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  • 1
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2018-03-28
    Description: Presents the cover/table of contents for this issue of the periodical.
    Print ISSN: 1545-598X
    Electronic ISSN: 1558-0571
    Topics: Architecture, Civil Engineering, Surveying , Geography , Geosciences
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  • 2
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2018-03-28
    Description: Hyperspectral images in remote sensing systems with rich spatial and spectral information provide an opportunity for researchers to discover the world. Anomaly detection is one of the most interesting topics over the last two decades in hyperspectral imagery (HSI). In this letter, we propose a modified collaborative-representation-based with outlier removal anomaly detector (CRBORAD) for anomaly detection. We use both spectral and spatial information for detecting anomalies since that is more precise than using only spectral information. The proposed detector can adaptively estimate the background by its adjacent pixels within a sliding dual-window. We remove outlier pixels that are significantly different from majority of pixels, before estimating background pixels. It can lead us to precise detection of anomalies in subsequent stages. By subtracting the predicted background from the original HSI, the residual image is resulted and anomalies can be determined, finally. Kernel extension of the proposed approach is also presented. CRBORAD results on San Diego airport and the Rochester Institute of Technology data are illustrated using intuitive images, receiver operating characteristic curves, and area under curve values. The results are compared with four popular and previous methods and prove the superiority of the proposed CRBORAD method.
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  • 3
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2018-03-28
    Description: In this letter, we propose a superpixel-level target detection approach for synthetic aperture radar (SAR) images. With superpixel segmentation, SAR image is divided into meaningful patches and more statistical information can be provided in superpixels compared with single pixels. The statistical difference between target and clutter superpixels can be measured with the intensity distributions of pixels in them. With the assumption of SAR data obeying Gamma distribution, the superpixel dissimilarity is defined. With this basis, the global and local contrast can be obtained and integrated to enhance target and suppress clutter simultaneously. Thus, better target detection performance can be achieved. Different from traditional target detection schemes based on backscattering difference between target and clutter pixels, the proposed method relies on the statistical difference of superpixels. The effectiveness of the proposed method can be demonstrated with experimental results on real SAR images.
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  • 4
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2018-03-28
    Description: This letter presents a band selection method relying on saliency bands and scale selection (SBSS). The SBSS method is used to excavate the hidden information of hyperspectral images effectively, while its underlying assumptions are: 1) it is reasonable to combine spectral and spatial information to excavate the intrinsic property of a hyperspectral image; 2) there are some saliency bands that can represent a hyperspectral image without significant information loss in data exploitation; and 3) saliency, scale, and image description have an intrinsic connection. The computational complexity of the SBSS method is linear, and experimental results demonstrate that the proposed method obtains competitively good results compared with other state-of-the-art band selection techniques, in terms of classification accuracy.
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  • 5
    Publication Date: 2018-03-28
    Description: Recently, collaborative representation has received much attention in the hyperspectral image (HSI) classification due to its simplicity and effectiveness. However, the existing collaborative representation-based HSI classification methods ignore the correlation among different classes. To overcome this problem, we propose a discriminative kernel collaborative representation and Tikhonov regularization method (DKCRT) for HSI classification, which can make the kernel collaborative representation of different classes to be more discriminative. Specifically, the kernel trick is adopted to map the original HSI into a high space to improve the class separability. Besides, distance-weighted kernel Tikhonov regularization is adopted to enforce these training samples to have large representation coefficients, which are similar to the test sample in the high-dimensional feature space. Moreover, we add a discriminative regularization term to further enhance the separability of different classes, which can take the correlation among different classes into consideration. Furthermore, to take the spatial information of HSI into consideration, we extend the DKCRT to a joint version named JDKCRT. Experiments on real HSIs demonstrate the efficiency of the proposed DKCRT and JDKCRT.
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  • 6
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2018-03-28
    Description: Water-body segmentation is an important issue in remote sensing and image interpretation. Classic methods for counteracting this problem usually include the construction of index features by combining different spectra, however, these methods are essentially rule-based and fail to take advantage of context information. Additionally, as the quality of image resolution improves, these methods are proved to be inadequate. With the rise of convolutional neural networks (CNN), the level of research about segmentation has taken a huge leap, but the field is still facing an increasing demand for data and the problem of blurring boundaries. In this letter, a new segmentation network called restricted receptive field deconvolution network (RRF DeconvNet) is proposed, with which to extract water bodies from high-resolution remote sensing images. Compared with natural images, remote sensing images have a weaker pixel neighborhood relativity; in consideration of this challenge, an RRF DeconvNet compresses the redundant layers in the original DeconvNet and no longer relies on a pretrained model. In addition, to tackle the blurring boundaries that occur in CNN, a new loss function called edges weighting loss is proposed to train segmentation networks, which has been shown to significantly sharpen the segmentation boundaries in results. Experiments, based on Google Earth images for water-body segmentation, are presented in this letter to prove our method.
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  • 7
    Publication Date: 2018-03-28
    Description: The combination of linear range walk correction and keystone transform is a good choice to focus high-resolution highly squint synthetic aperture radar (SAR) data because it is an effective way to remove linear range cell migration (RCM) completely and mitigate range–azimuth coupling. However, the results of this kind of imaging algorithm produce 2-D-variant residual RCM and variant-dependence Doppler phases. To obtain high-quality SAR image, an improved imaging algorithm using an azimuth-variant residual RCM correction (RCMC) and an extended nonlinear chirp scaling (ENLCS) is proposed in this letter. A new circle model is constructed to analyze the azimuth-variant properties of the residual high-order RCM and the Doppler phases. Based on this circle model, an azimuth-variant residual RCMC is implemented by multiplying a fourth-order phase function, and an improved ENLCS is derived to accomplish the azimuth equalization for azimuth compression. Simulation results validate the excellent performance of the proposed algorithm.
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  • 8
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2018-03-28
    Description: Today, very dense synthetic aperture radar (SAR) time series are available through the framework of the European Copernicus Programme. These time series require innovative processing and preprocessing approaches including novel speckle suppression algorithms. Here we propose an image transform for hypertemporal SAR image time stacks. This proposed image transform relies on the temporal patterns only, and therefore fully preserves the spatial resolution. Specifically, we explore the potential of empirical mode decomposition (EMD), a data-driven approach to decompose the temporal signal into components of different frequencies. Based on the assumption that the high-frequency components are corresponding to speckle, these effects can be isolated and removed. We assessed the speckle filtering performance of the transform using hypertemporal Sentinel-1 data acquired over central Germany comprising 53 scenes. We investigated speckle suppression, ratio images, and edge preservation. For the latter, a novel approach was developed. Our findings suggest that EMD features speckle suppression capabilities similar to that of the Quegan filter while preserving the original image resolution.
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  • 9
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2018-03-28
    Description: This letter presents an analysis of the temporal characteristics of electromagnetic waves scattered from a time-varying reservoir surface at low grazing angles. The data collection campaigns were conducted using a polarimetric S-band radar at Wachusett Reservoir in MA, USA, and VV and HH polarized radar returns were simultaneously captured. The temporal behavior of the backscattering from the reservoir surface was analyzed for 180 distinct radar geometries, focusing in particular on the impact of polarization, radar geometry, and wind condition. To understand the shape of the Doppler spectrum, the power spectral density is estimated by a periodogram. In addition, decorrelation time, Doppler centroid, and variance are estimated and compared with the associated Doppler spectral width and peak Doppler frequency. Results show that Doppler spectral width, decorrelation time, and the standard deviation of Doppler spectra are correlated. In addition, the Doppler frequency shift induced by the motion of the water surface is analyzed by peak Doppler frequency and Doppler centroid, which show dependence on radar geometry and wind direction.
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  • 10
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    Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2018-03-28
    Description: The lack of proper class discrimination among the hyperspectral (HS) data points poses a potential challenge in HS classification. To address this issue, this letter proposes an optimal geometry-aware transformation for enhancing the classification accuracy. The underlying idea of this method is to obtain a linear projection matrix by solving a nonlinear objective function based on the intrinsic geometrical structure of the data. The objective function is constructed to quantify the discrimination between the points from dissimilar classes on the projected data space. Then, the obtained projection matrix is used to linearly map the data to more discriminative space. The effectiveness of the proposed transformation is illustrated with three benchmark real-world HS data sets. The experiments reveal that the classification and dimensionality reduction methods on the projected discriminative space outperform their counterpart in the original space.
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