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In this paper, a new joint spectral–spatial subpixel mapping model is proposed for hyperspectral remotely sensed imagery. Conventional approaches generally use an intermediate step based on the derivation of fractional abundance maps obtained after a spectral unmixing process, and thus the rich spectral information contained in the original hyperspectral data set may not be utilized fully. In this paper, a concept of subpixel abundance map, which calculates the abundance fraction of each subpixel to belong to a given class, was introduced. This allows us to directly connect the original (coarser) hyperspectral image with the final subpixel result. Furthermore, the proposed approach incorporates the spectral information contained in the original hyperspectral imagery and the concept of spatial dependence to generate a final subpixel mapping result. The proposed approach has been experimentally evaluated using both synthetic and real hyperspectral images, and the obtained results demonstrate that the method achieves better results when compared to other seven subpixel mapping methods. The numerical comparisons are based on different indexes such as the overall accuracy and the CPU time. Moreover, the obtained results are statistically significant at 95% confidence. [ABSTRACT FROM AUTHOR]

Path delays in IP networks are important metrics, required by network operators for assessment, planning, and fault diagnosis. Monitoring delays of all source-destination pairs in a large network are, however, challenging and wasteful of resources. This paper advocates a spatio-temporal Kalman filtering approach to construct network-wide delay maps using measurements on only a few paths. The proposed network cartography framework allows efficient tracking and prediction of delays by relying on both topological as well as historical data. Optimal paths for delay measurement are selected in an online fashion by leveraging the notion of submodularity. The resulting predictor is optimal in the class of linear predictors, and outperforms competing alternatives on real-world data sets. [ABSTRACT FROM AUTHOR]

Spectrum cartography constructs maps of metrics such as channel gain or received signal power across a geographic area of interest using spatially distributed sensor measurements. Applications of these maps include network planning, interference coordination, power control, localization, and cognitive radios to name a few. Since existing spectrum cartography techniques require accurate estimates of the sensor locations, their performance is drastically impaired by multipath affecting the positioning pilot signals. This phenomenon occurs especially in indoor or dense urban scenarios. To overcome such a limitation, this paper introduces a novel paradigm for spectrum cartography, where estimation of spectral maps relies on features of these positioning signals rather than on location estimates. Specific learning algorithms are built on this approach and offer a markedly improved estimation performance than those of the existing approaches relying on localization, as demonstrated by simulation studies in indoor scenarios. [ABSTRACT FROM AUTHOR]

Recognizing the identity of a sketched face from a face photograph dataset is a critical yet challenging task in many applications, not least law enforcement and criminal investigations. An intelligent sketched face identification system would rely on automatic face sketch synthesis from photographs, thereby avoiding the cost of artists manually drawing sketches. However, conventional face sketch–photo synthesis methods tend to generate sketches that are consistent with the artists’drawing styles. Identity-specific information is often overlooked, leading to unsatisfactory identity verification and recognition performance. In this paper, we discuss the reasons why conventional methods fail to recover identity-specific information. Then, we propose a novel dual-transfer face sketch–photo synthesis framework composed of an inter-domain transfer process and an intra-domain transfer process. In the inter-domain transfer, a regressor of the test photograph with respect to the training photographs is learned and transferred to the sketch domain, ensuring the recovery of common facial structures during synthesis. In the intra-domain transfer, a mapping characterizing the relationship between photographs and sketches is learned and transferred across different identities, such that the loss of identity-specific information is suppressed during synthesis. The fusion of information recovered by the two processes is straightforward by virtue of an ad hoc information splitting strategy. We employ both linear and nonlinear formulations to instantiate the proposed framework. Experiments on The Chinese University of Hong Kong face sketch database demonstrate that compared to the current state-of-the-art the proposed framework produces more identifiable facial structures and yields higher face recognition performance in both the photo and sketch domains. [ABSTRACT FROM AUTHOR]

In order to characterize the mechanical behavior of fragile resonant microelectromechanical systems (MEMS)/nanoelectromechanical systems (NEMS), nondestructive measurements are required. In this paper, a cartography of local stiffness variations on a suspended micromembrane is established for the first time, by a tuning-fork-based dynamic force sensor inside a scanning electron microscope (SEM). Experiments are conducted individually on a batch of InP membranes 200  nm thin, using a 9-degree-of-freedom (dof) nanomanipulation system, complemented with virtual reality and automation tools. Results provide stiffness values in the range of a few newton per meter, with variations in a single sample depending on the membrane models. [ABSTRACT FROM PUBLISHER]

Visualizing spatial data on small-screen devices such as smartphones and smartwatches poses new challenges in computational cartography. The current interfaces for map exploration require their users to zoom in and out frequently. Indeed, zooming and panning are tools suitable for choosing the map extent corresponding to an area of interest. They are not as suitable, however, for resolving the graphical clutter caused by a high feature density since zooming in to a large map scale leads to a loss of context. Therefore, in this paper, we present new external labeling methods that allow a user to navigate through dense sets of points of interest while keeping the current map extent fixed. We provide a unified model, in which labels are placed at the boundary of the map and visually associated with the corresponding features via connecting lines, which are called leaders. Since the screen space is limited, labeling all features at the same time is impractical. Therefore, at any time, we label a subset of the features. We offer interaction techniques to change the current selection of features systematically and, thus, give the user access to all features. We distinguish three methods, which allow the user either to slide the labels along the bottom side of the map or to browse the labels based on pages or stacks. We present a generic algorithmic framework that provides us with the possibility of expressing the different variants of interaction techniques as optimization problems in a unified way. We propose both exact algorithms and fast and simple heuristics that solve the optimization problems taking into account different criteria such as the ranking of the labels, the total leader length as well as the distance between leaders. In experiments on real-world data we evaluate these algorithms and discuss the three variants with respect to their strengths and weaknesses proving the flexibility of the presented algorithmic framework. [ABSTRACT FROM AUTHOR]

In quantitative MR T1 mapping, the spin-lattice relaxation time T1 of tissues is estimated from a series of T1 -weighted images. As the T1 estimation is a voxel-wise estimation procedure, correct spatial alignment of the T1 -weighted images is crucial. Conventionally, the T1 -weighted images are first registered based on a general-purpose registration metric, after which the T1 map is estimated. However, as demonstrated in this paper, such a two-step approach leads to a bias in the final T1 map. In our work, instead of considering motion correction as a preprocessing step, we recover the motion-free T1 map using a unified estimation approach. In particular, we propose a unified framework where the motion parameters and the T1 map are simultaneously estimated with a Maximum Likelihood (ML) estimator. With our framework, the relaxation model, the motion model as well as the data statistics are jointly incorporated to provide substantially more accurate motion and T1 parameter estimates. Experiments with realistic Monte Carlo simulations show that the proposed unified ML framework outperforms the conventional two-step approach as well as state-of-the-art model-based approaches, in terms of both motion and T1 map accuracy and mean-square error. Furthermore, the proposed method was additionally validated in a controlled experiment with real T1 -weighted data and with two in vivo human brain T1 -weighted data sets, showing its applicability in real-life scenarios. [ABSTRACT FROM AUTHOR]

Cross-calibration of satellite sensors permits the quantitative comparison of measurements obtained from different Earth Observing (EO) systems. Cross-calibration studies usually use simultaneous or near-simultaneous observations from several spaceborne sensors to develop band-by-band relationships through regression analysis. The investigation described in this paper focuses on evaluation of the uncertainties inherent in the cross-calibration process, including contributions due to different spectral responses, spectral resolution, spectral filter shift, geometric misregistrations, and spatial resolutions. The hyperspectral data from the Environmental Satellite SCanning Imaging Absorption SpectroMeter for Atmospheric CartograpHY and the EO-1 Hyperion, along with the relative spectral responses (RSRs) from the Landsat 7 Enhanced Thematic Mapper (TM) Plus and the Terra Moderate Resolution Imaging Spectroradiometer sensors, were used for the spectral uncertainty study. The data from Landsat 5 TM over five representative land cover types (desert, rangeland, grassland, deciduous forest, and coniferous forest) were used for the geometric misregistrations and spatial-resolution study. The spectral resolution uncertainty was found to be within 0.25%, spectral filter shift within 2.5%, geometric misregistrations within 0.35%, and spatial-resolution effects within 0.1% for the Libya 4 site. The one-sigma uncertainties presented in this paper are uncorrelated, and therefore, the uncertainties can be summed orthogonally. Furthermore, an overall total uncertainty was developed. In general, the results suggested that the spectral uncertainty is more dominant compared to other uncertainties presented in this paper. Therefore, the effect of the sensor RSR differences needs to be quantified and compensated to avoid large uncertainties in cross-calibration results. [ABSTRACT FROM PUBLISHER]

The unceasing demand for continuous situational awareness calls for innovative and large-scale signal processing algorithms, complemented by collaborative and adaptive sensing platforms to accomplish the objectives of layered sensing and control. Towards this goal, the present paper develops a spline-based approach to field estimation, which relies on a basis expansion model of the field of interest. The model entails known bases, weighted by generic functions estimated from the field's noisy samples. A novel field estimator is developed based on a regularized variational least-squares (LS) criterion that yields finite-dimensional (function) estimates spanned by thin-plate splines. Robustness considerations motivate well the adoption of an overcomplete set of (possibly overlapping) basis functions, while a sparsifying regularizer augmenting the LS cost endows the estimator with the ability to select a few of these bases that “better” explain the data. This parsimonious field representation becomes possible, because the sparsity-aware spline-based method of this paper induces a group-Lasso estimator for the coefficients of the thin-plate spline expansions per basis. A distributed algorithm is also developed to obtain the group-Lasso estimator using a network of wireless sensors, or, using multiple processors to balance the load of a single computational unit. The novel spline-based approach is motivated by a spectrum cartography application, in which a set of sensing cognitive radios collaborate to estimate the distribution of RF power in space and frequency. Computer simulations and tests on real data corroborate that the estimated power spectrum density atlas yields the desired RF state awareness, since the maps reveal spatial locations where idle frequency bands can be reused for transmission, even when fading and shadowing effects are pronounced. [ABSTRACT FROM PUBLISHER]

Effective engine mapping and calibration are contingent upon tight control of the environment in which the mapping and calibration are performed. Among the most important variables to be controlled are the temperatures of coolant and oil that circulate through the engine block. Because of the large time constants associated with thermodynamic systems, controlling these variables often represents a bottleneck in the engine mapping and calibration processes. In this paper, we examine a particular layout for a thermal management unit, which is currently being used in practice. By developing and analyzing a thermodynamic model of the system, we are able to gain insight into the system dynamics and explore special features to optimize the temperature response. In particular, we will show how the overactuation in the system may be leveraged in the presence of hard saturation constraints and different dynamic actuator authorities. We present design and validation results (both simulation and experimental) for the proposed controller, and compare the performance to the baseline con- troller in order to quantify improvements. [ABSTRACT FROM AUTHOR]

Monitoring changes in the vegetation cover during the intercrop season is of special interest in intensive agricultural region, such as the Brittany region in France, to locate bare soils and control their influence to the environment. The presence of bare soils leads to detrimental environmental effects such as soil erosion or water quality degradation. Therefore, identification and monitoring of bare soils at a regional scale in the winter season are required for any agricultural management program. Data from the Moderate Resolution Imaging Spectroradiometer have been selected for this paper due to their low spatial resolution, which decreases the cost of processing and storage, and the high revisit frequency, which increases the probability to acquire scenes free of clouds and shadows during the winter season. Unfortunately, few images per season only are free of cloud contamination and associated shadows. Therefore, the specific objective of this paper is to develop and implement a preprocessing method to recover spectral values of contaminated data by weather conditions for subsequent bare-soil mapping. In the context of this paper, Kohonen's self-organizing map (SOM) is used for recovering data contaminated by weather conditions that have been considered as erroneous data. This nonparametric regression procedure has proved its success to deal with missing-values problem. Hence, the erroneous-values problem, reflectance values contaminated by clouds or shadows, has been converted to the missing-values problem by using a cloud and shadow (outlier) detector. The SOM algorithm was tested also on the erroneous data directly, but better results were found with the "missing values" formulation. The idea is to, first, train SOM onto clear temporal profiles free of clouds and shadows during the winter season. Second, erroneous values are converted to missing values by an outlier detector which operates on each temporal profile (set of colocated pixels acquired at different dates). Finally, the SOM algorithm for missing values is used to estimate contaminated reflectance values. [ABSTRACT FROM AUTHOR]

This paper describes the development of an underwater camera system with a laser line source to measure seafloor features at millimeter scales. The quality of underwater photography is limited by the visibility of the water column. In real underwater environments, there are always suspended particles in the water column and light is scattered by these particles. As a result, photographic images taken under such conditions will be blurred. The stronger the light source is, the more severe the blurring will be. Therefore, it is difficult to discern the features of the intended target directly from the underwater images. To overcome this problem, a laser stripe can be projected onto the target and the profile of the target can be inferred from the displacement of the laser scan lines relative to a reference baseline. With a calibrated camera, the displacement expressed in pixels can be converted into the dimensions of the target in engineering units. To obtain a broader view in a closer distance, a wide-angle lens is usually used. As a result, the image taken with the wide-angle lens is nonlinear and is strongly distorted at the edges of the image. Calibration of a camera involves finding the optical and geometrical parameters of the camera and the environment in which it works. In this paper, a modified coordinate mapping calibration procedure is used. We divide the scope of the camera into several regions and build linear mappings between the world coordinate system and the pixels in the regions. We lay vertical and horizontal grid lines separated by 50 mm on an acrylic board that is aligned with the laser scanning sheet. These grid lines serve as longitude and latitude lines of a map. On the captured image, we curve-lit the grid points in pixel coordinates. A pair of interpolated longitude and latitude lines which pass through the target point are used to estimate the location of the point in the world coordinate system. We assess the accuracy of this procedure with test pieces (grooved blocks and seabed ripples) fabricated by a computer numerical control milling machine. Our measurements show that the error is less than 1.5 mm when the target is scanned from a distance of 1 m. [ABSTRACT FROM AUTHOR]

Landsat data are now available for more than 30 years, providing the longest high-resolution record of Earth monitoring. This unprecedented time series of satellite imagery allows for extensive temporal observation of terrestrial processes such as land cover and land use change. However, despite this unique opportunity, most existing change detection techniques do not fully capitalize on this long time series. In this paper, a method that exploits both the temporal and spatial domains of time series satellite data to map land cover changes is presented. The time series of each pixel in the image is modeled with a combination of: 1) pixel-specific remotely sensed data; 2) neighboring pixels derived from ground observation data; and 3) time series transition probabilities. The spatial information is modeled with variograms and integrated using indicator kriging; time series transition probabilities are combined using an information-based cascade approach. This results in a map that is significantly more accurate in identifying when, where, and what land cover changes occurred. For the six images used in this paper, the prediction accuracy of the time series improves significantly, increasing from 31% to 61%, when both space and time are considered with the maximum likelihood. The consideration of spatial continuity also reduced unwanted speckles in the classified images, removing the need for any postprocessing. These results indicate that combining space and time domains significantly improves the accuracy of temporal change detection analyses and can produce high-quality time series land cover maps. [ABSTRACT FROM AUTHOR]

This paper describes a novel representation for two-dimensional maps, and shows how this representation may be applied to the problem of multirobot simultaneous localization and mapping. We are inspired by the notion of a manifold, which takes maps out of the two-dimensional plane and onto a surface embedded in a higher-dimensional space. The key advantage of the manifold representation is self-consistency: when closing loops, manifold maps do not suffer from the "cross over" problem exhibited in planar maps. This self-consistency, in turn. facilitates a number of important capabilities, including autonomous exploration, search, and retro-traverse. It also supports a very robust form of loop closure, in which pairs of robots act collectively to confirm or reject possible correspondence points. In this paper, we develop the basic formalism of the manifold representation, show how this may be applied to the multirobot simultaneous localization and mapping problem, and present experimental results obtained from teams of up to four robots in environments ranging in size from 400 to 900 m². [ABSTRACT FROM AUTHOR]

In this paper, we introduce new types of variational segmentation cost functions and associated active contour methods that are based on pairwise similarities or dissimilarities of the pixels. As a solution to a minimization problem, we introduce a new curve evolution framework, the graph partitioning active contours (GPAC). Using global features, our curve evolution is able to produce results close to the ideal minimization of such cost functions. New and efficient implementation techniques are also introduced in this paper. Our experiments show that GPAC solution is effective on natural images and computationally efficient. Experiments on gray-scale, color, and texture images show promising segmentation results. [ABSTRACT FROM AUTHOR]

This paper presents a global diffeomorphism from a visible set of rigid-body configurations, a subset of SE(3), to an image space. Using the diffeomorphism, we develop an image-based, essentially global, dynamic visual servoing algorithm that keeps features in the field of view and avoids self-occlusions. The approach is geometric in the sense that the visible set and its corresponding image are differentiable manifolds, and the diffeomorphism is global. The mapping to image space and the resulting Jacobian rely on a specific target geometry, a sphere with a known radius marked with an "arrow" feature point. The paper presents simulation experiments for a more typical visual target comprised of a collection of isolated feature points. In this setting, the diffeomorphism to image space is approximate, nevertheless, the simulations converge for a wide variety of target geometries and initial conditions. [ABSTRACT FROM AUTHOR]

The recognition and delineation of hydrological stream lines has, traditionally, been a subjective manual task in cartography. However, digital elevation models (DEMs) are nowadays often employed to extract stream lines automatically, via the use of geographic information systems. Whereas the automatic generation of hydrological networks presents errors, their manual recognition can be almost arbitrary. In this paper, we propose a methodology with which to label potentially sensitive zones in the comparison of hydrological cartographic networks. Two different sources were analyzed: a conventional cartographic stream network, and one automatically extracted from a DEM. The 72 500 vectors of displacement, representing the spatial disagreement (or fit) between the stream networks, were also examined. A number of remarkable distributions of large errors were identified that were a cause for alarm; these errors are here denoted by “warnings” and are classified into six different groups. The displacement vectors were also analyzed in terms of modulus and azimuth, thereby allowing the analysis of the isotropy of the spatial displacements. We propose the use of all of the derived information as metadata for hydrological spatial quality, as well as the extension of the methodology to any other type of cartographic element (roads, cadastral, etc.) for which two different vector format information sources are compared. [ABSTRACT FROM AUTHOR]

Mapping texture onto 3D meshes with positional constraints is a popular technique that can effectively enhance the visual realism of geometric models. Such a process usually requires constructing a valid mesh embedding satisfying a set of positional constraints, which is known to be a challenging problem. This paper presents a novel algorithm for computing a foldover-free piecewise linear mapping with exact positional constraints. The algorithm begins with an unconstrained planar embedding, followed by iterative constrained mesh transformations. At the heart of the algorithm are radial basis function (RBF)-based warping and the longest edge bisection (LEB)-based refinement. A delicate integration of the RBF-based warping and the LEB-based refinement provides a provably foldover-free, smooth constrained mesh warping, which can handle a large number of constraints and output a visually pleasing mapping result without extra smoothing optimization. The experiments demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Space-filling techniques seek to use as much as possible the visual space to represent a dataset, splitting it into regions that represent the data elements. Amongst those techniques, Treemaps have received wide attention due to its simplicity, reduced visual complexity, and compact use of the available space. Several different Treemap algorithms have been proposed, however the core idea is the same, to divide the visual space into rectangles with areas proportional to some data attribute or weight. Although pleasant layouts can be effectively produced by the existing techniques, most of them do not take into account relationships that might exist between different data elements when partitioning the visual space. This violates the distance-similarity metaphor, that is, close rectangles do not necessarily represent similar data elements. In this paper, we propose a novel approach, called Neighborhood Treemap (Nmap), that seeks to solve this limitation by employing a slice and scale strategy where the visual space is successively bisected on the horizontal or vertical directions and the bisections are scaled until one rectangle is defined per data element. Compared to the current techniques with the same similarity preservation goal, our approach presents the best results while being two to three orders of magnitude faster. The usefulness of Nmap is shown by two applications involving the organization of document collections and the construction of cartograms illustrating its effectiveness on different scenarios. [ABSTRACT FROM PUBLISHER]

This paper presents a new approach to flow mapping that extracts inherent patterns from massive geographic mobility data and constructs effective visual representations of the data for the understanding of complex flow trends. This approach involves a new method for origin-destination flow density estimation and a new method for flow map generalization, which together can remove spurious data variance, normalize flows with control population, and detect high-level patterns that are not discernable with existing approaches. The approach achieves three main objectives in addressing the challenges for analyzing and mapping massive flow data. First, it removes the effect of size differences among spatial units via kernel-based density estimation, which produces a measurement of flow volume between each pair of origin and destination. Second, it extracts major flow patterns in massive flow data through a new flow sampling method, which filters out duplicate information in the smoothed flows. Third, it enables effective flow mapping and allows intuitive perception of flow patterns among origins and destinations without bundling or altering flow paths. The approach can work with both point-based flow data (such as taxi trips with GPS locations) and area-based flow data (such as county-to-county migration). Moreover, the approach can be used to detect and compare flow patterns at different scales or in relatively sparse flow datasets, such as migration for each age group. We evaluate and demonstrate the new approach with case studies of U.S. migration data and experiments with synthetic data. [ABSTRACT FROM PUBLISHER]

High-resolution satellite imagery is considered an excellent candidate for extracting information about the human activities on Earth. The information about residential development and suburban area mapping is of interest that can be obtained from these images. Shadow of structures such as man-made buildings is one of the main cues for structure detection in panchromatic high-resolution satellite imagery. However, to correctly exploit the information of the shadow in an image, the shadow needs to be detected and isolated first. In this paper, we propose a new algorithm for shadow detection and isolation of buildings in high-resolution panchromatic satellite imagery. The proposed algorithm is based on tailoring the traditional model of the geometric active contours such that the new model of the contours is systematically biased toward segmenting the shadow and the dark regions in the image. The systematic biasing in the proposed contour model is accomplished by novel encoding of the radiometric characteristics of the shadows regions. After detecting and segmenting the shadow and the dark regions in the image, further processing steps are introduced. The proposed postprocessing is based on selection of optimal threshold and a boundary complexity metric to distinguish the true shadows from the clutter. Experimental results are presented to validate the performance of the proposed algorithm on real high-resolution panchromatic satellite images. [ABSTRACT FROM PUBLISHER]

This paper presents the design, analysis, and experimental validation of a globally asymptotically stable (GAS) filter for simultaneous localization and mapping (SLAM), with application to unmanned aerial vehicles. The SLAM problem is formulated in a sensor-based framework and modified in such a way that the underlying system structure can be regarded as linear time varying for observability analysis and filter design purposes, from which a linear Kalman filter with GAS error dynamics follows naturally. The performance and consistency validation of the proposed sensor-based SLAM filter are successfully assessed with real data, acquired indoors, using an instrumented quadrotor. [ABSTRACT FROM PUBLISHER]

In this paper, we propose to combine two road extractors from very high resolution synthetic aperture radar scenes: one more successful in rural areas and one explicitly designed for urban areas. In order to get the best combination of both, a rapid mapping filter for discriminating rural and urban scenes is utilized. Finally, the results are fused on a feature level and connected by means of a network optimization. The approach is tested and evaluated on TerraSAR-X data containing complex urban areas and urban-rural fringe scenes. [ABSTRACT FROM AUTHOR]

The mapping of urban areas at regional to global scales is a crucial task due to its value for environmental monitoring, habitat and biodiversity conservation, and decision-making. In most current applications, two techniques (i.e., supervised classification and data fusion) are widely applied in large-scale urban mapping. However, the costly training sample collection, inadequate data-source descriptions, and diverse urban characteristics (e.g., shape, size, socioeconomic status, and physical environment) are challenging problems for the urban mapping approaches. In this context, aiming at effectively deriving accurate urban areas at a large scale, we propose a novel ensemble support vector machine (SVM) method which consists of three steps: 1) the automatic generation of training data to reduce labor costs; 2) the construction of an ensemble SVM model to effectively combine the multisource data (including remote sensing and socioeconomic data); and 3) an adaptive patch-based thresholding technique to tackle the diverse urban characteristics. The proposed method is employed to map urban areas of China in 2005 and 2010, and the resulting maps are compared with the existing urban maps for 287 prefecture-level cities. It is found that our results present a satisfactory superiority, especially in challenging small cities, with a significant improvement in median Kappa (0.174 for 2005 and 0.203 for 2010). When incorporating moderate-resolution imaging spectroradiometer multispectral data as an additional source, the Kappa coefficient can be further raised by 0.028 for 2010. In general, the proposed method shows great potential for accurately mapping urban areas at regional, continental, or even global scales in a cost-effective manner. [ABSTRACT FROM AUTHOR]

Facial landmark detection is typically cast as a point-wise regression problem that focuses on how to build an effective image-to-point mapping function. In this paper, we propose an end-to-end deep learning approach for contextually discriminative feature construction together with effective facial structure modeling. The proposed learning approach is able to predict more contextually discriminative facial landmarks by capturing their associated contextual information. Moreover, we present a tree model to characterize human face structure and a structural loss function to measure the deformation cost between the ground-truth and predicted tree model, which are further incorporated into the proposed learning approach and jointly optimized within a unified framework. The presented tree model is able to well characterize the spatial layout patterns of facial landmarks for capturing the facial structure information. Experimental results demonstrate the effectiveness of the proposed approach against the state-of-the-art over the MTFL and AFLW-full data sets. [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 introduces a new computational visual-attention model for static and dynamic saliency maps. First, we use the Earth Mover's Distance (EMD) to measure the center-surround difference in the receptive field, instead of using the Difference-of-Gaussian filter that is widely used in many previous visual-attention models. Second, we propose to take two steps of biologically inspired nonlinear operations for combining different features: combining subsets of basic features into a set of super features using the L^m-norm and then combining the super features using the Winner-Take-All mechanism. Third, we extend the proposed model to construct dynamic saliency maps from videos by using EMD for computing the center-surround difference in the spatiotemporal receptive field. We evaluate the performance of the proposed model on both static image data and video data. Comparison results show that the proposed model outperforms several existing models under a unified evaluation setting. [ABSTRACT FROM AUTHOR]

This paper compares ALOS PALSAR fully polarimetric and dual-polarized data in the application area of land cover mapping. To assure versatile comparison of the data, different classification methods and different features of data are used. Two of the classification methods used are based on supervised classification and two on unsupervised classification. Polarimetric data are used in three ways: 1) as fully polarimetric data; 2) features calculated from fully polarimetric data; and 3) intensity data of selected channels. Combinations of six (water, field, sparse forest, dense forest, peat land, and urban areas), five, four, and three classes were used for classification. Fully polarimetric data gave better results (87.5%–84.7% with three classes; open land areas, forest, and water) than intensity data only (83.6%–78.6%), but the differences in the overall accuracies between the methods were not more than 7.6%. Kappa coefficients of agreement are moderate for all the classifications. Supervised classification can be expected to perform better than unsupervised classification, given that the training areas can be selected accurately. Dual polarization data were found to be an attractive alternative in cases where fully polarimetric data are not available or it is of low resolution. With intensities of selected polarimetric features, it was possible to obtain a high classification accuracy as with fully polarimetric data. This also opens possibilities for nonspecialist users to benefit from polarimetric information in classification. [ABSTRACT FROM AUTHOR]

This paper, which deals with the stability of adaptive observers for induction motor in the regenerating mode, proposes a new approach that is consist of describing the error system in state space representation. With this formulation, it is possible to establish a cartography of unstable eigenvalues in the torque/speed plane, thus simplifying the stability analysis. Moreover, a new stability criterion is defined and used to realize stabilizing designs based on feedback gain as well as on the speed adaptation law. [ABSTRACT FROM AUTHOR]

This paper develops a Square Root Unscented Kalman Filter (SRUKF) for performing video-rate visual simultaneous localization and mapping (SLAM) using a single camera. The conventional UKF has been proposed previously for SLAM, improving the handling of nonlinearities compared with the more widely used Extended Kalman Filter (EKF). However, no account was taken of the comparative complexity of the algorithms: In SLAM, the UKF scales as O(N³) in the state length, compared to the EKF's O(N²), making it unsuitable for video-rate applications with other than unrealistically few scene points. Here, it is shown that the SRUKF provides the same results as the UKF to within machine accuracy and that it can be reposed with complexity O(N²) for state estimation in visual SLAM. This paper presents results from video-rate experiments on live imagery. Trials using synthesized data show that the consistency of the SRUKF is routinely better than that of the EKF, but that its overall cost settles at an order of magnitude greater than the EKF for large scenes. [ABSTRACT FROM AUTHOR]

Aerial photography obtained by unmanned aerial vehicles (UAVs) is a rising market for their civil application. Small UAVs are believed to close gaps in niche markets, such as acquiring airborne image data for remote sensing purposes. Small UAVs can fly at low altitudes, in dangerous environments, and over long periods of time. However, their small lightweight construction leads to new problems, such as higher agility and more susceptibility to turbulence, which has a big impact on the quality of the data and their suitability for aerial photography. This paper investigates the use of fish-eye lenses to overcome field-of-view (FOV) issues for highly agile UAV platforms susceptible to turbulence. The fish-eye lens has the benefit of a large observation area (large FOV) and does not add additional weight to the aircraft, such as traditional mechanical stabilizing systems. We present the implementation of a fish-eye lens for aerial photography and mapping purposes, with potential use in remote sensing applications. We describe a detailed investigation from the fish-eye lens distortion to the registering of the images. Results of the process are presented using low-quality sensors typically found on small UAVs. The system was flown on a midsize platform (a more stable Cessna aircraft) and also on ARCAA's small (< 10 kg) UAV platform. The effectiveness of the approach is compared for the two sized platforms. [ABSTRACT FROM AUTHOR]

Sign-selection uses a set of subcarrier signs to reduce the peak-to-average power ratio (PAR) of orthogonal-frequency-division multiplexing (OFDM). However, the computational complexity (worst-case) is exponential in N, the number of subcarriers. Suboptimal sign-selection algorithms, achieving different tradeoffs between the PAR reduction and complexity, have thus been developed. For example, the derandomization method achieves high PAR reduction of σ(log N) with relatively high complexity of σ(N2). On the other hand, selective mapping (SLM) and partial transmit sequences (PTS) sacrifice the achievable PAR reduction for lower complexity. In this paper, we develop two new cross-entropy (CE)-based sign-selection algorithms. Our algorithms simultaneously updates the probabilities of the signs of all subcarriers. As shown in Section II-D, the first algorithm obtains a PAR lower than the above methods with a complexity level of σ(N2). However, if the number of iterations is fixed, this algorithm obtains the same PAR reduction as derandomization, but with σ(N log N) complexity. Practical PAR reduction algorithms require that the extra cost of PAR reduction must be small. Therefore, we propose the second algorithm, which adaptively adjusts the probability of "elite" samples, and stops whenever a PAR threshold is reached. Our second algorithm achieves up to 95% complexity savings over the first (with only a 0.4-dB PAR reduction loss). The simulations confirm the complexity advantages of the proposed algorithms compared to SLM and derandomization. [ABSTRACT FROM AUTHOR]

Random fields are useful models of spatially variable quantities, such as those occurring in environmental processes and medical imaging. The fluctuations obtained in most natural data sets are typically anisotropic. The parameters of anisotropy are often determined from the data by means of empirical methods or the computationally expensive method of maximum likelihood. In this paper, we propose a systematic method for the identification of geometric (elliptic) anisotropy parameters of scalar fields. The proposed method is computationally efficient, nonparametric, noniterative, and it applies to differentiable random fields with normal or lognormal probability density functions. Our approach uses sample-based estimates of the random field spatial derivatives that we relate through closed form expressions to the anisotropy parameters. This paper focuses on two spatial dimensions. We investigate the performance of the method on synthetic samples with Gaussian and Matérn correlations, both on regular and irregular lattices. The systematic anisotropy detection provides an important preprocessing stage of the data. Knowledge of the anisotropy parameters, followed by suitable rotation and rescaling transformations restores isotropy thus allowing classical interpolation and signal processing methods to be applied. [ABSTRACT FROM AUTHOR]

Mapping high-dimensional data in a low-dimensional space, for example, for visualization, is a problem of increasingly major concern in data analysis. This paper presents data-driven high-dimensional scaling (DD-HDS), a nonlinear mapping method that follows the line of multidimensional scaling (MDS) approach, based on the preservation of distances between pairs of data. It improves the performance of existing competitors with respect to the representation of high-dimensional data, in two ways. It introduces 1) a specific weighting of distances between data taking into account the concentration of measure phenomenon and 2) a symmetric handling of short distances in the original and output spaces, avoiding false neighbor representations while still allowing some necessary tears in the original distribution. More precisely, the weighting is set according to the effective distribution of distances in the data set, with the exception of a single user-defined parameter setting the tradeoff between local neighborhood preservation and global mapping. The optimization of the stress criterion designed for the mapping is realized by "force-directed placement" (FDP). The mappings of low- and high-dimensional data sets are presented as illustrations of the features and advantages of the proposed algorithm. The weighting function specific to high-dimensional data and the symmetric handling of short distances can be easily incorporated in most distance preservation-based nonlinear dimensionality reduction methods. [ABSTRACT FROM AUTHOR]

This paper describes how multisource satellite data and efficient image analysis may successfully be used to conduct rapid-mapping tasks in the domain of disaster and crisis- management support. The German Aerospace Center (DLR) has set up a dedicated crosscutting service, which is the so-called "Center for satellite-based Crisis Information" (ZKI), to facilitate the use of its Earth-observation capacities in the service of national and international response to major disaster situations, humanitarian relief efforts, and civil security issues. This paper describes successful rapid satellite mapping campaigns supporting disaster relief and demonstrates how this technology can be used for civilian crisis-management purposes. During the last years, various international coordination bodies were established, improving the disaster-response-related cooperation within the Earth-observation community worldwide. DLR/ZKI operates in this context, closely networking with public authorities (civil security), nongovernmental organizations (humanitarian relief organizations), satellite operators, and other space agencies. This paper reflects on several of these international activities, such as the International Charter Space and Major Disasters, describes mapping procedures, and reports on rapid-mapping experiences gained during various disaster-response applications. The example cases presented cover rapid impact assessment after the Indian Ocean Tsunami, forest fires mapping for Portugal, earthquake-damage assessment for Pakistan, and landslide extent mapping for the Philippines. [ABSTRACT FROM AUTHOR]

In this paper, we present a graph-based approach for mining geospatial data. The system uses error-tolerant graph matching to find correspondences between the detected image features and the geospatial vector data. Spatial relations between objects are used to find a reliable object-to-object mapping. Graph matching is used as a flexible query mechanism to answer the spatial query. A condition based on the expected graph error has been presented which allows determining the bounds of error tolerance and, in this way, characterizes the relevancy of a query solution. We show that the number of null labels is an important measure to determine relevancy. To be able to correctly interpret the matching results in terms of relevancy, the derived bounds of error tolerance are essential. [ABSTRACT FROM AUTHOR]

This paper presents an autonomous terrain navigation system for a mobile robot. The system employs a two-dimensional laser range finder (LRF) for terrain mapping. A so-called ‘traversability field histogram’ (TFH) method is proposed to guide the robot. The TFH method first transforms a local terrain map surrounding the robot's momentary position into a traversability map by extracting the slope and roughness of a terrain patch through least-squares plane fitting. It then computes a so-called ‘polar traversability index’ (PTI) that represents the overall difficulty of traveling along the corresponding direction. The PTIs are represented in a form of histogram. Based on this histogram, the velocity and steering commands of the robot are determined. The concept of a virtual valley and an exit condition are proposed and used to direct the robot such that it can reach the target with a finite-length path. The algorithm is verified by simulation and experimental results. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of estimating underwater vehicle trajectories using gyro-Doppler (body-fixed velocities) and acoustic positioning signals (earth-fixed positions). The approach consists of diffusion-based observers processing a whole trajectory segment at a time, allowing the consideration of important practical problems such as different information update rates, outages, and outliers in a very simple framework. Results of contraction theory are used to prove that the observers are convergent, i.e., stable in the incremental sense. Simulation and experimental results are presented to illustrate the potential of application of the method. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of contour lines extraction from scanned topographic maps. A novel method called "local window segmentation" is developed and used to overcome the difficulty of the gaps and thick lines. Experiments and evaluations on a number of well- and common-conditioned topographic maps are provided. [ABSTRACT FROM AUTHOR]

Massive maps have been shared as Web Map Service (WMS) from various providers, which could be used to facilitate people's daily lives and support space analysis and management. The theme classification of maps could help users efficiently find maps and support theme-related applications. Traditionally, metadata is usually used in analyzing maps content, few papers use maps, especially legends. In fact, people usually considers metadata, maps and legends together to understand what maps tell, however, no study has tried to exploit how to combine them. This paper proposes a method to fuse them with the purpose of classifying map themes, named latent feature based multimodality fusion for theme classification (LFMF-TC). Firstly, a multimodal dataset is created that supports the supervised classification on map themes. Secondly, textual and visual features are designed for metadata, maps, and legends using some advanced techniques. Thirdly, a latent feature based fusion method is proposed to fuse the multimodal features on the feature level. Finally, a neural network classifier is implemented using supervised learning on the multimodal dataset. In addition, a web-based collaboration platform is developed to facilitate users in labeling multimodal samples through an interactive Graphical User Interface (GUI). Extensive experiments are designed and implemented, whose results prove that LFMF-TC could significantly improve the classification accuracy. In theory, the LFMF-TC could be used for other applications with few modifications.

Navigation, mapping, and tracking are state estimation problems relevant to a wide range of applications. These problems have traditionally been formulated using random vectors in stochastic filtering, smoothing, or optimization-based approaches. Alternatively, the problems can be formulated using random finite sets, which offer a more robust solution in poor detection conditions (i.e., low probabilities of detection, and high clutter intensity). This paper mathematically shows that the two estimation frameworks are related, and equivalences can be determined under a set of ideal detection conditions. The findings provide important insights into some of the limitations of each approach. These are validated using simulations with varying detection statistics, along with a real experimental dataset. [ABSTRACT FROM PUBLISHER]

Proposals to establish a 'science of interaction' have been forwarded from Information Visualization and Visual Analytics, as well as Cartography, Geovisualization, and GIScience. This paper reports on two studies to contribute to this call for an interaction science, with the goal of developing a functional taxonomy of interaction primitives for map-based visualization. A semi-structured interview study first was conducted with 21 expert interactive map users to understand the way in which map-based visualizations currently are employed. The interviews were transcribed and coded to identify statements representative of either the task the user wished to accomplish (i.e., objective primitives) or the interactive functionality included in the visualization to achieve this task (i.e., operator primitives). A card sorting study then was conducted with 15 expert interactive map designers to organize these example statements into logical structures based on their experience translating client requests into interaction designs. Example statements were supplemented with primitive definitions in the literature and were separated into two sorting exercises: objectives and operators. The objective sort suggested five objectives that increase in cognitive sophistication (identify, compare, rank, associate, & delineate), but exhibited a large amount of variation across participants due to consideration of broader user goals (procure, predict, & prescribe) and interaction operands (space-alone, attributes-in-space, & space-in-time; elementary & general). The operator sort suggested five enabling operators (import, export, save, edit, & annotate) and twelve work operators (reexpress, arrange, sequence, resymbolize, overlay, pan, zoom, reproject, search, filter, retrieve, & calculate). This taxonomy offers an empirically-derived and ecologically-valid structure to inform future research and design on interaction. [ABSTRACT FROM AUTHOR]

Modern embedded systems are becoming increasingly multifunctional. The dynamism in multifunctional embedded systems manifests itself with more dynamic applications and the presence of multiple applications executing on a single embedded system. This dynamism in the application workload must be taken into account during the early system-level design space exploration (DSE) of multiprocessor system-on-a-chip (MPSoC)-based embedded systems. Scenario-based DSE utilizes the concept of application scenarios to search for optimal mappings of a multi-application workload onto an MPSoC. The scenario-based DSE uses a multi-objective genetic algorithm (GA) to identifying the mapping with the best average quality for all the application scenarios in the workload. In order to keep the exploration of the scenario-based DSE efficient, fitness prediction is used to obtain the quality of a mapping. This fitness prediction is performed using a representative subset of application scenarios that is obtained using co-exploration of the scenario subset space. In this paper, multiple fitness prediction techniques are presented: stochastic, deterministic, and a hybrid combination. Results show that, for our test cases, accurate fitness prediction is already provided for subsets containing only 1–4% of the application scenarios. Larger subsets will obtain a similar accuracy, but the DSE will require more time to identify promising mappings that meet the requirements of multifunctional embedded systems. [ABSTRACT FROM AUTHOR]

This paper presents a new subpixel mapping method based on subpixel attraction-repulsion. The proposed method is formulated as an optimization problem with respect to attraction-repulsion among subpixels and is used to reconstruct a finer spatial resolution image from a lower resolution one. A comprehensive experiment is conducted to demonstrate the performance of the proposed method, by comparing it with the other three existing subpixel mapping methods, i.e., linear optimization, pixel swapping and spatial attraction model methods. In the experiment, both a synthetic image with known fractional abundances and an EO-1 Hyperion hyperspectral image of Shanghai were used to evaluate performances of the subpixel mapping methods. The experimental result shows that by using spatial dependence with attraction between the same types of ground objects and repulsion between different types of these objects, the proposed subpixel mapping method achieves a better performance on subpixel mapping than the other three methods. [ABSTRACT FROM PUBLISHER]

This paper studies the problem of vision-based simultaneous localization and mapping (SLAM) in dynamic environments with multiple cameras. These cameras move independently and can be mounted on different platforms. All cameras work together to build a global map, including 3D positions of static background points and trajectories of moving foreground points. We introduce intercamera pose estimation and intercamera mapping to deal with dynamic objects in the localization and mapping process. To further enhance the system robustness, we maintain the position uncertainty of each map point. To facilitate intercamera operations, we cluster cameras into groups according to their view overlap, and manage the split and merge of camera groups in real time. Experimental results demonstrate that our system can work robustly in highly dynamic environments and produce more accurate results in static environments. [ABSTRACT FROM AUTHOR]

Separability in a code is crucial in guaranteeing a decent Hamming-distance separation among the codewords. In multibit biometric discretization where a code is used for quantization-intervals labeling, separability is necessary for preserving distance dissimilarity when feature components are mapped from a discrete space to a Hamming space. In this paper, we examine separability of Binary Reflected Gray Code (BRGC) encoding and reveal its inadequacy in tackling interclass variation during the discrete-to-binary mapping, leading to a tradeoff between classification performance and entropy of binary output. To overcome this drawback, we put forward two encoding schemes exhibiting full-ideal and near-ideal separability capabilities, known as Linearly Separable Subcode (LSSC) and Partially Linearly Separable Subcode (PLSSC), respectively. These encoding schemes convert the conventional entropy-performance tradeoff into an entropy-redundancy tradeoff in the increase of code length. Extensive experimental results vindicate the superiority of our schemes over the existing encoding schemes in discretization performance. This opens up possibilities of achieving much greater classification performance with high output entropy. [ABSTRACT FROM AUTHOR]

This paper proposes a novel change-detection-driven transfer learning (TL) approach to update land-cover maps by classifying remote-sensing images acquired on the same area at different times (i.e., image time series). The proposed approach requires that a reliable training set is available only for one of the images (i.e., the source domain) in the time series whereas it is not for another image to be classified (i.e., the target domain). Unlike other literature TL methods, no additional assumptions on either the similarity between class distributions or the presence of the same set of land-cover classes in the two domains are required. The proposed method aims at defining a reliable training set for the target domain, taking advantage of the already available knowledge on the source domain. This is done by applying an unsupervised-change-detection method to target and source domains and transferring class labels of detected unchanged training samples from the source to the target domain to initialize the target-domain training set. The training set is then optimized by a properly defined novel active learning (AL) procedure. At the early iterations of AL, priority in labeling is given to samples detected as being changed, whereas in the remaining ones, the most informative samples are selected from changed and unchanged unlabeled samples. Finally, the target image is classified. Experimental results show that transferring the class labels from the source domain to the target domain provides a reliable initial training set and that the priority rule for AL results in a fast convergence to the desired accuracy with respect to Standard AL. [ABSTRACT FROM AUTHOR]