the correctness and completeness rate were 71.12% and 95.99% with an RMSE of 2.8 GSD. For the Foutain-P11 data set, the proposed method outperformed the other existing methods with the ratio of pixels less than 2 cm. Extensive comparison using Zürich data set shows that it can derive results comparable to the state-of-the-art software (PhotoScan, Pix4d, and Smart3D) in urban buildings areas. [ABSTRACT FROM PUBLISHER]

Satellite remote-sensing data are essential for monitoring and quantifying water properties in the Great Lakes, providing useful monitoring and management tools for understanding water optical, biological, and ecological processes and phenomena. However, during the winter season, large parts of the Great Lakes are often covered by ice, which can cause significant uncertainties in satellite-measured water quality products. Although some developed radiance-based ice-detection algorithms for satellite ocean color data processing can eliminate most of the ice pixels in a region, there are still some significant errors due to misidentification of ice-contaminated pixels, particularly for the thin ice-covered regions. Therefore, it is necessary to improve the ice-detection methods for satellite ocean color data processing in the Great Lakes. In this paper, impacts of ice contamination on satellite-derived ocean color products in the Great Lakes are investigated, and a refined regional ice-detection algorithm which is based on the radiance spectra and normalized water-leaving radiance at the wavelength of 551 nm, nLw(551), is developed and assessed for satellite ocean color data processing in the Great Lakes. Results show that this proposed ice-detection method can reasonably identify ice-contaminated pixels, including those in very thin ice-covered regions, and provide accurate satellite ocean color products for the winter season in the Great Lakes. [ABSTRACT FROM PUBLISHER]

Infrared (IR) dim and small target detection in a highly complex background play an important role in many applications, and remain a challenging problem. In this paper, a novel method named stable multisubspace learning is presented to deal with this problem. The new method takes into account the inner structure of actual images so that it overcomes the shortage of the traditional method. First, by analyzing the multisubspace structure of heterogeneous background data, a corresponding image model is proposed using subspace learning strategy. This model is also stable to noise interference. Second, an efficient optimization algorithm is designed to solve the proposed IR image model. By adding the proper postprocessing procedure, we can get the detection result. Experiments on simulation scenes and real scenes show that the proposed method has superior detection ability under heterogeneous background. [ABSTRACT FROM PUBLISHER]

Estimation of the surface elevation change of the Greenland Ice Sheet (GrIS) is essential for understanding its response to recent and future climate change. Laser measurements from the NASA’s Ice, Cloud, and land Elevation Satellite (ICESat) created altimetric surveys of GrIS surface elevations over the 2003–2009 operational period of the mission. This paper compares four change detection methods using Release 634 ICESat laser altimetry data: repeat tracks (RTs), crossovers (XOs), overlapping footprints (OFPs), and triangulated irregular networks (TINs). All four methods begin with a consistently edited data set and yield estimates of volumetric loss of ice from the GrIS ranging from −193 to −269 km3/yr. Using a uniform approach for quantifying uncertainties, we find that volume change rates at the drainage system scale from the four methods can be reconciled within 1- $\sigma $ uncertainties in just 5 of 19 drainage systems. Ice-sheet-wide volume change estimates from the four methods cannot be reconciled within 1- $\sigma $ uncertainties. Our volume change estimates lie within the range of previously published estimates, highlighting that the choice of method plays a dominant role in the scatter of volume change estimates. We find that for much of the GrIS, the OFP and TIN methods yield the lowest volume change uncertainties because of their superior spatial distribution of elevation change rate estimates. However, the RT and XO methods offer inherent advantages, and the future work to combine the elevation change detection methods to produce better estimates is warranted. [ABSTRACT FROM PUBLISHER]

Image reranking is a popular postprocessing method for remote sensing image retrieval (RSIR), which aims at enhancing the initial retrieval performance. In general, it takes either users’ opinions or the relationships between images into consideration to find an optimal reranked list based on the initial retrieved results. In this paper, we present a reranking method for improving RSIR, which is named two-stage reranking (TSR). Suppose the k -nearest neighbors of a query RS image have been obtained by the initial retrieval. The first step of our TSR is to edit these neighbors using the editing scheme. A handful of informative and representative RS images are selected by the active learning algorithm, and their binary labels are provided by the users relative to the query image. Then, a binary classifier is trained using the selected RS images and their labels to classify the rest of the neighbors. Finally, both classification results and rank information in the initial retrieval results are considered to decide which neighbor should be excluded. In the next step, the remaining RS images are reranked by the proposed reranking scheme, i.e., multisimilarity fusion reranking. Both the user’s experience and image relationships are taken into account in TSR to ensure the performance of the reranking. The efficiency and the robustness of our method are validated by experiments conducted on two different types of RS images. Compared with the existing visual reranking approaches, our method achieves improved performance. [ABSTRACT FROM PUBLISHER]

The Gaofen-5 (GF-5) satellite, the only satellite that provides the thermal infrared (TIR) sensor in the national high-resolution earth observation project of China, will observe earth surface at a spatial resolution of 40 m in four TIR channels. This paper aims at developing a new nonlinear, four-channel split-window (SW) algorithm to retrieve land surface temperature (LST) from GF-5 image. In the SW algorithm, its coefficients were obtained based on several subranges of atmospheric column water vapors (CWV) under various land surface conditions, in order to remove the atmospheric effect and improve the retrieval accuracy. Results showed that the new algorithm can obtain LST with root-mean-square errors of less than 1 K. Compared with previous two- and three-channel SW algorithms, the four-channel SW algorithm obtained better results in estimating LST, especially under moist atmospheres. Methods of estimating CWV and pixel emissivity were also conducted. The sensitive analysis of LST retrieval to instrument noise and uncertainty of pixel emissivity and water vapor demonstrated the good performance of the proposed algorithm. At last, the new SW algorithm was validated using ground-measured data at six sites, and some simulated images from airborne hyperspectral TIR data. [ABSTRACT FROM PUBLISHER]

Highly squinted synthetic aperture radar (SAR) data focusing is a challenging problem with difficulty to correct the severe range-azimuth coupling and motion errors. Squint minimization processing with the range-walk correction is widely adapted to simplify the decoupling processing, while it destructs the azimuth-shift invariance of conventional SAR transfer function. In this paper, a two-stage focusing algorithm (TSFA) is proposed to generate a focused imagery for the highly squinted airborne SAR. In the proposed algorithm, conventional range cell migration correction and azimuth matched filtering are performed and a fine focusing stage is established to correct the azimuth variance. In the fine focusing procedure, the coarse-focused image is divided into azimuth blocks to accommodate the correction of azimuth-variant residual range migration and phase terms. Moreover, precise motion compensation is embedded into the TSFA procedure to form an accurate airborne SAR imagery, which may be called the extended TSFA. In order to balance the processing precision and computational load, optimal selection of block size is investigated in detail. Both simulated and real measured airborne SAR data sets are used to validate the proposed approaches. [ABSTRACT FROM PUBLISHER]

In high spatial resolution (HSR) imagery scene classification, it is a challenging task to recognize the high-level semantics from a large volume of complex HSR images. The probabilistic topic model (PTM), which focuses on modeling topics, has been proposed to bridge the so-called semantic gap. Conventional PTMs usually model the images with a dense semantic representation and, in general, one topic space is generated for all the different features. However, this approach fails to consider the sparsity of the semantic representation, the classification quality, as well as the time consumption. In this paper, to solve the above problems, a fully sparse semantic topic model (FSSTM) framework is proposed for HSR imagery scene classification. FSSTM, with an elaborately designed modeling procedure, is able to represent the image with sparse but representative semantics. Based on this framework, the topic weights of multiple features are exploited by solving a concave maximization problem, which improves the fusion of the discriminative semantic information at the topic level. Meanwhile, the sparsity and representativeness of the topics generated by FSSTM guarantee that the image is adaptive to the change of a topic number. FSSTM can consistently achieve a good performance with a limited number of training samples, and is robust for HSR image scene classification. The experimental results obtained with three different types of HSR image data sets confirm that the proposed algorithm is effective in improving the performance of scene classification, and is highly efficient in discovering the semantics of HSR images when compared with the state-of-the-art PTM methods. [ABSTRACT FROM PUBLISHER]

Hyperspectral image classification faces various levels of difficulty due to the use of different types of hyperspectral image data. Recently, spectral–spatial approaches have been developed by jointly taking care of spectral and spatial information. This paper presents a completely different approach from a subpixel target detection view point. It implements four stage processes, a preprocessing stage, which uses band selection (BS) and nonlinear band expansion, referred to as BS-then-nonlinear expansion (BSNE), a detection stage, which implements constrained energy minimization (CEM) to produce subpixel target maps, and an iterative stage, which develops an iterative CEM (ICEM) by applying Gaussian filters to capture spatial information, and then feeding the Gaussian-filtered CEM-detection maps back to BSNE band images to reprocess CEM in an iterative manner. Finally, in the last stage Otsu’s method is applied to converting ICEM-detected real-valued maps to discrete values for classification. The entire process is called BSNE-ICEM. Experimental results demonstrate BSNE-ICEM, which has advantages over support vector machine-based approaches in many aspects, such as easy implementation, fewer parameters to be used, and better false classification and precision rates. [ABSTRACT FROM PUBLISHER]

The concept of the “velocity synthetic aperture radar” (VSAR)—a multiaperture sensor capable of measuring radial velocities in the scene and utilizing this information to correct motion-induced imaging distortions inherent to SAR—was proposed two decades ago. Lately, with the emergence of truly multichannel systems featuring antenna arrays with dozens of elements, the approach has been enjoying a renewed interest. The viability and effectiveness of the algorithm were successfully demonstrated in a series of airborne field campaigns that involved imaging both man-made targets and natural maritime features. These experiments and the wealth of resulting data also underscored the need for comprehensive mathematical descriptions of expected target signatures in the collected “image stacks” and for further refinements of the VSAR imaging theory. This paper addresses both tasks by building upon the available mathematical results developed for the along-track interferometric SAR imagery of distributed evolving targets. The approach allows simultaneous accounting for all essential effects known to impact SAR imagery of a target or an extended feature: its azimuth velocity, radial velocity and acceleration, as well as finite coherence time. The emphasis is on obtaining closed-form expressions that could readily illustrate the structure and behavior of the VSAR stack spectrum of such a target and help gauge anticipated focusing improvement stemming from the VSAR image correction. In particular, it is rigorously shown that the VSAR algorithm is successful in situations when SAR defocusing arises predominantly from radial motion and short coherence times—the resulting resolution is generally no worse than that of the corresponding real-aperture radar. On the other hand, strong defocusing due to azimuth translation may be problematic to compensate within the VSAR approach framework. [ABSTRACT FROM PUBLISHER]

Low-cost flight-based hyperspectral imaging systems have the potential to provide important information for ecosystem and environmental studies as well as aide in land management. To realize this potential, methods must be developed to provide large-area surface reflectance data allowing for temporal data sets at the mesoscale. This paper describes a bootstrap method of producing a large-area, radiometrically referenced hyperspectral data set using the Landsat surface reflectance (LaSRC) data product as a reference target. The bootstrap method uses standard hyperspectral processing techniques that are extended to remove uneven illumination conditions between flight passes, allowing for radiometrically self-consistent data after mosaicking. Through selective spectral and spatial resampling, LaSRC data are used as a radiometric reference target. Advantages of the bootstrap method include the need for minimal site access, no ancillary instrumentation, and automated data processing. Data from two hyperspectral flights over the same managed agricultural and unmanaged range land covering approximately 5.8 km2 acquired on June 21, 2014 and June 24, 2015 are presented. Data from a flight over agricultural land collected on June 6, 2016 are compared with concurrently collected ground-based reflectance spectra as a means of validation. [ABSTRACT FROM PUBLISHER]

This paper develops distortion metrics for compressed synthetic aperture radar (SAR) imagery from change detection test statistics. These metrics are used to predict lossy image compression’s impact on change detection performance. The metrics do not require the intended change detection comparison image to provide these benefits. An SAR compression system leveraging the distortion metrics is proposed. The system generates a bad-pixel mask highlighting potential false alarms that are generated due to compression and are subsequently discarded in the change detection process. The proposed system’s performance is demonstrated through noncoherent change detection analysis after JPEG2000 and JPEG image compression. Similarly, a coherent change detection system is evaluated after JPEG2000 image compression. For noncoherent change detection at large compression ratios (CRs) using JPEG2000, the proposed system provides a 33% reduction in false alarms at a 0.1 probability of detection as well as the ability to maintain near-distortionless false alarm rates across a wide range of CRs. At a 0.1 probability of detection for coherent change detection, the system provides a 37% reduction in false alarms at modest CRs. The coherent change detection system is also demonstrated to maintain low false alarm rates across a range of CRs. [ABSTRACT FROM PUBLISHER]

Surface upward longwave radiation (SULR) is a significant component of the surface radiation budget and is closely linked with evapotranspiration, soil moisture, and surface cooling on clear nights. Therefore, accurately estimating SULR is essential to better understand its spatiotemporal dynamics or to characterize the thermal environment of a given land surface. Currently, most methods for estimating SULR (including the physical and hybrid methods) fail to account for the thermal anisotropy, which can introduce significant errors into the calculation. We previously proposed the combined algorithm that considers the thermal anisotropy to more accurately estimate the SULR. However, this proposed method has several shortcomings. For example, it considers the directionality of the emissivity and the effective temperature separately under the support of a parametric directional emissivity model. However, the directional emissivity model is not maturely developed for different land surface types, especially on nonvegetated surfaces. And the separation of land surface temperature and emissivity may undermine the estimation accuracy. Furthermore, this proposed method requires a series of input parameters that is not always available, limiting its applicability. In this paper, we present a refined algorithm that uses a kernel-driven model and the technique of band conversion to calculate the SULR directly based on surface-leaving radiances. This direct physical algorithm is then applied to the Wide-angle infrared Dual-mode line/area Array Scanner data set and validated using longwave radiation data collected by automatic meteorological stations from the Heihe Watershed Allied Telemetry Experimental Research experiment. The results of these tests suggest that the direct algorithm works effectively. The root-mean-square error (RMSE) and mean bias error (MBE) of the direct algorithm on maize surfaces are 4.417 and 0.474 \text W\cdot ~\text m^-2 , respectively. When the thermal anisotropy is incorporated, the RMSE and absolute MBE decrease by a maximum of 4.734 and 7.414 \mathrm W\cdot \mathrm m^\mathrm -2 , respectively. Different land types yield different results: for vegetable surfaces, the estimation biases of the direct model are approximately -2\mathrm W\cdot \mathrm m^\mathrm -2 , whereas orchard surfaces yield biases are between −2 and -3.5\mathrm W\cdot \mathrm m^-2 , and village surfaces yield biases exceeding - 10~\mathrm W\cdot \mathrm m^-2 . These differences can be attributed to the varying effects of the kernel-driven model across different types of land surfaces. The RMSE and absolute MBE obtained using the direct algorithm are slightly smaller (0.587 and 1.685 \mathrm W\cdot \mathrm m^\mathrm -2 , respectively) than those obtained using the combined algorithm; they are also smaller than the results of the traditional temperature–emissivity algorithm (by 8.7 and 11.7 \mathrm W\cdot \mathrm m^-2 , respectively). [ABSTRACT FROM PUBLISHER]

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]

It has always been a great challenge to efficiently detect small infrared targets from complex image backgrounds without any prior knowledge. This is especially true when both strong and weak targets appear in the same image or when the weak targets come up on image borders. The main contribution of this paper is to design a robust infrared maritime target detection method, in which a visual attention and pipeline-filtering model is proposed by integrating a revised visual attention model (VAM) and the antivibration pipeline-filtering algorithm. The revised VAM, a single-frame target detection strategy, will first compute a saliency map (SM) from a specific modality, which is automatically selected according to image background smoothness. Then, an automatic strategy for extracting suspected targets from an SM is also proposed here, which highlights targets and suppresses background clutters in SMs through local saliency singularity evaluation. Moreover, contrary to the original VAM, we adopt border saliency preservation in center-surround difference so that robust detection can be guaranteed for targets on image borders. Finally, to eliminate the interference of sea glints and confirm real targets, we adopt the antivibration pipeline-filtering algorithm, a multiframe-based clutter removal method. Compared with the original VAM and two other existing target detection algorithms, experimental results have proven that our strategy can detect infrared maritime targets much better under different environmental conditions. This research can significantly improve the success rate and efficiency of searching maritime targets in different weathers using infrared imager, especially in heavy sea fog and strong ocean waves. [ABSTRACT FROM PUBLISHER]

We analyze the methods for measuring azimuth deformation with the L-band Advanced Land Observing Satellite-2 (ALOS-2) scanning synthetic aperture radar (ScanSAR) interferometry. To implement the methods, we extract focused bursts from the ALOS-2 full-aperture product, which is the only product available for ScanSAR interferometry at present. The extracted bursts are properly processed to measure azimuth deformation using interferometric phase. We apply the range split-spectrum method to ScanSAR to estimate the differential ionospheric phase of the interferogram, and take the azimuth derivative of the differential ionospheric phase to mitigate the relative azimuth shift caused by ionosphere. For the first time, azimuth deformation of a large earthquake (April 25, 2015 Nepal earthquake) is nearly completely measured by the L-band ScanSAR interferometry with moderate precision. The result is validated by the azimuth deformation measured by incoherent cross correlation using a pair of high-resolution RADARSAT-2 images. In addition to the final azimuth deformation, we show the possibility of processing full-aperture ScanSAR product using a burst-by-burst approach to form regular interferograms. We also show the recent strong large-scale ionospheric effects on the L-band ALOS-2 ScanSAR interferograms. Other possible applications of this paper include measuring the movement of glaciers. [ABSTRACT FROM PUBLISHER]

In this paper, a new pseudoanalytical axial mode-matching formulation is introduced to provide a flexible technique for analyzing direction well-logging tools in anisotropic formations. The presented technique does not rely on spatial discretization as former well-known methods. In this problem, a number of coil antennas with arbitrary relative tilt angle with respect to the symmetry axis are used to radiate electromagnetic fields in a cylindrically layered medium with both axial and radial stratifications, composed of a metallic mandrel, a borehole, and a surrounding layered earth formation. This configuration corresponds to that of triaxial well-logging tools used for oil and gas exploration. Our approach combines closed-form solutions of Maxwell’s equations for uniaxially anisotropic media in cylindrical coordinates with the generalized scattering matrix (GSM) at each axial discontinuity based on the mode-matching technique. The electromagnetic field from the tilted-coil source is represented by a set of modal coefficients that, after computation using GSM matrices, are used to extract the transimpedances of the well-logging tool. [ABSTRACT FROM PUBLISHER]

In this paper, a graph-based nonlocal total variation method is proposed for unsupervised classification of hyperspectral images (HSI). The variational problem is solved by the primal-dual hybrid gradient algorithm. By squaring the labeling function and using a stable simplex clustering routine, an unsupervised clustering method with random initialization can be implemented. The effectiveness of this proposed algorithm is illustrated on both synthetic and real-world HSI, and numerical results show that the proposed algorithm outperforms other standard unsupervised clustering methods, such as spherical $K$ -means, nonnegative matrix factorization, and the graph-based Merriman–Bence–Osher scheme. [ABSTRACT FROM PUBLISHER]

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

In this paper, we focus on tackling the problem of automatic accurate localization of detected objects in high-resolution remote sensing images. The two major problems for object localization in remote sensing images caused by the complex context information such images contain are achieving generalizability of the features used to describe objects and achieving accurate object locations. To address these challenges, we propose a new object localization framework, which can be divided into three processes: region proposal, classification, and accurate object localization process. First, a region proposal method is used to generate candidate regions with the aim of detecting all objects of interest within these images. Then, generic image features from a local image corresponding to each region proposal are extracted by a combination model of 2-D reduction convolutional neural networks (CNNs). Finally, to improve the location accuracy, we propose an unsupervised score-based bounding box regression (USB-BBR) algorithm, combined with a nonmaximum suppression algorithm to optimize the bounding boxes of regions that detected as objects. Experiments show that the dimension-reduction model performs better than the retrained and fine-tuned models and the detection precision of the combined CNN model is much higher than that of any single model. Also our proposed USB-BBR algorithm can more accurately locate objects within an image. Compared with traditional features extraction methods, such as elliptic Fourier transform-based histogram of oriented gradients and local binary pattern histogram Fourier, our proposed localization framework shows robustness when dealing with different complex backgrounds. [ABSTRACT FROM PUBLISHER]

The high-dimensional data space of hyperspectral images (HSIs) often result in ill-conditioned formulations, which finally leads to many of the high-dimensional feature spaces being empty and the useful data existing primarily in a subspace. To avoid these problems, we use distance metric learning for dimensionality reduction. The goal of distance metric learning is to incorporate abundant discriminative information by reducing the dimensionality of the data. Considering that global metric learning is not appropriate for all training samples, this paper proposes an ensemble discriminative local metric learning (EDLML) algorithm for HSI analysis. The EDLML algorithm learns robust local metrics from both the training samples and the relative neighborhood of them and considers the different local discriminative distance metrics by dealing with the data region by region. It aims to learn a subspace to keep all the samples in the same class are as near as possible, while those from different classes are separated. The learned local metrics are then used to build an ensemble metric. Experiments on a number of different hyperspectral data sets confirm the effectiveness of the proposed EDLML algorithm compared with that of the other dimension reduction methods. [ABSTRACT FROM PUBLISHER]

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 a plethora of applications dealing with inverse problems, e.g., image processing, social networks, compressive sensing, and biological data processing, the signal of interest is known to be structured in several ways at the same time. This premise has recently guided research into the innovative and meaningful idea of imposing multiple constraints on the unknown parameters involved in the problem under study. For instance, when dealing with problems whose unknown parameters form sparse and low-rank matrices, the adoption of suitably combined constraints imposing sparsity and low rankness is expected to yield substantially enhanced estimation results. In this paper, we address the spectral unmixing problem in hyperspectral images. Specifically, two novel unmixing algorithms are introduced in an attempt to exploit both spatial correlation and sparse representation of pixels lying in the homogeneous regions of hyperspectral images. To this end, a novel mixed penalty term is first defined consisting of the sum of the weighted \ell1 and the weighted nuclear norm of the abundance matrix corresponding to a small area of the image determined by a sliding square window. This penalty term is then used to regularize a conventional quadratic cost function and impose simultaneous sparsity and low rankness on the abundance matrix. The resulting regularized cost function is minimized by: 1) an incremental proximal sparse and low-rank unmixing algorithm; and 2) an algorithm based on the alternating direction method of multipliers. The effectiveness of the proposed algorithms is illustrated in experiments conducted both on simulated and real data. [ABSTRACT FROM PUBLISHER]

This paper investigates the problems of outliers and/or noise in surface segmentation and proposes a statistically robust segmentation algorithm for laser scanning 3-D point cloud data. Principal component analysis (PCA)-based local saliency features, e.g., normal and curvature, have been frequently used in many ways for point cloud segmentation. However, PCA is sensitive to outliers; saliency features from PCA are nonrobust and inaccurate in the presence of outliers; consequently, segmentation results can be erroneous and unreliable. As a remedy, robust techniques, e.g., RANdom SAmple Consensus (RANSAC), and/or robust versions of PCA (RPCA) have been proposed. However, RANSAC is influenced by the well-known swamping effect, and RPCA methods are computationally intensive for point cloud processing. We propose a region growing based robust segmentation algorithm that uses a recently introduced maximum consistency with minimum distance based robust diagnostic PCA (RDPCA) approach to get robust saliency features. Experiments using synthetic and laser scanning data sets show that the RDPCA-based method has an intrinsic ability to deal with outlier- and/or noise-contaminated data. Results for a synthetic data set show that RDPCA is 105 times faster than RPCA and gives more accurate and robust results when compared with other segmentation methods. Compared with RANSAC and RPCA based methods, RDPCA takes almost the same time as RANSAC, but RANSAC results are markedly worse than RPCA and RDPCA results. Coupled with a segment merging algorithm, the proposed method is efficient for huge volumes of point cloud data consisting of complex objects surfaces from mobile, terrestrial, and aerial laser scanning systems. [ABSTRACT FROM PUBLISHER]

In hyperspectral unmixing applications, one typically assumes that a single spectrum exists for every endmember. In many scenarios, this is not the case, and one requires a set or a distribution of spectra to represent an endmember or class. This inherent spectral variability can pose severe difficulties in classical unmixing approaches. In this paper, we present a new algorithm for dealing with endmember variability in spectral unmixing, based on the geometrical interpretation of the resulting unmixing problem, and an alternating optimization approach. This alternating-angle-minimization algorithm uses sets of spectra to represent the variability present in each class and attempts to identify the subset of endmembers which produce the smallest reconstruction error. The algorithm is analogous to the popular multiple endmember spectral mixture analysis technique but has a much more favorable computational complexity while producing similar results. We illustrate the algorithm on several artificial and real data sets and compare with several other recent techniques for dealing with endmember variability. [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]

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]