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IEEE transactions on geoscience and remote sensing... 54건

  1. [해외논문]   Vicarious Cold Calibration for Conical Scanning Microwave Imagers   SCI SCIE

    Kroodsma, Rachael A. , McKague, Darren S. , Ruf, Christopher S.
    IEEE transactions on geoscience and remote sensing : a publication of the IEEE Geoscience and Remote Sensing Society v.55 no.2 ,pp. 816 - 827 , 2017 , 0196-2892 ,

    초록

    Vicarious cold calibration (VCC) for spaceborne microwave radiometers is analyzed and modified for application to conical scanning microwave imagers at frequencies from 6 to 90 GHz. The details of the algorithm are modified to account for additional frequencies and polarizations that were not included in the development of the original algorithm. The modified algorithm is shown to produce a more stable cold reference brightness temperature (TB) than the original algorithm. An analysis is performed of this updated algorithm to show the global regions that contribute to the derivation of the cold reference TB and to show which geophysical parameters contribute to the coldest TBs. The analysis suggests that water vapor variability has the largest impact on the TBs in the VCC algorithm. The modified VCC algorithm is applied to microwave imager data and is used as an intercalibration method. It is shown to agree well with other intercalibration methods, demonstrating that it is a valid and accurate method for calibration of microwave imagers.

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  2. [해외논문]   A Novel Probabilistic Method to Model the Uncertainty of Tidal Prediction   SCI SCIE

    Kavousi-Fard, Abdollah
    IEEE transactions on geoscience and remote sensing : a publication of the IEEE Geoscience and Remote Sensing Society v.55 no.2 ,pp. 828 - 833 , 2017 , 0196-2892 ,

    초록

    This paper develops a probabilistic model to predict the tidal current for modeling the prediction uncertainty, and thereby the forecast error. This requires the extension of the deterministic models from a point-by-point forecast to the probabilistic models with prediction intervals (PIs). The proposed model uses PIs to construct the bandwidth, which models the uncertainty of tidal current prediction properly. It uses the lower upper bound estimation method to train the neural network (NN) without making any assumption about the distribution of the forecast error. In order to adjust the weighting and biasing factors of NN, firefly algorithm with a new two-phase modification method is developed to search the problem space globally. Two benchmarks are used to show the search ability of the algorithm. The high accuracy of the proposed model is examined on the practical tidal data collected from the Bay of Fundy, NS, Canada.

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  3. [해외논문]   Ultrawideband FMCW Radar for Airborne Measurements of Snow Over Sea Ice and Land   SCI SCIE

    Yan, Jie-Bang (Center for Remote Sensing of Ice Sheets, The University of Kansas, Lawrence, KS, USA ) , Gomez-Garcia Alvestegui, Daniel (Center for Remote Sensing of Ice Sheets, The University of Kansas, Lawrence, KS, USA ) , McDaniel, Jay W. (Center for Remote Sensing of Ice Sheets, The University of Kansas, Lawrence, KS, USA ) , Li, Yan (Center for Remote Sensing of Ice Sheets, The University of Kansas, Lawrence, KS, USA ) , Gogineni, Sivaprasad (Center for Remote Sensing of Ice Sheets, The University of Kansas, Lawrence, KS, USA ) , Rodriguez-Morales, Fernando (Marine Physics Branch, Naval Research Laboratory, Washington, DC, USA ) , Brozena, John (Center for Remote Sensing of Ice Sheets, The University of Kansas, Lawrence, KS, USA) , Leuschen, Carlton J.
    IEEE transactions on geoscience and remote sensing : a publication of the IEEE Geoscience and Remote Sensing Society v.55 no.2 ,pp. 834 - 843 , 2017 , 0196-2892 ,

    초록

    We present an ultrawideband frequency-modulated continuous-wave radar for airborne measurements of snow thickness. The radar operates over a frequency range of 2–18 GHz and is capable of about 1.4-cm range resolution at a nominal survey altitude of 500 m. The system was installed on a Twin Otter and used to collect data to demonstrate the capability of fine-resolution measurements of snow thickness over both sea ice and land near Barrow, AK. Data collected over a relatively smooth water surface, a lead, were used to deconvolve system effects to reduce range sidelobes and obtain close-to-ideal range resolution. Radar data collected over snow covered sea ice and land from the field campaign showed that we can map air–snow and snow–ice interfaces of thin and thick snow. The radar-derived snow thickness data are in a very good agreement with the in situ measured data with a correlation of 0.88.

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    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

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  4. [해외논문]   Hyperspectral Image Classification Using Deep Pixel-Pair Features   SCI SCIE

    Li, Wei (College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China ) , Wu, Guodong (College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China) , Zhang, Fan , Du, Qian
    IEEE transactions on geoscience and remote sensing : a publication of the IEEE Geoscience and Remote Sensing Society v.55 no.2 ,pp. 844 - 853 , 2017 , 0196-2892 ,

    초록

    The deep convolutional neural network (CNN) is of great interest recently. It can provide excellent performance in hyperspectral image classification when the number of training samples is sufficiently large. In this paper, a novel pixel-pair method is proposed to significantly increase such a number, ensuring that the advantage of CNN can be actually offered. For a testing pixel, pixel-pairs, constructed by combining the center pixel and each of the surrounding pixels, are classified by the trained CNN, and the final label is then determined by a voting strategy. The proposed method utilizing deep CNN to learn pixel-pair features is expected to have more discriminative power. Experimental results based on several hyperspectral image data sets demonstrate that the proposed method can achieve better classification performance than the conventional deep learning-based method.

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    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

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  5. [해외논문]   Millimeter-Wave Radar Sensor for Snow Height Measurements   SCI SCIE

    Ayhan, Serdal (Institute of Radio Frequency Engineering and Electronics, Karlsruhe Institute of Technology, Karlsruhe, Germany ) , Pauli, Mario (Institute of Radio Frequency Engineering and Electronics, Karlsruhe Institute of Technology, Karlsruhe, Germany ) , Scherr, Steffen (Institute of Radio Frequency Engineering and Electronics, Karlsruhe Institute of Technology, Karlsruhe, Germany ) , Gottel, Benjamin (Institute of Radio Frequency Engineering and Electronics, Karlsruhe Institute of Technology, Karlsruhe, Germany ) , Bhutani, Akanksha (Fraunhofer Institute for High Frequency Physics and Radar Techniques FHR, Wachtberg, Germany ) , Thomas, Sven (Ruhr University Bochum, Bochum, Germany ) , Jaeschke, Timo (Centre d'Études de la Neige, Saint-Martin-d'Hères, France ) , Panel, Jean-Michel (Laboratoire d'Océanographie et du Climat, Paris, France ) , Vivier, Frederic (Laboratoire d'Océanographie et du Climat, Paris, France ) , Eymard, Laurence (Laboratoire Atmosphères, Milieux, Observations Spatiales, Paris, France ) , Weill, Alain (Fraunhofer Institute for High Frequency Physics and Radar Techniques FHR, Wachtberg, Germany ) , Pohl, Nils (Institute of Radio) , Zwick, Thomas
    IEEE transactions on geoscience and remote sensing : a publication of the IEEE Geoscience and Remote Sensing Society v.55 no.2 ,pp. 854 - 861 , 2017 , 0196-2892 ,

    초록

    A small and lightweight frequency-modulated continuous-wave (FMCW) radar system is used for the determination of snow height by measuring the distance to the snow surface from a platform. The measurements have been performed at the Centre des Études de la Neige (Col de Porte), which is located near Grenoble in the French Alps. It is shown that the FMCW radar at millimeter-wave frequencies is an extremely promising approach for distance measurements to snow surfaces, e.g., in the mountains or in an Arctic environment. The characteristics of the radar sensor are described in detail. The relevant accuracy to measure the distance to a snow layer is shown at different heights and over an extended time duration. A dedicated laser snow telemeter is used as reference. In addition, the reflection from different types of snow is shown.

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    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

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  6. [해외논문]   On the Sampling Strategy for Evaluation of Spectral-Spatial Methods in Hyperspectral Image Classification   SCI SCIE

    Liang, Jie (College of Mechatronic Engineering and Automation, National University of Defense Technology, Changsha, Hunan, China ) , Zhou, Jun (Institute of Artificial Intelligence, College of Computer Science, Zhejiang University, Hangzhou, China ) , Qian, Yuntao (Institute of Integrated and Intelligent Systems, Griffith University, Brisbane, QLD, Australia ) , Wen, Lian (School of Computer Science and Engineer, Beihang University, Beijing, China ) , Bai, Xiao (Institute of Integrated and Intelligent Systems, Griffith University, Brisbane, QLD, Australia) , Gao, Yongsheng
    IEEE transactions on geoscience and remote sensing : a publication of the IEEE Geoscience and Remote Sensing Society v.55 no.2 ,pp. 862 - 880 , 2017 , 0196-2892 ,

    초록

    Spectral-spatial processing has been increasingly explored in remote sensing hyperspectral image classification. While extensive studies have focused on developing methods to improve the classification accuracy, experimental setting and design for method evaluation have drawn little attention. In the scope of supervised classification, we find that traditional experimental designs for spectral processing are often improperly used in the spectral-spatial processing context, leading to unfair or biased performance evaluation. This is especially the case when training and testing samples are randomly drawn from the same image—a practice that has been commonly adopted in the experiments. Under such setting, the dependence caused by overlap between the training and testing samples may be artificially enhanced by some spatial information processing methods, such as spatial filtering and morphological operation. Such enhancement of dependence in return amplifies the classification accuracy, leading to an improper evaluation of spectral-spatial classification techniques. Therefore, the widely adopted pixel-based random sampling strategy is not always suitable to evaluate spectral-spatial classification algorithms, because it is difficult to determine whether the improvement of classification accuracy is caused by incorporating spatial information into classifier or by increasing the overlap between training and testing samples. To tackle this problem, we propose a novel controlled random sampling strategy for spectral-spatial methods. It can greatly reduce the overlap between training and testing samples and provides more objective and accurate evaluation.

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    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

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  7. [해외논문]   Dense Semantic Labeling of Subdecimeter Resolution Images With Convolutional Neural Networks   SCI SCIE

    Volpi, Michele , Tuia, Devis
    IEEE transactions on geoscience and remote sensing : a publication of the IEEE Geoscience and Remote Sensing Society v.55 no.2 ,pp. 881 - 893 , 2017 , 0196-2892 ,

    초록

    Semantic labeling (or pixel-level land-cover classification) in ultrahigh-resolution imagery (<10 cm) requires statistical models able to learn high-level concepts from spatial data, with large appearance variations. Convolutional neural networks (CNNs) achieve this goal by learning discriminatively a hierarchy of representations of increasing abstraction. In this paper, we present a CNN-based system relying on a downsample-then-upsample architecture. Specifically, it first learns a rough spatial map of high-level representations by means of convolutions and then learns to upsample them back to the original resolution by deconvolutions. By doing so, the CNN learns to densely label every pixel at the original resolution of the image. This results in many advantages, including: 1) the state-of-the-art numerical accuracy; 2) the improved geometric accuracy of predictions; and 3) high efficiency at inference time. We test the proposed system on the Vaihingen and Potsdam subdecimeter resolution data sets, involving the semantic labeling of aerial images of 9- and 5-cm resolution, respectively. These data sets are composed by many large and fully annotated tiles, allowing an unbiased evaluation of models making use of spatial information. We do so by comparing two standard CNN architectures with the proposed one: standard patch classification, prediction of local label patches by employing only convolutions, and full patch labeling by employing deconvolutions. All the systems compare favorably or outperform a state-of-the-art baseline relying on superpixels and powerful appearance descriptors. The proposed full patch labeling CNN outperforms these models by a large margin, also showing a very appealing inference time.

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    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

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  8. [해외논문]   Joint Sparse Representation and Multitask Learning for Hyperspectral Target Detection   SCI SCIE

    Zhang, Yuxiang , Du, Bo , Zhang, Liangpei , Liu, Tongliang
    IEEE transactions on geoscience and remote sensing : a publication of the IEEE Geoscience and Remote Sensing Society v.55 no.2 ,pp. 894 - 906 , 2017 , 0196-2892 ,

    초록

    With the high spectral resolution, hyperspectral images (HSIs) provide great potential for target detection, which is playing an increasingly important role in HSI processing. Many target detection methods uniformly utilize all the spectral information or employ reduced spectral information to distinguish the targets and background. Simultaneously reducing spectral redundancy and preserving the discriminative information is a challenging problem in hyperspectral target detection. The multitask learning (MTL) technique may have the potential to solve the above problem, since it can explore the redundancy knowledge to construct multiple sub-HSIs and integrate them without any information loss. This paper proposes the joint sparse representation and MTL (JSR-MTL) method for hyperspectral target detection. This approach: 1) explores the HSIs similarity by a band cross-grouping strategy to construct multiple sub-HSIs; 2) takes full advantage of the MTL technique to integrate the sparse representation models for the multiple related sub-HSIs; and 3) applies the total reconstruction error difference accumulated over all the tasks to detect the targets. Extensive experiments were carried out on three HSIs, and it was founded that JSR-MTL generally shows a better detection performance than the other target detection methods.

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    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

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  9. [해외논문]   Evaluation of GRACE Mascon Gravity Solution in Relation to Interannual Oceanic Water Mass Variations   SCI SCIE

    Melzer, Bryce A. (School of the Earth, Ocean, and Environment, University of South Carolina, Columbia, SC, USA) , Subrahmanyam, Bulusu
    IEEE transactions on geoscience and remote sensing : a publication of the IEEE Geoscience and Remote Sensing Society v.55 no.2 ,pp. 907 - 914 , 2017 , 0196-2892 ,

    초록

    With evidence of an accelerated water cycle over the past few decades, we make inferences on the spatial variability of interannual evaporation and precipitation patterns from 2003 to 2014 gravity anomalies, using the Gravity Recovery and Climate Experiment (GRACE) satellite mascon data set. Comparison of the mascon solution with an ensemble harmonic solution is conducted, along with validation over the oceans via sea surface height from multimission altimetry minus Argo floats data/GECCO2 [the GECCO2 ocean synthesis is the German contribution to Estimating the Circulation and Climate of the Ocean project ( www.ecco-group.org )] steric sea level. The mascon solution was consistently more accurate than its spherical harmonic counterpart across large spatial and temporal scales, due mainly to the inherent smoothing from the mascon cells. Comparison of GRACE with both GECCO2 + altimetry and Argo + altimetry mass estimates revealed an offset in phase with regard to the annual cycle, but yielded an rmse of only 5.6 mm in the interannual signal after phase correction. This paper furthers evidence of an accelerated water cycle at a rate of 1.5% ± 1.1% at low latitudes, and provides a means of validation for oceanic freshwater budget studies based on salinity measurements.

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    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

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  10. [해외논문]   Pulse Compression Waveform and Filter Optimization for Spaceborne Cloud and Precipitation Radar   SCI SCIE

    Beauchamp, Robert M. (Colorado State University, Fort Collins, CO, USA ) , Tanelli, Simone (Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA ) , Peral, Eva (Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA ) , Chandrasekar, V. (Colorado State University, Fort Collins, CO, USA)
    IEEE transactions on geoscience and remote sensing : a publication of the IEEE Geoscience and Remote Sensing Society v.55 no.2 ,pp. 915 - 931 , 2017 , 0196-2892 ,

    초록

    The optimal design of pulse compression waveform/filter pairs for use with near-nadir spaceborne radar in low earth orbit for the observation of clouds and precipitation is discussed. An optimization technique is introduced that considers performance metrics specific to the remote sensing of clouds and precipitation from such platforms. Specifically, the sensitivity of the radar to precipitation and clouds is maximized as close to the ground as required. The sensitivity of the radar near the surface is typically limited by the pulse compression range sidelobes from the surface’s echo. Optimization of the waveform/filter pair’s performance is facilitated by a time-domain radar scattering model to simulate radar reflectivity range profiles. The presented radar-scattering model accounts for the radar’s configuration constraints and platform motion, as well as the spatial distribution and relative motion of the scatterers. In this paper, the optimization of both linear frequency modulation (LFM) and nonlinear frequency modulation (NLFM) waveforms is considered. It is demonstrated that the LFMwaveforms provide superior performance over NLFM waveforms for application subject to unmitigated Doppler shifts.

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    회원님의 원문열람 권한에 따라 열람이 불가능 할 수 있으며 권한이 없는 경우 해당 사이트의 정책에 따라 회원가입 및 유료구매가 필요할 수 있습니다.이동하는 사이트에서의 모든 정보이용은 NDSL과 무관합니다.

    NDSL에서는 해당 원문을 복사서비스하고 있습니다. 아래의 원문복사신청 또는 장바구니담기를 통하여 원문복사서비스 이용이 가능합니다.

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