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  • Articles  (2,719)
  • 1
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2018-03-13
    Description: Provides a listing of current staff, committee members and society officers.
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    Topics: Geosciences
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  • 2
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2018-03-13
    Description: Presents the table of contents for this issue of the periodical.
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    Topics: Geosciences
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  • 3
    Publication Date: 2018-03-13
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    Topics: Geosciences
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  • 4
    Publication Date: 2018-03-13
    Description: Very high resolution optical remote sensing images (RSI) are often corrupted by noise. Among popular denoising methods in the state of the art, nonlocal Bayes (NLB) has led to successful results on real datasets, with high quality and reasonable computation time. However, its computation time remains prohibitive with respect to requirements of operational RSI pipelines, such as Pléiades one. In this paper, we tackle such an issue and introduce several optimizations aiming to significantly reduce the computation time required by NLB while keeping the best denoising quality (i.e., preserving edges, textures, and homogeneous areas). More precisely, our improvements consist of reducing multiple estimations of a same pixel with a masking technique and modifying the spatial extent of the similar patch search area (i.e., one of the main parts of nonlocal algorithms, such as NLB). We report several experiments and discuss optimal settings for these parameters, allowing a gain in computation time of 50% (resp. 15%) with optimized masking strategy (resp. spatial extent of the search area). When both contributions are combined, we achieve the same denoising quality as standard NLB while doubling the computation efficiency, the latter being increased fivefold if we accept a very small (lower than 0.1%) loss in quality.
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    Topics: Geosciences
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  • 5
    Publication Date: 2018-03-13
    Description: Compared with color or grayscale images, hyperspectral images deliver more informative representation of ground objects and enhance the performance of many recognition and classification applications. However, hyperspectral images are normally corrupted by various types of noises, which have a serious impact on the subsequent image processing tasks. In this paper, we propose a novel hyperspectral image denoising method based on tucker decomposition to model the nonlocal similarity across the spatial domain and global similarity along the spectral domain. In this method, 3-D full band patches extracted from a hyperspectral image are grouped to form a third-order tensor by utilizing the nonlocal similarity in a proper window size. In this way, the task of image denoising is transformed into a high-order tensor approximation problem, which can be solved by nonnegative tucker decomposition. Instead of a traditional alternative least square based tucker decomposition, we propose a hierarchical least square based nonnegative tucker decomposition method to reduce the computational cost and improve the denoising effect. In addition, an iterative denoising strategy is adopted to achieve better denoising outcome in practice. Experimental results on three datasets show that the proposed method outperforms several state-of-the-art methods and significantly enhances the quality of the corrupted hyperspectral image.
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  • 6
    Publication Date: 2018-03-13
    Description: Hyperspectral images (HSIs) are usually contaminated by various kinds of noise, such as stripes, deadlines, impulse noise, Gaussian noise, and so on, which significantly limits their subsequent application. In this paper, we model the stripes, deadlines, and impulse noise as sparse noise, and propose a unified mixed Gaussian noise and sparse noise removal framework named spatial–spectral total variation regularized local low-rank matrix recovery (LLRSSTV). The HSI is first divided into local overlapping patches, and rank-constrained low-rank matrix recovery is adopted to effectively separate the low-rank clean HSI patches from the sparse noise. Differing from the previous low-rank-based HSI denoising approaches, which process all the patches individually, a global spatial–spectral total variation regularized image reconstruction strategy is utilized to ensure the global spatial–spectral smoothness of the reconstructed image from the low-rank patches. In return, the globally reconstructed HSI further promotes the separation of the local low-rank components from the sparse noise. An augmented Lagrange multiplier method is adopted to solve the proposed LLRSSTV model, which simultaneously explores both the local low-rank property and the global spatial–spectral smoothness of the HSI. Both simulated and real HSI experiments were conducted to illustrate the advantage of the proposed method in HSI denoising, from visual/quantitative evaluations and time cost.
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  • 7
    Publication Date: 2018-03-13
    Description: Satellite imagery often exhibits large spatial extent areas that encompass object classes with considerable variability. This often limits large-scale model generalization with machine learning algorithms. Notably, acquisition conditions, including dates, sensor position, lighting condition, and sensor types, often translate into class distribution shifts introducing complex nonlinear factors and hamper the potential impact of machine learning classifiers. This paper investigates the challenge of exploiting satellite images using convolutional neural networks (CNN) for settlement classification where the class distribution shifts are significant. We present a large-scale human settlement mapping workflow based-off multiple modules to adapt a pretrained CNN to address the negative impact of distribution shift on classification performance. To extend a locally trained classifier onto large spatial extents areas we introduce several submodules: First, a human-in-the-loop element for relabeling of misclassified target domain samples to generate representative examples for model adaptation; second, an efficient hashing module to minimize redundancy and noisy samples from the mass-selected examples; and third, a novel relevance ranking module to minimize the dominance of source example on the target domain. The workflow presents a novel and practical approach to achieve large-scale domain adaptation with binary classifiers that are based-off CNN features. Experimental evaluations are conducted on areas of interest that encompass various image characteristics, including multisensors, multitemporal, and multiangular conditions. Domain adaptation is assessed on source–target pairs through the transfer loss and transfer ratio metrics to illustrate the utility of the workflow.
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  • 8
    Publication Date: 2018-03-13
    Description: A novel method that combines joint clusters and iterative graph cuts for ALS point cloud filtering is proposed in this paper. The method first extracts clusters of points from the raw point cloud, and then classifies ground points at the cluster level. There are four main steps, i.e., two-step point cloud clustering, critical point extraction, initial terrain determination, and terrain densification based on iterative graph cuts. Smooth clusters, rough clusters, and scattered points are extracted by the two-step clustering to depict the raw point cloud, which reduces the complexity of raw data and the judgment difficulty in the subsequent procedures. Critical points of each cluster are extracted, and the initial terrain is determined among the smooth clusters. Using the initial terrain and critical points, iterative graph cuts is performed to segment ground and nonground points at the cluster level. Experiments on ISPRS dataset with a low point density and Utah dataset with a moderate point density show that our approach provides a satisfactory trade off between Type I and Type II errors. Moreover, our method significantly outperforms progressive TIN densification based filters, and successfully controls Type II errors.
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  • 9
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2018-03-13
    Description: Provides instructions and guidelines to prospective authors who wish to submit manuscripts.
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    Topics: Geosciences
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  • 10
    Publication Date: 2018-03-13
    Description: We propose a high-resolution velocity analysis method to estimate the electromagnetic wave propagation velocity in subsurface medium. The estimation is achieved by applying the ℓ-1 norm regularized least-squares method to the conventional common-midpoint (CMP) velocity analysis algorithm. The proposed method can provide not only higher resolution than the conventional velocity analysis method, but can also be applied with a coarse sampling array system, such as our array ground penetrating radar YAKUMO, which returns eight CMP traces within a two meter width. The main purpose of this approach is for precise pavement inspection at shallow depths. We applied this method to both a simulated dataset and real data acquired by YAKUMO at a model airport taxiway to detect the slight velocity changes caused by millimeter-thin cracks filled with air or water within the 15 cm-thick asphalt pavement. In both cases slight velocity changes of about 0.005 m/ns can be detected, and the difference between air- and water-filled cracks can be distinguished. Also, this method is applied to a data acquired at airport taxi-way, the damaged parts are detected successfully and shows good agreement with the corning results. The results indicate that the proposed method is effective for pavement inspection, especially in the presence of thin cracks that cannot be seen directly with the reflected signal.
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