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Journal Cover IEEE Geoscience and Remote Sensing Letters
  [SJR: 1.203]   [H-I: 60]   [143 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1545-598X
   Published by IEEE Homepage  [191 journals]
  • IEEE Geoscience and Remote Sensing Letters publication information
    • Abstract: Provides a listing of current staff, committee members and society officers.
      PubDate: Oct. 2017
      Issue No: Vol. 14, No. 10 (2017)
       
  • IEEE Geoscience and Remote Sensing Letters information for authors
    • Abstract: Provides instructions and guidelines to prospective authors who wish to submit manuscripts.
      PubDate: Oct. 2017
      Issue No: Vol. 14, No. 10 (2017)
       
  • IEEE Geoscience and Remote Sensing Letters Institutional Listings
    • Abstract: Advertisements.
      PubDate: Oct. 2017
      Issue No: Vol. 14, No. 10 (2017)
       
  • An Adaptive Strips Method for Extraction Buildings From Light Detection
           and Ranging Data
    • Authors: Xionggao Zou;Yueping Feng;Huiying Li;Jinlong Zhu;
      Pages: 1651 - 1655
      Abstract: A method is proposed for extracting building points set from light detecting and ranging (LiDAR) data. This proposed method is based on a strip strategy to filter building points and extract the edge point set rapidly and effectively in largescale urban building groups. This approach divides the LiDAR data into small strips and classifies each strip of data with an adaptive-weight polynomial in the x- or y-direction. The building edge set can then be extracted by utilizing the regional clustering relationships between points. The results of a series of experiments show that our method can not only filter the LiDAR point cloud, which performs better than existing methods, but also determine the building edge set efficiently, with an average accuracy rate of up to 91.1%.
      PubDate: Oct. 2017
      Issue No: Vol. 14, No. 10 (2017)
       
  • Structure Preserving Transfer Learning for Unsupervised Hyperspectral
           Image Classification
    • Authors: Jianzhe Lin;Chen He;Z. Jane Wang;Shuying Li;
      Pages: 1656 - 1660
      Abstract: Recent advances on remote sensing techniques allow easier access to imaging spectrometer data. Manually labeling and processing of such collected hyperspectral images (HSIs) with a vast quantities of samples and a large number of bands is labor and time consuming. To relieve these manual processes, machine learning based HSI processing methods have attracted increasing research attention. A major assumption in many machine learning problems is that the training and testing data are in the same feature space and follow the same distribution. However, this assumption doesn't always hold true in many real world problems, especially in certain HSI processing problems with extremely insufficient or even without training samples. In this letter, we present a transfer learning framework to address this unsupervised challenge (i.e., without training samples in the target domain), by making the following three main contributions: 1) to the best of our knowledge, this is the first time for transfer learning framework to be used for the classification of totally unknown target HSI data with no training samples; 2) the characteristics of HSI are learned on dual spaces to exploit its structure knowledge to better label HSI samples; and 3) two specific new scenarios suitable for transfer learning are investigated. Experimental results on several real world HSIs support the superiority of the proposed work.
      PubDate: Oct. 2017
      Issue No: Vol. 14, No. 10 (2017)
       
  • Spectrum Width Estimation Using Matched Autocorrelations
    • Authors: David A. Warde;Sebastian M. Torres;
      Pages: 1661 - 1664
      Abstract: The matched-autocorrelation spectrum-width estimator is introduced; statistics are derived and compared to those of the conventional estimator. It is demonstrated that the proposed estimator exhibits improved performance for narrow spectrum widths without increased computational complexity.
      PubDate: Oct. 2017
      Issue No: Vol. 14, No. 10 (2017)
       
  • Fully Convolutional Network With Task Partitioning for Inshore Ship
           Detection in Optical Remote Sensing Images
    • Authors: Haoning Lin;Zhenwei Shi;Zhengxia Zou;
      Pages: 1665 - 1669
      Abstract: Ship detection in optical remote sensing imagery has drawn much attention in recent years, especially with regards to the more challenging inshore ship detection. However, recent work on this subject relies heavily on hand-crafted features that require carefully tuned parameters and on complicated procedures. In this letter, we utilize a fully convolutional network (FCN) to tackle the problem of inshore ship detection and design a ship detection framework that possesses a more simplified procedure and a more robust performance. When tackling the ship detection problem with FCN, there are two major difficulties: 1) the long and thin shape of the ships and their arbitrary direction makes the objects extremely anisotropic and hard to be captured by network features and 2) ships can be closely docked side by side, which makes separating them difficult. Therefore, we implement a task partitioning model in the network, where layers at different depths are assigned different tasks. The deep layer in the network provides detection functionality and the shallow layer supplements with accurate localization. This approach mitigates the tradeoff of FCN between localization accuracy and feature representative ability, which is of importance in the detection of closely docked ships. The experiments demonstrate that this framework, with the advantages of FCN and the task partitioning model, provides robust and reliable inshore ship detection in complex contexts.
      PubDate: Oct. 2017
      Issue No: Vol. 14, No. 10 (2017)
       
  • Experimental Analysis of Small Drone Polarimetry Based on Micro-Doppler
           Signature
    • Authors: Byung Kwan Kim;Hyun-Seong Kang;Seong-Ook Park;
      Pages: 1670 - 1674
      Abstract: We present a polarimetric analysis of small drones from different aspect angles. Polarimetric analysis can provide more information of a target, since the returned radar signal is affected by different wave polarization. The analysis is performed with micro-Doppler signature (MDS) to investigate micromotions of the drone detected by the radar. We measured operating small drones in an anechoic chamber from two aspect angles, 0° and 90°. An outdoor experiment was carried out with metal clutters for verification in real environment. The indoor analysis result shows that copolarized antenna receives signals better than cross polarized when the aspect angle is 0°, and vice versa. We also verified that cross-polarized antenna receives MDS from the drone better than copolarized antenna, from outdoors when an aspect angle is almost 90°. By utilizing the polarimetric characteristic of the drone at this frequency band, it is preferable to use a polarimetric radar for drone detection.
      PubDate: Oct. 2017
      Issue No: Vol. 14, No. 10 (2017)
       
  • Range-Recursive IAA for Scanning Radar Angular Super-Resolution
    • Authors: Yongchao Zhang;Andreas Jakobsson;Jianyu Yang;
      Pages: 1675 - 1679
      Abstract: Recently, the iterative adaptive approach (IAA) was adopted to allow for the estimation of high-resolution scanning radar images. In this letter, we further develop this approach by introducing a range-recursive IAA (IAA-RR) formulation allowing for a computationally efficient updating of the resulting estimates along range. Besides exploiting the rich matrix structure to mitigate the computational complexity for each iteration, the correlation between adjacent range cells is exploited to accelerate the convergence of the IAA iterations. When an additional range measurement becomes available, further acceleration is available by exploiting the estimates already formed for the adjacent range cells. Compared with the existing fast IAA implementation, the proposed IAA-RR is shown to offer significant computational savings, without noticeable loss in performance. Numerical results illustrate the superior performance of the proposed IAA-RR algorithm.
      PubDate: Oct. 2017
      Issue No: Vol. 14, No. 10 (2017)
       
  • Remote Sensing Image Registration Based on Multifeature and Region
           Division
    • Authors: Wenping Ma;Yue Wu;Yafei Zheng;Zelian Wen;Liang Liu;
      Pages: 1680 - 1684
      Abstract: Although many feature-based registration methods have been proposed, automatic image registration is still a challenging task due to the influence of various conditions and uncertain difficulties for remote sensing images. In this letter, a novel image registration method, including two types of feature detectors and a region boundary constraint strategy for matching, is proposed. Two types of features detected by scale-invariant feature transform and Harris operators have advantages of keeping different structural information in the image and increasing the number of keypoints for later matching. Afterward, a region boundary constraint strategy based on the image sketch map is utilized in matching step. This strategy restricts the detected two types of features in their respective structural region and nonstructural region to reduce the incorrect correspondences. Experimental results demonstrate the superiority of our proposed registration algorithm compared with other research works in terms of correct matching number and aligning accuracy.
      PubDate: Oct. 2017
      Issue No: Vol. 14, No. 10 (2017)
       
  • Land Cover Classification via Multitemporal Spatial Data by Deep Recurrent
           Neural Networks
    • Authors: Dino Ienco;Raffaele Gaetano;Claire Dupaquier;Pierre Maurel;
      Pages: 1685 - 1689
      Abstract: Nowadays, modern earth observation programs produce huge volumes of satellite images time series that can be useful to monitor geographical areas through time. How to efficiently analyze such a kind of information is still an open question in the remote sensing field. Recently, deep learning methods proved suitable to deal with remote sensing data mainly for scene classification(i.e., convolutional neural networks on single images) while only very few studies exist involving temporal deep learning approaches [i.e., recurrent neural networks (RNNs)] to deal with remote sensing time series. In this letter, we evaluate the ability of RNNs, in particular, the long short-term memory (LSTM) model, to perform land cover classification considering multitemporal spatial data derived from a time series of satellite images. We carried out experiments on two different data sets considering both pixel-based and object-based classifications. The obtained results show that RNNs are competitive compared with the state-of-the-art classifiers, and may outperform classical approaches in the presence of low represented and/or highly mixed classes. We also show that the alternative feature representation generated by LSTM can improve the performances of standard classifiers.
      PubDate: Oct. 2017
      Issue No: Vol. 14, No. 10 (2017)
       
  • Harbor Water Area Extraction From Pan-Sharpened Remotely Sensed Images
           Based on the Definition Circle Model
    • Authors: Yin Zhuang;Penglin Wang;Yiding Yang;Hao Shi;He Chen;Fukun Bi;
      Pages: 1690 - 1694
      Abstract: Harbor water area extraction is a key step in nearshore environment pollution surveillance using remote sensing image processing techniques. This letter proposes the definition circle (DC) model of color gradient to describe color fluctuations in harbor water surface areas based on pan-sharpened remote sensing images. The DC model includes two steps: center setting and radius tuning. In the center setting process, labeled training set pixels are selected in the red, green, and blue color space. Then, center setting is completed in the hue, saturation, and intensity color space using the perceptron model. In the radius tuning process, positive and negative sample pixels are used to tune the radius value. After these two steps, the DC model can describe the color gradient of a water surface area and provide accurate harbor water area extraction. A series of experiments shows that the proposed DC model is robust and performs better than other extraction methods based on pan-sharpened remote sensing images.
      PubDate: Oct. 2017
      Issue No: Vol. 14, No. 10 (2017)
       
  • Feature-Fused SAR Target Discrimination Using Multiple Convolutional
           Neural Networks
    • Authors: Ning Wang;Yinghua Wang;Hongwei Liu;Qunsheng Zuo;Jinglu He;
      Pages: 1695 - 1699
      Abstract: Target discrimination has been one of the hottest issues in the interpretation of synthetic aperture radar (SAR) images. However, the presence of speckle noise and the absence of robust features make SAR discrimination difficult to deal with. Recently, convolutional neural network has obtained state-of-the-art results in pattern recognition. In this letter, we propose a target discrimination framework that jointly uses intensity and edge information of SAR images. This framework contains three parts, namely, feature extraction block, feature fusion block, and final classification block. In addition, a novel feature fusion method that can preserve the spatial relationship of different features is introduced. Experimental results on the miniSAR data demonstrate the effectiveness of our method.
      PubDate: Oct. 2017
      Issue No: Vol. 14, No. 10 (2017)
       
  • Infrared Small Target Detection via Nonnegativity-Constrained Variational
           Mode Decomposition
    • Authors: Xiaoyang Wang;Zhenming Peng;Ping Zhang;Yanmin He;
      Pages: 1700 - 1704
      Abstract: Infrared small target detection is one of the key techniques in the infrared search and track system. Frequency differences among target, background, and noise are often important information for target detection. In this letter, a nonnegativity-constrained variational mode decomposition (NVMD) method is proposed. Unlike the traditional frequency-domain methods, the proposed method can adaptively decompose the input signal into several separated band-limited subsignals, with the nonnegativity constraint. First, a bandpass filter is used as a preprocessing step. Second, by exploring the frequency and nonnegativity properties of the small target, the NVMD model is constructed. The potential target subsignal can be obtained by solving the NVMD model. By performing threshold segmentation on the potential target subsignal, we can obtain the detection result of the infrared small target. Experiments on six real infrared image sequences demonstrate that the proposed method has a good performance in target enhancement and background suppression. Additionally, the proposed method shows strong robustness under various backgrounds.
      PubDate: Oct. 2017
      Issue No: Vol. 14, No. 10 (2017)
       
  • Submesoscale Tidal-Inlet Dipoles Resolved Using Stereo WorldView Imagery
    • Authors: George Marmorino;Wei Chen;Richard P. Mied;
      Pages: 1705 - 1709
      Abstract: A pair of high-resolution visible-band satellite images, acquired 65 s apart and analyzed using an optical-flow algorithm, is shown to provide a realistic snapshot of the velocity field of dipolar vortices (dipoles) emitted from the Gulf of San Jose, Argentina. The results reveal the expected counter-rotating vortices within three dipoles, as well as one monopole; the magnitude of vorticity ranges from 8 to 29 times the local Coriolis parameter. Analysis of the derived velocity and vorticity fields yields an estimate of 0.27 ms-1 for the dipole self-propagation velocity, which is the part of the physics that allows transport of gulf-derived material into open waters. Dipole size, measured as the distance between vortex centers, increases with distance from the source area in a manner consistent with the effects of entrainment of ambient sea water. Given favorable imaging conditions, the general approach used here can provide a detailed portrayal of local circulation patterns-one suitable for use in validating high-resolution numerical models, especially in coastal areas having complex bathymetry.
      PubDate: Oct. 2017
      Issue No: Vol. 14, No. 10 (2017)
       
  • A Band-Weighted Support Vector Machine Method for Hyperspectral Imagery
           Classification
    • Authors: Weiwei Sun;Chun Liu;Yan Xu;Long Tian;Weiyue Li;
      Pages: 1710 - 1714
      Abstract: A band-weighted support vector machine (BWSVM) method is proposed to classify hyperspectral imagery (HSI). The BWSVM presents an L1 penalty term of band weight vector to regularize the regular SVM model. The L1 norm regularization term guarantees the sparsity of band weights and describes potentially divergent contributions from different bands in modeling the binary SVM model. The BWSVM adopts the KerNel iterative feature extraction algorithm to minimize the nonconvex program. It linearizes nonlinear kernels and iteratively optimizes two convex subproblems with respect to both sample coefficients and band weights. The class label is determined by picking the largest sample coefficients from all its binary models of BWSVM. Two popular HSI data sets are utilized to testify the classification performance of BWSVM. Experimental results show that the BWSVM outperforms three state-of-the-art classifiers including SVM, random forest, and k-nearest neighbor.
      PubDate: Oct. 2017
      Issue No: Vol. 14, No. 10 (2017)
       
  • An Efficient Contrast Enhancement Method for Remote Sensing Images
    • Authors: Jiahang Liu;Chenghu Zhou;Peng Chen;Chaomeng Kang;
      Pages: 1715 - 1719
      Abstract: Remote sensing images often suffer low contrast. Although many contrast enhancement methods have been proposed in recent literature, the efficiency and robustness of remote sensing image contrast enhancement is still a challenge. In this letter, a novel self-adaptive histogram compacting transform-based contrast enhancement method for remote sensing images is presented to meet with the requirements of automation, robustness, and efficiency in applications. First, the histogram of an input image is optimized into compact and continuous status with the constraints of the merging cost, the moderate global brightness, and the entropy contribution of gray levels. Then, a local remapping algorithm is proposed to catch more details during the course of gray extending with the linear stretch. Finally, a dual-gamma transform is proposed to enhance the contrast in both bright and black areas. Experimental and comparison results demonstrate that the proposed method yields better results than the state-of-the-art methods and maintains robustness in different cases. It provides an effective approach for remote sensing image automatic contrast enhancement.
      PubDate: Oct. 2017
      Issue No: Vol. 14, No. 10 (2017)
       
  • Simultaneous Coherent and Random Noise Attenuation by Morphological
           Filtering With Dual-Directional Structuring Element
    • Authors: Weilin Huang;Runqiu Wang;Yang Zhou;Xiaoqing Chen;
      Pages: 1720 - 1724
      Abstract: Seismic data are highly corrupted by noise or unwanted energies arising from different kinds of sources. In general, seismic noise can be divided into two categories, namely, coherent noise and random noise, and is treated with essentially different methods. Traditional methods often utilize the differences in frequency, wavenumber, or amplitude to separate signal and noise. However, the application of traditional methods is limited if the above-mentioned differences are too small to distinguish. For this reason, we have proposed a novel morphology-based technique to simultaneously attenuate random noise and coherent noise, i.e., to extract the useful signal. In this technique, we first flatten the signal by normal move out correction or other alternative approaches. For the extraction of the flatten reflections, we propose dual-directional mathematical morphological filtering, which can detect morphological information of the seismic waveforms from two orthogonal directions and then separate signal and other unwanted energy utilizing their difference in morphological scales. Application of the proposed technique on synthetic and field data examples demonstrates a successful performance.
      PubDate: Oct. 2017
      Issue No: Vol. 14, No. 10 (2017)
       
  • PolSAR Ship Detection Using Local Scattering Mechanism Difference Based on
           Regression Kernel
    • Authors: Jinglu He;Yinghua Wang;Hongwei Liu;Ning Wang;
      Pages: 1725 - 1729
      Abstract: In this letter, the local scattering mechanism difference based on regression kernel (LSMDRK) is developed as a discriminative feature for ship detection. The LSMDRK measures the scattering mechanism dissimilarity of a center pixel to its neighboring pixels. A ship detection scheme is proposed based on the LSMDRK. The detection scheme consists of two stages. In the feature extraction stage, polarimetric target decomposition is required to improve the discriminative ability of the descriptor. In the detection stage, a saliency detection strategy is utilized to construct the saliency map. Then, local maximum detection is employed. Finally, an adaptive threshold method is designed to achieve the final detection. The effectiveness of the detection scheme is validated by a RADARSAT-2 data set. Experimental results demonstrate that the proposed method can acquire a better detection on weak targets and has much less false alarms than some classical detection methods.
      PubDate: Oct. 2017
      Issue No: Vol. 14, No. 10 (2017)
       
  • Automatic and Fast PCM Generation for Occluded Object Detection in
           High-Resolution Remote Sensing Images
    • Authors: Shaohua Qiu;Gongjian Wen;Zhipeng Deng;Yaxiang Fan;Bingwei Hui;
      Pages: 1730 - 1734
      Abstract: Partial configuration model (PCM) is an occluded object detection method in high-resolution remote sensing images (HR-RSIs) based on the deformable part-based model (DPM). However, it needs extra category predefinition, considerable partlevel annotation, and repeated multimodel training. In this letter, an automatic and fast PCM generation method is proposed based on a novel part sharing mechanism. We propose to share parts from one trained DPM model (tDPM) among different models of partial configurations (PCs) to address the above problems. PCs are first designed according to part anchors of tDPM. The model is then generated through corresponding parts selection, root coverage cropping, and elements reweighing. This method avoids the need for manual category predefinition and partlevel annotation, while largely reducing the computation of PCM training. Experimental results on three HR-RSI data sets show that the proposed method obtains a training speedup of 6.7× and 2× for each PC of airplane and ship categories, while achieving a comparable accuracy compared with PCM.
      PubDate: Oct. 2017
      Issue No: Vol. 14, No. 10 (2017)
       
  • Remote Sensing Image Scene Classification Using Bag of Convolutional
           Features
    • Authors: Gong Cheng;Zhenpeng Li;Xiwen Yao;Lei Guo;Zhongliang Wei;
      Pages: 1735 - 1739
      Abstract: More recently, remote sensing image classification has been moving from pixel-level interpretation to scene-level semantic understanding, which aims to label each scene image with a specific semantic class. While significant efforts have been made in developing various methods for remote sensing image scene classification, most of them rely on handcrafted features. In this letter, we propose a novel feature representation method for scene classification, named bag of convolutional features (BoCF). Different from the traditional bag of visual words-based methods in which the visual words are usually obtained by using handcrafted feature descriptors, the proposed BoCF generates visual words from deep convolutional features using off-the-shelf convolutional neural networks. Extensive evaluations on a publicly available remote sensing image scene classification benchmark and comparison with the state-of-the-art methods demonstrate the effectiveness of the proposed BoCF method for remote sensing image scene classification.
      PubDate: Oct. 2017
      Issue No: Vol. 14, No. 10 (2017)
       
  • Estimation of Significant Wave Height From X-Band Marine Radar Images
           Based on Ensemble Empirical Mode Decomposition
    • Authors: Xinlong Liu;Weimin Huang;Eric W. Gill;
      Pages: 1740 - 1744
      Abstract: In this letter, an ensemble empirical mode decomposition (EEMD)-based method is proposed to estimate significant wave height (SWH) from the X-band marine radar sea surface images. First, the data sequence in each radial direction of a radar subimage is decomposed by the EEMD into several intrinsic mode functions (IMFs). A normalization scheme is then applied to the IMFs to obtain their amplitude modulation components. Finally, by adopting a linear model, the SWH is estimated from the sum of the amplitudes from the second to the fifth modes. The method is tested using radar and buoy data collected in a sea trial off the east coast of Canada. The root-mean-square differences with respect to the buoy reference for the SWH estimations using the traditional signal-to-noise-based method, a recent shadowing-based method, and the proposed technique are 0.78, 0.48, and 0.36 m, respectively. The result indicates that the proposed technique produces improvement in the SWH measurements.
      PubDate: Oct. 2017
      Issue No: Vol. 14, No. 10 (2017)
       
  • Prediction of Sea Surface Temperature Using Long Short-Term Memory
    • Authors: Qin Zhang;Hui Wang;Junyu Dong;Guoqiang Zhong;Xin Sun;
      Pages: 1745 - 1749
      Abstract: This letter adopts long short-term memory (LSTM) to predict sea surface temperature (SST), and makes short-term prediction, including one day and three days, and long-term prediction, including weekly mean and monthly mean. The SST prediction problem is formulated as a time series regression problem. The proposed network architecture is composed of two kinds of layers: an LSTM layer and a full-connected dense layer. The LSTM layer is utilized to model the time series relationship. The full-connected layer is utilized to map the output of the LSTM layer to a final prediction. The optimal setting of this architecture is explored by experiments and the accuracy of coastal seas of China is reported to confirm the effectiveness of the proposed method. The prediction accuracy is also tested on the SST anomaly data. In addition, the model's online updated characteristics are presented.
      PubDate: Oct. 2017
      Issue No: Vol. 14, No. 10 (2017)
       
  • A Novel Approach to Subpixel Land-Cover Change Detection Based on a
           Supervised Back-Propagation Neural Network for Remotely Sensed Images With
           Different Resolutions
    • Authors: Ke Wu;Yanfei Zhong;Xianmin Wang;Weiwei Sun;
      Pages: 1750 - 1754
      Abstract: Extracting subpixel land-cover change detection (SLCCD) information is important when multitemporal remotely sensed images with different resolutions are available. The general steps are as follows. First, soft classification is applied to a low-resolution (LR) image to generate the proportion of each class. Second, the proportion differences are produced by the use of another high-resolution (HR) image and used as the input of subpixel mapping. Finally, a subpixel sharpened difference map can be generated. However, the prior HR land-cover map is only used to compare with the enhanced map of LR image for change detection, which leads to a nonideal SLCCD result. In this letter, we present a new approach based on a back-propagation neural network (BPNN) with a HR map (BPNN_HRM), in which a supervised model is introduced into SLCCD for the first time. The known information of the HR land-cover map is adequately employed to train the BPNN, whether it predates or postdates the LR image, so that a subpixel change detection map can be effectively generated. In order to evaluate the performance of the proposed algorithm, it was compared with four state-of-the-art methods. The experimental results confirm that the BPNN_HRM method outperforms the other traditional methods in providing a more detailed map for change detection.
      PubDate: Oct. 2017
      Issue No: Vol. 14, No. 10 (2017)
       
  • Accurate Insect Orientation Extraction Based on Polarization Scattering
           Matrix Estimation
    • Authors: C. Hu;W. Li;R. Wang;C. Liu;T. Zhang;W. Li;
      Pages: 1755 - 1759
      Abstract: A novel insect orientation extraction method is proposed based on the target polarization scattering matrix (PSM) estimation, which is applicable for traditional vertical-looking insect radar with noncoherent reception as well as the coherent radar. The insect echo signal at different polarization directions on the radar polarization plane is usually acquired by means of rotating linearly polarized antenna. In this letter, the insect echo signal is first used to accurately estimate insect PSM by an iterative algorithm based on the second-order polynomial approximation. Meanwhile, the Cramer–Rao lower bound is also analyzed to test the estimation performance. Next, based on the assumption that the target orientation is consistent with the dominant eigenvector, the insect orientation is extracted from the estimated PSM. Finally, both theoretical simulations and real experimental data are used to validate the effectiveness and feasibility of our proposed method, which can achieve good orientation estimation accuracy at low signal-to-noise ratio.
      PubDate: Oct. 2017
      Issue No: Vol. 14, No. 10 (2017)
       
  • Supervised Gaussian Process Latent Variable Model for Hyperspectral Image
           Classification
    • Authors: Xinwei Jiang;Xiaoping Fang;Zhikun Chen;Junbin Gao;Junjun Jiang;Zhihua Cai;
      Pages: 1760 - 1764
      Abstract: Discriminative features are significant for hyper-spectral image (HSI) classification. In this letter, we apply the supervised dimensionality reduction (DR) model termed supervised latent linear Gaussian process latent variable model (SLLGPLVM) for feature extraction. As a semiparametric classification model, the new model has ability in simultaneous feature extraction and classification and demonstrates high classification accuracy with only a small training set. This is therefore suitable for HSI classification. Experimental results on six real HSI data sets show that the proposed SLLGPLVM outperforms several conventional supervised DR models and the support vector machine implemented in the original spectral space.
      PubDate: Oct. 2017
      Issue No: Vol. 14, No. 10 (2017)
       
  • Ship Classification in Moderate-Resolution SAR Image by Naive Geometric
           Features-Combined Multiple Kernel Learning
    • Authors: Haitao Lang;Siwen Wu;
      Pages: 1765 - 1769
      Abstract: Compared with the high-resolution synthetic aperture radar (SAR) image, a moderate-resolution SAR image can offer wider swath, which is more suitable for maritime ship surveillance. Taking into account the amount of information in a moderate-resolution SAR image and the stability of feature extraction, we propose naive geometric features (NGFs) for ship classification. In contrast to the strictly defined geometric features (SGFs), the extraction of NGFs is very simpler and efficient. And more importantly, the NGFs are enough to reveal the essential difference between different types of ships for classification. To fuse various NGFs with different physical properties and discriminability, the multiple kernel learning (MKL) is utilized to learn the combination weights, rather than assigning the same weight to all features as usually applied by the traditional support vector machines (SVMs). The comprehensive experiments validate that: (1) the performance of the proposed NGF-combined MKL outperforms that of NGF-combined SVM by 3.4% and is very close to that obtained by SGF-combined MKL and (2) in terms of classifying ships in a moderate-resolution SAR image, NGFs are more feasible than scattering features.
      PubDate: Oct. 2017
      Issue No: Vol. 14, No. 10 (2017)
       
  • DEM Retrieval From Airborne LiDAR Point Clouds in Mountain Areas via Deep
           Neural Networks
    • Authors: Yimin Luo;Hongchao Ma;Liguo Zhou;
      Pages: 1770 - 1774
      Abstract: Airborne light detection and ranging (LiDAR) remote sensing enables accurate estimation and monitoring of terrain and vegetation, and digital surface model (DSM) and digital elevation model (DEM) are vital analytical tools to achieve this estimation and monitoring. Among them, DSM can be directly acquired from airborne LiDAR point clouds; nevertheless, for the production of DEM, point clouds representing a surface of ground objects should be accurately filtered out at first. In some mountain forest areas, due to the limited penetration of airborne LiDAR, ground points sustain a serious lack, which results in the difficulty in producing accurate DEMs. To reduce the intricacy and subjectivity caused by the manual supplement to ground points, this letter proposes a new DEM retrieval method from airborne LiDAR point clouds in mountain areas based on deep neural networks (DNNs). With a DNN model trained by accurate DEMs and DSMs, DEM retrieval becomes much easier by inputting their DSM into this model for prediction. Experiments on Fujian and Hainan mountain data sets demonstrate the effectiveness of this supervised method.
      PubDate: Oct. 2017
      Issue No: Vol. 14, No. 10 (2017)
       
  • Sparse Tensor-Based Dimensionality Reduction for Hyperspectral
           Spectral–Spatial Discriminant Feature Extraction
    • Authors: Zhi Liu;Bo Tang;Xiaofu He;Qingchen Qiu;Hongjun Wang;
      Pages: 1775 - 1779
      Abstract: This letter explores a spectral-spatial tensor-based dimensionality reduction (DR) method to cope with hyperspectral image (HSI) feature extraction and classification. This method uses the Gabor filter banks as the bias spectral-spatial feature hybrider and further integrates the tensor-based alignment strategy for the discriminant locality with sparse factorization by extracting optimal spectral-spatial features and simultaneously maintaining structural relevance. Comparative experimental results with two real HSIs demonstrate that the proposed DR method has a considerable advantage over other traditional feature extraction methods.
      PubDate: Oct. 2017
      Issue No: Vol. 14, No. 10 (2017)
       
  • Microwave Thermal Emission Characteristics of a Two-Layer Medium With
           Rough Interfaces Using the Second-Order Small Perturbation Method
    • Authors: R. J. Burkholder;J. T. Johnson;M. Sanamzadeh;L. Tsang;S. Tan;
      Pages: 1780 - 1784
      Abstract: The second-order small perturbation method is applied to investigate brightness temperature corrections caused by the rough interfaces of a two-layer medium. The spectral weighting functions of the two rough interfaces are extracted from the solution, and their properties examined. It is found that the functions are identical for the two interfaces as the spectral variable approaches zero, indicating an identical weighting of the surface height variance on each interface and an additive effect on the brightness temperature at nadir. Sample results for some realistic scenarios show that surface roughness in a two-layer medium can increase or decrease the observed brightness temperature at shallower angles, and in the case of a wideband measurement, can shift the interference pattern in frequency.
      PubDate: Oct. 2017
      Issue No: Vol. 14, No. 10 (2017)
       
  • Distinguishing Cloud and Snow in Satellite Images via Deep Convolutional
           Network
    • Authors: Yongjie Zhan;Jian Wang;Jianping Shi;Guangliang Cheng;Lele Yao;Weidong Sun;
      Pages: 1785 - 1789
      Abstract: Cloud and snow detection has significant remote sensing applications, while they share similar low-level features due to their consistent color distributions and similar local texture patterns. Thus, accurately distinguishing cloud from snow in pixel level from satellite images is always a challenging task with traditional approaches. To solve this shortcoming, in this letter, we proposed a deep learning system to classify cloud and snow with fully convolutional neural networks in pixel level. Specifically, a specially designed fully convolutional network was introduced to learn deep patterns for cloud and snow detection from the multispectrum satellite images. Then, a multiscale prediction strategy was introduced to integrate the low-level spatial information and high-level semantic information simultaneously. Finally, a new and challenging cloud and snow data set was labeled manually to train and further evaluate the proposed method. Extensive experiments demonstrate that the proposed deep model outperforms the state-of-the-art methods greatly both in quantitative and qualitative performances.
      PubDate: Oct. 2017
      Issue No: Vol. 14, No. 10 (2017)
       
  • Development of LiDAR-Based UAV System for Environment Reconstruction
    • Authors: Kai-Wei Chiang;Guang-Je Tsai;Yu-Hua Li;Naser El-Sheimy;
      Pages: 1790 - 1794
      Abstract: In disaster management, reconstructing the environment and quickly collecting the geospatial data of the impacted areas in a short time are crucial. In this letter, a light detection and ranging (LiDAR)-based unmanned aerial vehicle (UAV) is proposed to complete the reconstruction task. The UAV integrate an inertial navigation system (INS), a global navigation satellite system (GNSS) receiver, and a low-cost LiDAR. An unmanned helicopter is introduced and the multisensor payload architecture for direct georeferencing is designed to improve the capabilities of the vehicle. In addition, a new strategy of iterative closest point algorithm is proposed to solve the registration problems in the sparse and inhomogeneous derived point cloud. The proposed registration algorithm addresses the local minima problem by the use of direct-georeferenced points and the novel hierarchical structure as well as taking the feedback bias into INS/GNSS. The generated point cloud is compared with a more accurate one derived from a high-grade terrestrial LiDAR which uses real flight data. Results indicate that the proposed UAV system achieves meter-level accuracy and reconstructs the environment with dense point cloud.
      PubDate: Oct. 2017
      Issue No: Vol. 14, No. 10 (2017)
       
  • Boosting the Accuracy of Multispectral Image Pansharpening by Learning a
           Deep Residual Network
    • Authors: Yancong Wei;Qiangqiang Yuan;Huanfeng Shen;Liangpei Zhang;
      Pages: 1795 - 1799
      Abstract: In the field of multispectral (MS) and panchromatic image fusion (pansharpening), the impressive effectiveness of deep neural networks has recently been employed to overcome the drawbacks of the traditional linear models and boost the fusion accuracy. However, the existing methods are mainly based on simple and flat networks with relatively shallow architectures, which severely limits their performance. In this letter, the concept of residual learning is introduced to form a very deep convolutional neural network to make the full use of the high nonlinearity of the deep learning models. Through both quantitative and visual assessments on a large number of high-quality MS images from various sources, it is confirmed that the proposed model is superior to all the mainstream algorithms included in the comparison, and achieves the highest spatial-spectral unified accuracy.
      PubDate: Oct. 2017
      Issue No: Vol. 14, No. 10 (2017)
       
  • A Novel Ionospheric Sounding Radar Based on USRP
    • Authors: Ziyang Zhao;Ming Yao;Xiaohua Deng;Kai Yuan;Huixia Li;Zheng Wang;
      Pages: 1800 - 1804
      Abstract: Ionospheric sounding is a technique that provides real-time data on high-frequency ionospheric-dependent radio propagation. This letter presents a Universal Software Radio Peripheral-based ionospheric sounding radar, which relies on a basic system consisting of a synchronized transmitter and receiver. The radar has the advantages of miniaturization, modularization, low power, and low cost. The three most significant features of the radar system are that it is software-defined and universal platform-based and that it has low transmitting power. This novel software-defined vertical-incidence radar system can probe the ionosphere and obtain real-time plasma parameters according to the simulation. Ionograms that directly express probe results are generated by MATLAB after data processing and simulation. Successful development of such an ionospheric sounding software radar will allow universalization and miniaturization of an ionosonde radar system. This letter introduces the implementation of the novel ionospheric sounding radar.
      PubDate: Oct. 2017
      Issue No: Vol. 14, No. 10 (2017)
       
  • Embedding Learning on Spectral–Spatial Graph for Semisupervised
           Hyperspectral Image Classification
    • Authors: Jiayan Cao;Bin Wang;
      Pages: 1805 - 1809
      Abstract: Scarcity of labeled samples is the main obstacle for hyperspectral image classification tasks when labeling data is considerably costly and time-consuming in real-world scenarios. To alleviate any underfitting problem that may occur due to lack of training data, semisupervised classification frameworks explore the intrinsic information of unlabeled samples and bridge labeled and unlabeled data. In this letter, we propose a novel framework that learns underlying manifold representation and semisupervised classifier simultaneously. It avoids explicit eigenvector decomposition and directly samples via iterating random walk on the similarity graph, which makes it feasible to implement on huge graphs. To verify the efficacy of embedding the learning process, we compare the proposed method with other dimensionality reduction and manifold-learning-based approaches. Experimental results show that compared to the methods using traditional semisupervised strategies, the graph embedding method gives a better result.
      PubDate: Oct. 2017
      Issue No: Vol. 14, No. 10 (2017)
       
  • Adaptive Pulse Compression Technique for X-Band Phased Array Weather Radar
    • Authors: H. Kikuchi;E. Yoshikawa;T. Ushio;F. Mizutani;M. Wada;
      Pages: 1810 - 1814
      Abstract: Weather radar commonly uses a matched filter (MF) method to improve the range resolution and signal-to-noise ratio. A X-band phased array weather radar (PAWR), which is capable of 3-D precipitation observations in less than 30 s, is in operation at the Osaka University. The PAWR uses the MF method. In weather radar systems, the magnitude of the range sidelobes is an important topic because it can cause overestimation of the received power from a target, such as precipitation or ground clutter echoes. We propose a minimum mean square error (MMSE)-based pulse compression method to reduce the range sidelobes of the PAWR. We evaluated an MF, an MF with a raised-cosine window, and MMSE methods using numerical simulations and actual measurement data obtained from the PAWR. The results show that the MMSE method is clearly superior to the MF and MF with a raised-cosine filter methods when considering the reduction in the range sidelobes.
      PubDate: Oct. 2017
      Issue No: Vol. 14, No. 10 (2017)
       
  • High-Resolution Satellite Observations of a New Hazard of Golden Tides
           Caused by Floating Sargassum in Winter in the Yellow Sea
    • Authors: Qianguo Xing;Ruihong Guo;Lingling Wu;Deyu An;Ming Cong;Song Qin;Xuerong Li;
      Pages: 1815 - 1819
      Abstract: A new marine hazard of golden tides caused by floating brown macroalgae-Sargassum occurred in the Yellow Sea in December 2016. An economic loss of 0.5 billion CNY (about U.S. $73 million) was estimated due to the damaged seaweed aquaculture in the Jiangsu Shoal, China. In this letter, images from the new Chinese satellite of Gaofen (GF) with high-resolution optical cameras are used to retrieve the drifting path of floating Sargassum and its origin. A southward drifting path of floating Sargassum in the western Yellow Sea is identified for the first time, and the initial site of bloom occurrence is near the eastern end of the Shandong Peninsula, China, implying the origin of this hazard of floating Sargassum. The scale of this Sargassum bloom event in the Jiangsu Shoal is also evaluated using a linear-mixing model suitable for high-resolution images. The result shows that the total area of Sargassum-containing pixels in the GF-1 wide-field-of-view images on December 31, 2016 was more than 46 km2, and according to the estimation by the linear-mixing model, the total area of sea surface completely covered by Sargassum was above 8.8 km2. The approach and the results presented in this letter should contribute to the future study and management of golden tides in Chinese coastal waters.
      PubDate: Oct. 2017
      Issue No: Vol. 14, No. 10 (2017)
       
  • DI2S Multiswath Innovative Technique for SAR Acquisitions Optimization
    • Authors: Diego Calabrese;Vanessa Mastroddi;Stefano Federici;
      Pages: 1820 - 1824
      Abstract: The DIscrete stepped strip (DI2S) technique (actually patent pending) introduces an innovative method to use a synthetic aperture radar in time-sharing allowing the acquisition of different images either to increase azimuth resolution (DI2S-improved resolution) or to have a multi-image system improving the system capability and flexibility (DI2S multiswath). In this letter, the approach used by the DI2S multiswath technique will be described highlighting the main advantages in terms of performance and application.
      PubDate: Oct. 2017
      Issue No: Vol. 14, No. 10 (2017)
       
  • Hyperspectral Image Super-Resolution by Spectral Difference Learning and
           Spatial Error Correction
    • Authors: Jing Hu;Yunsong Li;Weiying Xie;
      Pages: 1825 - 1829
      Abstract: A hyperspectral image (HSI) super-resolution (SR) is a highly attractive topic in computer vision. However, most existed methods require an auxiliary high-resolution (HR) image with respect to the input low-resolution (LR) HSI. This limits the practicability of these HSI SR methods. Moreover, these methods often destroy the important spectral information. This letter presents a deep spectral difference convolutional neural network (SDCNN) with the combination of a spatial-error-correction (SEC) model for HSI SR. This method allows for full exploration of the spectral and spatial correlations, which achieves a good spatial information enhancement and spectral information preservation. In the proposed method, the key band is automatically selected and super-resolved with the boundary bands. Meanwhile, spectral difference mapping between the LR and HR HSIs can be learned by the SDCNN, and then be transformed according to the SEC model, which aims at correcting the spatial error while preserving the spectral information. The rest nonkey bands will be super-resolved under the guidance of the transformed spectral difference. Experimental results on synthesized and real-scenario HSIs suggest that the proposed method: (1) achieves comparable performance without requiring any auxiliary images of the same scene and (2) requires less computation time than the state-of-the-art methods.
      PubDate: Oct. 2017
      Issue No: Vol. 14, No. 10 (2017)
       
  • Three-Operator Proximal Splitting Scheme for 3-D Seismic Data
           Reconstruction
    • Authors: Yufeng Wang;Hui Zhou;Shaohuan Zu;Weijian Mao;Yangkang Chen;
      Pages: 1830 - 1834
      Abstract: The proximal splitting algorithm, which reduces complex convex optimization problems into a series of smaller subproblems and spreads the projection operator onto a convex set into the proximity operator of a convex function, has recently been introduced in the area of signal processing. Following the splitting framework, we propose a novel three-operator proximal splitting (TOPS) algorithm for 3-D seismic data reconstruction with both singular value decomposition (SVD)-based low-rank constraint and curvelet-domain sparsity constraint. Compared with the well-known forward-backward splitting (FBS) method, our proposed TOPS algorithm can be flexibly employed to recover a signal satisfying double convex constraints simultaneously, such as low-rank constraint and sparsity constraint used in this letter. We have used both synthetic and field data examples to demonstrate the superior performance of the TOPS method over traditional SVD-based low-rank method and curvelet-domain sparsity method based on the FBS framework.
      PubDate: Oct. 2017
      Issue No: Vol. 14, No. 10 (2017)
       
  • Blind Quality Assessment of Fused WorldView-3 Images by Using the
           Combinations of Pansharpening and Hypersharpening Paradigms
    • Authors: Chiman Kwan;Bence Budavari;Alan C. Bovik;Giovanni Marchisio;
      Pages: 1835 - 1839
      Abstract: WorldView 3 (WV-3) is the first commercially deployed super-spectral, very high-resolution (HR) satellite. However, the resolution of the short-wave infrared (SWIR) bands is much lower than that of the other bands. In this letter, we describe four different approaches, which are combinations of pansharpening and hypersharpening methods, to generate HR SWIR images. Since there are no ground truth HR SWIR images, we also propose a new picture quality predictor to assess hypersharpening performance, without the need for reference images. We describe extensive experiments using actual WV-3 images that demonstrate that some approaches can yield better performance than others, as measured by the proposed blind image quality assessment model of hypersharpened SWIR images.
      PubDate: Oct. 2017
      Issue No: Vol. 14, No. 10 (2017)
       
  • Precision of Ku-Band Reflected Signals of Opportunity Altimetry
    • Authors: Rashmi Shah;James L. Garrison;
      Pages: 1840 - 1844
      Abstract: This letter provides a proof-of-concept experiment and validation of an error model for bistatic altimetry using signals of opportunity (SoOps). Coastal sea surface height plays a prominent role in measuring the total water-level envelope directly and is one of the key quantities required by storm surge applications and services. Nadir satellite altimeters have a long history of mapping the variability of the earth's open ocean. However, they exhibit problems operating in coastal areas due to the effects, such as land contamination, rapid variations due to tides, and atmospheric effects. One technique for filling this gap is bistatic altimetry using SoOp (e.g., digital communication signal reflections). In this letter, we investigate capabilities of this technique. Twenty three days of data were collected at platform harvest from a single channel of the Ku-Band direct broadcast satellite. The wind speed observed during the experiment was between 4 and 14 m/s and significant wave height was between 0.7 and 4 m as measured by buoy 46 218 located 8 km away. The standard deviation in the estimation of height was found to be 7.2 cm (the same as predicted from theory). Using a least-squares approach improved the precision reducing the standard deviation to 6.8 cm. It is shown that the error in the estimation of height can be reduced to 3.5 cm by utilizing the full bandwidth (all the channels) of the SoOp. Extrapolating these results, we predict a precision of 5.3 cm from a typical (e.g., Jason) orbit of 1380 km.
      PubDate: Oct. 2017
      Issue No: Vol. 14, No. 10 (2017)
       
  • Change Detection Based on Deep Siamese Convolutional Network for Optical
           Aerial Images
    • Authors: Yang Zhan;Kun Fu;Menglong Yan;Xian Sun;Hongqi Wang;Xiaosong Qiu;
      Pages: 1845 - 1849
      Abstract: In this letter, we propose a novel supervised change detection method based on a deep siamese convolutional network for optical aerial images. We train a siamese convolutional network using the weighted contrastive loss. The novelty of the method is that the siamese network is learned to extract features directly from the image pairs. Compared with hand-crafted features used by the conventional change detection method, the extracted features are more abstract and robust. Furthermore, because of the advantage of the weighted contrastive loss function, the features have a unique property: the feature vectors of the changed pixel pair are far away from each other, while the ones of the unchanged pixel pair are close. Therefore, we use the distance of the feature vectors to detect changes between the image pair. Simple threshold segmentation on the distance map can even obtain good performance. For improvement, we use a k-nearest neighbor approach to update the initial result. Experimental results show that the proposed method produces results comparable, even better, with the two state-of-the-art methods in terms of F-measure.
      PubDate: Oct. 2017
      Issue No: Vol. 14, No. 10 (2017)
       
  • A Local Feature Descriptor Based on Log-Gabor Filters for Keypoint
           Matching in Multispectral Images
    • Authors: Cristiano F. G. Nunes;Flávio L. C. Pádua;
      Pages: 1850 - 1854
      Abstract: This letter presents a new local feature descriptor for problems related to multispectral images. Most previous approaches are typically based on descriptors designed to work with images uniquely captured in the visible light spectrum. In contrast, this letter proposes a descriptor termed a multispectral feature descriptor (MFD) that is especially developed, such that it can be employed with image data acquired at different frequencies across the electromagnetic spectrum. The performance of the MFD is evaluated by using three data sets composed of images obtained in visible light and infrared spectra, and its performance is compared with those of state-of-the-art algorithms, such as edge-oriented histogram (EOH) and log-Gabor histogram descriptor (LGHD). The experimental results indicate that the computational efficiency of MFD exceeds those of EOH and LGHD, and that the precision and recall values of MFD are statistically comparable to the corresponding values of the forementioned algorithms.
      PubDate: Oct. 2017
      Issue No: Vol. 14, No. 10 (2017)
       
  • Multiview Canonical Correlation Analysis Networks for Remote Sensing Image
           Recognition
    • Authors: Xinghao Yang;Weifeng Liu;Dapeng Tao;Jun Cheng;Shuying Li;
      Pages: 1855 - 1859
      Abstract: In the past decade, deep learning (DL) algorithms have been widely used for remote sensing (RS) image recognition tasks. As the most typical DL model, convolutional neural networks (CNNs) achieves outstand performance for big RS data classification. Recently, a variant of CNN, dubbed canonical correlation analysis network (CCANet), was proposed to abstract the two-view image features. Extensive experiments conducted on several benchmark databases validate the effectiveness of CCANet. However, the CCANet structure is powerless when the observations arrive from more than two sources. To serve the multiview purpose, in this letter, we propose multiview CCANets (MCCANets). Particularly, the MCCANet model learns the stacked multiperspective filter banks by the MCCA method and builds a deep convolutional structure. In the output stage, the binarization and the blockwise histogram are employed as nonlinear processing and feature pooling, respectively. To access the effectiveness of the MCCANet, we conduct a host of experiments on the RSSCN7 RS database. Extensive experimental results demonstrate that the MCCANet outperforms the two-view CCANet.
      PubDate: Oct. 2017
      Issue No: Vol. 14, No. 10 (2017)
       
  • On the Estimation of Ground and Volume Polarimetric Covariances in Forest
           Scenarios With SAR Tomography
    • Authors: Matteo Pardini;Konstantinos Papathanassiou;
      Pages: 1860 - 1864
      Abstract: A two-layer model composed by ground and volume contributions has been proven suitable to describe the 3-D backscattering signatures of forest scenarios in a number of experiments. Under this hypothesis, the purpose of this letter is to investigate how synthetic aperture radar tomography (TomoSAR) can be used to estimate ground and volume polarimetric covariances and with which performance. An algorithm which is able to overcome the intrinsic ambiguity in the estimation problem is proposed, and it is shown to be a reliable alternative to the poorly performing full-rank Capon beamformer for estimating the ground polarimetric covariances. This performance improvement can be achieved, for instance, if an a priori knowledge of the ground topography (or an accurate estimate of it) is available. This analysis is carried out by processing an L-band TomoSAR stack acquired by the DLR's E-SAR sensor over the temperate forest site of Traunstein.
      PubDate: Oct. 2017
      Issue No: Vol. 14, No. 10 (2017)
       
  • Rapid Line-Extraction Method for SAR Images Based on Edge-Field Features
    • Authors: Qian-Ru Wei;Da-Zheng Feng;Wei Zheng;Jiang-Bin Zheng;
      Pages: 1865 - 1869
      Abstract: This letter proposes a rapid line-extraction (RLE) method for synthetic aperture radar (SAR) images. RLE first transforms an image in the space domain into an image in the frequency domain. Then, using the central-slice theorem, RLE skilfully maps the image in the frequency domain into a parameter space, which effectively accelerates the straight-line extraction process. Unlike the traditional Hough transform, RLE is performed directly on an edge-field image rather than on a binary edge map. Theoretical analysis proves the advantages of using the edge-field map. Notably, the computational complexity can be greatly reduced relative to the complexity of obtaining a binary edge map, and the method can efficiently avoid the negative influence of false edges in the binary edge map. More importantly, because speckle, clutter, and blurred edges in real-world images decrease the sharpness of peaks, edge-field images that include the strength and direction information of SAR images are adopted to reduce the diffusion of peaks and improve the detection accuracy. Experimental studies show that RLE works independently, is robust to noise, has low computational complexity, achieves high true-positive detection rates, and yields satisfactory detection precision.
      PubDate: Oct. 2017
      Issue No: Vol. 14, No. 10 (2017)
       
  • Information-Assisted Density Peak Index for Hyperspectral Band Selection
    • Authors: Xiaoyan Luo;Rui Xue;Jihao Yin;
      Pages: 1870 - 1874
      Abstract: Band selection has become an effective method to reduce hyperspectral dimensionality. In this letter, an information-assisted density peak index (IaDPI) is proposed to prioritize the bands. Based on a clustering method by finding density peaks, IaDPI introduces the intraband information entropy into the local density and intercluster distance to ensure cluster centers with a high quality. Also, the band distance is integrated with channel proximity to control the compactness of local density. Owing to the intraband entropy and the interband weighted dissimilarity, the selected band set with top-ranked IaDPI scores can hold high local density, clear global distinction, and good informative quality. Experimental results on real hyperspectral data indicate the advantages of the proposed IaDPI in good selection quality, robust noise immunity, and high classification accuracy.
      PubDate: Oct. 2017
      Issue No: Vol. 14, No. 10 (2017)
       
  • Novel In Situ Boundary Detection Algorithm for Horizon
           Control in Longwall Mining
    • Authors: Jiu-Kun Che;Chi-Chih Chen;Larry G. Stolarczyk;Joseph T. Duncan;
      Pages: 1875 - 1879
      Abstract: Real-time horizon control of the cutting head is very important in longwall mining for maximizing production, minimizing wear, and reducing postimpurity processing cost. If the horizon control sensor has to be mounted directly onto the cutting drum, then it needs to withstand the impacts from mining debris as well as shock and vibration at an average level of 26 Gs and a peak level of 100-G force. A single-frequency boundary detection sensor has been developed for this purpose for its simple design and extremely high measurement rate compared to other more sophisticated pulsed or swept-frequency radar sensors, and thus is more suitable for fast rotating cutting drums. However, the accuracy and effectiveness of this method in practice are severely limited by: 1) the interference of the much stronger reflection arising from the air-ground interface and 2) the uncertainty of the permittivity and conductivity of the ground. These two issues are alleviated by the proposed practical in situ calibration procedure discussed in this letter. This procedure only requires more than four calibration control cuts prior to the normal longwall mining cuts. The effectiveness of this method is demonstrated in this letter via both simulated and experimental data.
      PubDate: Oct. 2017
      Issue No: Vol. 14, No. 10 (2017)
       
 
 
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