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  Subjects -> ELECTRONICS (Total: 181 journals)
Showing 1 - 200 of 277 Journals sorted alphabetically
Acta Electronica Malaysia     Open Access  
Advances in Electrical and Electronic Engineering     Open Access   (Followers: 6)
Advances in Electronics     Open Access   (Followers: 79)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 8)
Advances in Power Electronics     Open Access   (Followers: 33)
Advancing Microelectronics     Hybrid Journal  
Aerospace and Electronic Systems, IEEE Transactions on     Hybrid Journal   (Followers: 318)
American Journal of Electrical and Electronic Engineering     Open Access   (Followers: 24)
Annals of Telecommunications     Hybrid Journal   (Followers: 9)
APSIPA Transactions on Signal and Information Processing     Open Access   (Followers: 9)
Archives of Electrical Engineering     Open Access   (Followers: 13)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 8)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 28)
Bioelectronics in Medicine     Hybrid Journal  
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 19)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 36)
Biomedical Instrumentation & Technology     Hybrid Journal   (Followers: 6)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 12)
BULLETIN of National Technical University of Ukraine. Series RADIOTECHNIQUE. RADIOAPPARATUS BUILDING     Open Access   (Followers: 1)
Bulletin of the Polish Academy of Sciences : Technical Sciences     Open Access   (Followers: 1)
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 47)
China Communications     Full-text available via subscription   (Followers: 8)
Chinese Journal of Electronics     Hybrid Journal  
Circuits and Systems     Open Access   (Followers: 15)
Consumer Electronics Times     Open Access   (Followers: 5)
Control Systems     Hybrid Journal   (Followers: 270)
Edu Elektrika Journal     Open Access   (Followers: 1)
Electrica     Open Access  
Electronic Design     Partially Free   (Followers: 106)
Electronic Markets     Hybrid Journal   (Followers: 7)
Electronic Materials Letters     Hybrid Journal   (Followers: 4)
Electronics     Open Access   (Followers: 86)
Electronics and Communications in Japan     Hybrid Journal   (Followers: 10)
Electronics For You     Partially Free   (Followers: 93)
Electronics Letters     Hybrid Journal   (Followers: 26)
Elkha : Jurnal Teknik Elektro     Open Access  
Embedded Systems Letters, IEEE     Hybrid Journal   (Followers: 51)
Energy Harvesting and Systems     Hybrid Journal   (Followers: 4)
Energy Storage Materials     Full-text available via subscription   (Followers: 3)
EPJ Quantum Technology     Open Access  
EURASIP Journal on Embedded Systems     Open Access   (Followers: 11)
Facta Universitatis, Series : Electronics and Energetics     Open Access  
Foundations and Trends® in Communications and Information Theory     Full-text available via subscription   (Followers: 6)
Foundations and Trends® in Signal Processing     Full-text available via subscription   (Followers: 10)
Frequenz     Hybrid Journal   (Followers: 1)
Frontiers of Optoelectronics     Hybrid Journal   (Followers: 1)
Geoscience and Remote Sensing, IEEE Transactions on     Hybrid Journal   (Followers: 197)
Haptics, IEEE Transactions on     Hybrid Journal   (Followers: 4)
IACR Transactions on Symmetric Cryptology     Open Access  
IEEE Antennas and Propagation Magazine     Hybrid Journal   (Followers: 97)
IEEE Antennas and Wireless Propagation Letters     Hybrid Journal   (Followers: 77)
IEEE Journal of Emerging and Selected Topics in Power Electronics     Hybrid Journal   (Followers: 46)
IEEE Journal of the Electron Devices Society     Open Access   (Followers: 9)
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits     Hybrid Journal   (Followers: 1)
IEEE Power Electronics Magazine     Full-text available via subscription   (Followers: 67)
IEEE Transactions on Antennas and Propagation     Full-text available via subscription   (Followers: 70)
IEEE Transactions on Automatic Control     Hybrid Journal   (Followers: 56)
IEEE Transactions on Circuits and Systems for Video Technology     Hybrid Journal   (Followers: 20)
IEEE Transactions on Consumer Electronics     Hybrid Journal   (Followers: 40)
IEEE Transactions on Electron Devices     Hybrid Journal   (Followers: 19)
IEEE Transactions on Information Theory     Hybrid Journal   (Followers: 26)
IEEE Transactions on Power Electronics     Hybrid Journal   (Followers: 70)
IEEE Transactions on Signal and Information Processing over Networks     Full-text available via subscription   (Followers: 12)
IEICE - Transactions on Electronics     Full-text available via subscription   (Followers: 12)
IEICE - Transactions on Information and Systems     Full-text available via subscription   (Followers: 5)
IET Cyber-Physical Systems : Theory & Applications     Open Access   (Followers: 1)
IET Microwaves, Antennas & Propagation     Hybrid Journal   (Followers: 35)
IET Nanodielectrics     Open Access  
IET Power Electronics     Hybrid Journal   (Followers: 46)
IET Smart Grid     Open Access  
IET Wireless Sensor Systems     Hybrid Journal   (Followers: 18)
IETE Journal of Education     Open Access   (Followers: 4)
IETE Journal of Research     Open Access   (Followers: 11)
IETE Technical Review     Open Access   (Followers: 13)
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)     Open Access   (Followers: 3)
Industrial Electronics, IEEE Transactions on     Hybrid Journal   (Followers: 58)
Industry Applications, IEEE Transactions on     Hybrid Journal   (Followers: 25)
Informatik-Spektrum     Hybrid Journal   (Followers: 2)
Instabilities in Silicon Devices     Full-text available via subscription   (Followers: 1)
Intelligent Transportation Systems Magazine, IEEE     Full-text available via subscription   (Followers: 13)
International Journal of Advanced Research in Computer Science and Electronics Engineering     Open Access   (Followers: 18)
International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems     Open Access   (Followers: 10)
International Journal of Antennas and Propagation     Open Access   (Followers: 11)
International Journal of Applied Electronics in Physics & Robotics     Open Access   (Followers: 4)
International Journal of Computational Vision and Robotics     Hybrid Journal   (Followers: 6)
International Journal of Control     Hybrid Journal   (Followers: 11)
International Journal of Electronics     Hybrid Journal   (Followers: 7)
International Journal of Electronics and Telecommunications     Open Access   (Followers: 13)
International Journal of Granular Computing, Rough Sets and Intelligent Systems     Hybrid Journal   (Followers: 2)
International Journal of High Speed Electronics and Systems     Hybrid Journal  
International Journal of Hybrid Intelligence     Hybrid Journal  
International Journal of Image, Graphics and Signal Processing     Open Access   (Followers: 14)
International Journal of Microwave and Wireless Technologies     Hybrid Journal   (Followers: 8)
International Journal of Nanoscience     Hybrid Journal   (Followers: 1)
International Journal of Numerical Modelling: Electronic Networks, Devices and Fields     Hybrid Journal   (Followers: 4)
International Journal of Power Electronics     Hybrid Journal   (Followers: 24)
International Journal of Review in Electronics & Communication Engineering     Open Access   (Followers: 4)
International Journal of Sensors, Wireless Communications and Control     Hybrid Journal   (Followers: 10)
International Journal of Systems, Control and Communications     Hybrid Journal   (Followers: 4)
International Journal of Wireless and Microwave Technologies     Open Access   (Followers: 6)
International Transaction of Electrical and Computer Engineers System     Open Access   (Followers: 2)
JAREE (Journal on Advanced Research in Electrical Engineering)     Open Access  
Journal of Biosensors & Bioelectronics     Open Access   (Followers: 3)
Journal of Advanced Dielectrics     Open Access   (Followers: 1)
Journal of Artificial Intelligence     Open Access   (Followers: 10)
Journal of Circuits, Systems, and Computers     Hybrid Journal   (Followers: 4)
Journal of Computational Intelligence and Electronic Systems     Full-text available via subscription   (Followers: 1)
Journal of Electrical and Electronics Engineering Research     Open Access   (Followers: 25)
Journal of Electrical Bioimpedance     Open Access  
Journal of Electrical Bioimpedance     Open Access   (Followers: 2)
Journal of Electrical Engineering & Electronic Technology     Hybrid Journal   (Followers: 7)
Journal of Electrical, Electronics and Informatics     Open Access  
Journal of Electromagnetic Analysis and Applications     Open Access   (Followers: 7)
Journal of Electromagnetic Waves and Applications     Hybrid Journal   (Followers: 8)
Journal of Electronic Design Technology     Full-text available via subscription   (Followers: 6)
Journal of Electronics (China)     Hybrid Journal   (Followers: 4)
Journal of Energy Storage     Full-text available via subscription   (Followers: 4)
Journal of Engineered Fibers and Fabrics     Open Access   (Followers: 2)
Journal of Field Robotics     Hybrid Journal   (Followers: 3)
Journal of Guidance, Control, and Dynamics     Hybrid Journal   (Followers: 170)
Journal of Information and Telecommunication     Open Access   (Followers: 1)
Journal of Intelligent Procedures in Electrical Technology     Open Access   (Followers: 3)
Journal of Low Power Electronics     Full-text available via subscription   (Followers: 7)
Journal of Low Power Electronics and Applications     Open Access   (Followers: 9)
Journal of Microelectronics and Electronic Packaging     Hybrid Journal  
Journal of Microwave Power and Electromagnetic Energy     Hybrid Journal  
Journal of Microwaves, Optoelectronics and Electromagnetic Applications     Open Access   (Followers: 10)
Journal of Nuclear Cardiology     Hybrid Journal  
Journal of Optoelectronics Engineering     Open Access   (Followers: 4)
Journal of Physics B: Atomic, Molecular and Optical Physics     Hybrid Journal   (Followers: 29)
Journal of Power Electronics & Power Systems     Full-text available via subscription   (Followers: 11)
Journal of Semiconductors     Full-text available via subscription   (Followers: 5)
Journal of Sensors     Open Access   (Followers: 26)
Journal of Signal and Information Processing     Open Access   (Followers: 9)
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer     Open Access  
Jurnal Rekayasa Elektrika     Open Access  
Jurnal Teknik Elektro     Open Access  
Jurnal Teknologi Elektro     Open Access  
Kinetik : Game Technology, Information System, Computer Network, Computing, Electronics, and Control     Open Access  
Learning Technologies, IEEE Transactions on     Hybrid Journal   (Followers: 12)
Magnetics Letters, IEEE     Hybrid Journal   (Followers: 7)
Majalah Ilmiah Teknologi Elektro : Journal of Electrical Technology     Open Access   (Followers: 2)
Metrology and Measurement Systems     Open Access   (Followers: 5)
Microelectronics and Solid State Electronics     Open Access   (Followers: 19)
Nanotechnology Magazine, IEEE     Full-text available via subscription   (Followers: 33)
Nanotechnology, Science and Applications     Open Access   (Followers: 6)
Nature Electronics     Hybrid Journal  
Networks: an International Journal     Hybrid Journal   (Followers: 5)
Open Electrical & Electronic Engineering Journal     Open Access  
Open Journal of Antennas and Propagation     Open Access   (Followers: 8)
Optical Communications and Networking, IEEE/OSA Journal of     Full-text available via subscription   (Followers: 15)
Paladyn. Journal of Behavioral Robotics     Open Access   (Followers: 1)
Power Electronics and Drives     Open Access   (Followers: 1)
Problemy Peredachi Informatsii     Full-text available via subscription  
Progress in Quantum Electronics     Full-text available via subscription   (Followers: 7)
Pulse     Full-text available via subscription   (Followers: 5)
Radiophysics and Quantum Electronics     Hybrid Journal   (Followers: 2)
Recent Advances in Communications and Networking Technology     Hybrid Journal   (Followers: 3)
Recent Advances in Electrical & Electronic Engineering     Hybrid Journal   (Followers: 9)
Research & Reviews : Journal of Embedded System & Applications     Full-text available via subscription   (Followers: 5)
Revue Méditerranéenne des Télécommunications     Open Access  
Security and Communication Networks     Hybrid Journal   (Followers: 2)
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of     Hybrid Journal   (Followers: 54)
Semiconductors and Semimetals     Full-text available via subscription   (Followers: 1)
Sensing and Imaging : An International Journal     Hybrid Journal   (Followers: 2)
Services Computing, IEEE Transactions on     Hybrid Journal   (Followers: 4)
Software Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 75)
Solid-State Circuits Magazine, IEEE     Hybrid Journal   (Followers: 13)
Solid-State Electronics     Hybrid Journal   (Followers: 9)
Superconductor Science and Technology     Hybrid Journal   (Followers: 2)
Synthesis Lectures on Power Electronics     Full-text available via subscription   (Followers: 3)
Technical Report Electronics and Computer Engineering     Open Access  
TELE     Open Access  
Telematique     Open Access  
TELKOMNIKA (Telecommunication, Computing, Electronics and Control)     Open Access   (Followers: 9)
Universal Journal of Electrical and Electronic Engineering     Open Access   (Followers: 6)
Visión Electrónica : algo más que un estado sólido     Open Access   (Followers: 1)
Wireless and Mobile Technologies     Open Access   (Followers: 6)
Wireless Power Transfer     Full-text available via subscription   (Followers: 4)
Women in Engineering Magazine, IEEE     Full-text available via subscription   (Followers: 11)
Електротехніка і Електромеханіка     Open Access  

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Similar Journals
Journal Cover
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of
Journal Prestige (SJR): 1.547
Citation Impact (citeScore): 4
Number of Followers: 54  
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1939-1404
Published by IEEE Homepage  [191 journals]
  • Frontcover
    • Abstract: Presents the front cover for this issue of the publication.
      PubDate: May 2019
      Issue No: Vol. 12, No. 5 (2019)
  • IEEE Geoscience and Remote Sensing Societys
    • Abstract: Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
      PubDate: May 2019
      Issue No: Vol. 12, No. 5 (2019)
  • IEEE Geoscience and Remote Sensing Societys
    • Abstract: Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
      PubDate: May 2019
      Issue No: Vol. 12, No. 5 (2019)
  • Institutional Listings
    • Abstract: Presents GRSS society institutional listings for this issue of the publication.
      PubDate: May 2019
      Issue No: Vol. 12, No. 5 (2019)
  • Improvements to the AERIoe Thermodynamic Profile Retrieval Algorithm
    • Authors: David D. Turner;W. Greg Blumberg;
      Pages: 1339 - 1354
      Abstract: Temperature and humidity profiles in the atmospheric boundary layer (i.e., from the surface to 3 km) can be retrieved from ground-based spectral infrared observations made by the atmospheric emitted radiance interferometer (AERI) at high temporal and moderate vertical resolution. However, the retrieval is an ill-posed problem, and thus there are multiple thermodynamic solutions that might satisfy the observed radiances. Previous work developed a physical-iterative method called AERIoe that retrieved temperature and water vapor mixing ratio profiles from these radiance observations in both clear and cloudy conditions. The AERIoe algorithm was modified to enforce two physical constraints, namely that the derived relative humidity must be less than 100% and that the potential temperature must be monotonically increasing with height above some thin potentially subadiabatic layer after each iteration. Furthermore, additional observations including in situ surface meteorology, numerical weather prediction model output, microwave brightness temperatures, and partial profiles of water vapor from a Raman lidar were incorporated into the observation vector of the retrieval along with the infrared radiance observations. The addition of these new observations markedly improved the accuracy of the temperature profiles, especially above 2 km, and the water vapor profiles relative to radiosondes. These improvements are seen using cases from the tropics, mid-latitudes, and Arctic.
      PubDate: May 2019
      Issue No: Vol. 12, No. 5 (2019)
  • Radar–Radiometer-Based Liquid Water Content Retrievals of Warm Low-Level
           Clouds: How the Measurement Setup Affects Retrieval Uncertainties
    • Authors: Nils Küchler;Ulrich Löhnert;
      Pages: 1355 - 1361
      Abstract: Here, we propose a new methodology that increases the understanding of uncertainty sources of liquid water content (LWC) retrievals, which are caused by the combination of instruments having different beam widths and are horizontally displaced. Furthermore, we give first quantitative uncertainty estimates. This paper is based on a case study of a single-layer, warm, stratiform cloud observed at the Jülich Observatory for Cloud Evolution. The LWC profiles of this cloud have been forward-simulated with the passive and active radiative transfer model providing radar and microwave radiometer (MWR) observables for all cloud columns. These observables have been converted back into LWC profiles, whereas, in this case, the MWR and radar observables from different columns were combined, representing horizontal displacement. We investigate the influence of horizontal distance between a radar and an MWR on a commonly used retrieval for LWC, which scales radar reflectivity profiles with the liquid water path given by the MWR. We found that a displacement of only 10 m already introduces an additional relative uncertainty of 10%. At 100 m displacement, the relative error grows up to 30%. Additionally, different beam widths decrease the retrieval accuracy by a few percent; however, at large displacements, radiometers with larger beam widths slightly decrease the error due to the displacement. Finally, we show that cloud edge studies require optimally matched beams between the radar and the radiometer, and already a displacement of 10 m leads to unreasonable results.
      PubDate: May 2019
      Issue No: Vol. 12, No. 5 (2019)
  • A Bayesian Estimation Technique for Improving the Accuracy of SCATSAT-1
           Winds in Rainy Conditions
    • Authors: Kaushik Gopalan;Rajesh Sikhakolli;Abhisek Chakraborty;
      Pages: 1362 - 1368
      Abstract: In this study, we present a mean-of-posteriors Bayesian estimation technique for improving rain contaminated wind speed estimates from the SCATSAT-1 scatterometer, particularly at low and moderate wind speeds. The performance of the algorithm is assessed using the SCATSAT-1 Version 1.1.3 data, collocated National Centre for Medium Range Weather Forecasting (NCMRWF) analysis winds, measurements from the Advanced Scatterometer (ASCAT), and buoy measurements for a 6-month period from April to September 2018. The validation results show a reduction in wind speed bias from 2.6 to -0.1 m/s and a reduction in root mean square difference (RMSD) from 4.8 to 2.9 m/s relative to NCMRWF. The results from comparisons with ASCAT and buoy data are also qualitatively similar; the RMSD relative to ASCAT reduces from 2.9 to 1.8 m/s after correction, whereas the RMSD relative to buoy reduces from 4.1 to 2.2 m/s. Different methods such as histogram comparisons, across-track, wind speed dependent, and spatial variation of wind speed errors have also been presented to demonstrate the overall improvement achieved by this technique. This algorithm presents two main advantages: 1) the errors in wind speed estimates are significantly lower after correction and 2) this methodology can be applied on both the inner and outer SCATSAT-1 swath.
      PubDate: May 2019
      Issue No: Vol. 12, No. 5 (2019)
  • Fully Spectral Method for Radar-Based Precipitation Nowcasting
    • Authors: Seppo Pulkkinen;V. Chandrasekar;Ari-Matti Harri;
      Pages: 1369 - 1382
      Abstract: Short-term forecasts (nowcasts) of severe rainfall and flooding are of high importance to the society. In the collaborative adaptive sensing of the atmosphere (CASA) project, a high-resolution X-band radar network was deployed in the Dallas-Fort Worth (DFW) urban area. The dynamic and adaptive radar tracking of storms (DARTS) is a key component of the precipitation nowcasting system that was developed in the CASA project. In DARTS, the advection field determination is formulated in the spectral domain using the discrete Fourier transform (DFT). Building on the earlier work, an extension of DARTS is proposed. The novelty of the proposed scale filtering (SF-DARTS) method is the formulation of the extrapolation also in the spectral domain. The extrapolation method is combined with autoregressive AR(2) models applied to Fourier frequency bands together with adaptive truncation of DFT coefficients. This effectively filters small spatial scales having low predictability. It is shown that the proposed approach improves forecast skill and gives improved computational efficiency compared to conventional methods. Another important contribution is that DARTS is being evaluated for the first time beyond the urban scale. DARTS and SF-DARTS are evaluated using data from two different sources, namely the urban-scale CASA DFW network (200 km), and the country-wide radar network operated by the Finnish Meteorological Institute (1000 km).
      PubDate: May 2019
      Issue No: Vol. 12, No. 5 (2019)
  • Multivariate DINEOF Reconstruction for Creating Long-Term Cloud-Free
           Chlorophyll-a Data Records From SeaWiFS and MODIS: A Case Study in Bohai
           and Yellow Seas, China
    • Authors: Yueqi Wang;Zhiqiang Gao;Dongyan Liu;
      Pages: 1383 - 1395
      Abstract: A long-term reliable satellite chlorophyll-a (chl-a) data record is essential in understanding the state of ocean biology and quantifying its changes. Creating a long-term data record requires a combination/merger of multiple satellite products into one data record, since the lifetime of any single ocean color sensor is finite. However, because of differences in sensor design, calibration, and retrieval models, apparent cross-mission biases are usually observed between different sensor products. To attain a coherent multisensor chl-a data record, the observed cross-mission biases should be accurately addressed in the data combination/merging schemes. In this study, a multivariable data interpolating empirical orthogonal functions (M-DINEOF) approach was used to create long-term chl-a records by applying the sea-viewing wide field-of-view sensor and moderate resolution imaging spectroradiometer products. Under the assumption that the single-sensor chl-a product is free from spurious temporal artifacts and can be reference time series representing the actual variability of chl-a, the discrepancies of trends derived from different chl-a series were quantitatively evaluated based on statistical t-test and Taylor diagram analyses. Compared with direct concatenation and linear regression methods, the M-DINEOF method more effectively reproduced the main trend patterns observed in reference data series during their overlapped periods. The results highlight the importance of a cross-mission bias correction when combining multisensor satellite data records and suggest that the M-DINEOF reconstruction provides a simple and effective path forward for creating reliable multisensor ocean color records suitable for long-term trend analysis.
      PubDate: May 2019
      Issue No: Vol. 12, No. 5 (2019)
  • The Impact of Summer Arctic Cyclones on Chlorophyll-a Concentration and
           Sea Surface Temperature in the Kara Sea
    • Authors: Haili Li;Delu Pan;Difeng Wang;Fang Gong;Yan Bai;Xianqiang He;Zengzhou Hao;Changqing Ke;
      Pages: 1396 - 1408
      Abstract: In recent years, the intensity of Arctic cyclones has remarkably increased and the impact on the Arctic ecosystem has become more prominent. The Kara Sea is a Russian marginal sea with wide shelf, high productivity of phytoplankton, and abundant resources, and has a very important strategic position and research significance. We have used multiple datasets, including satellite remote sensing data and model reanalysis, to obtain the spatial-temporal distribution of Chlorophyll-a (Chl-a) concentration and sea surface temperature (SST) before and after the passage of the Arctic cyclone over the Kara Sea in summer, to explore basic processes and the ecosystem response to the cyclone. The results indicated that after the passage of the Arctic cyclone, the Chl-a concentration in different regions of the Kara Sea increased at different levels, and the SST decreased briefly. The increase of Chl-a concentration (0.49 mg/m3) caused by the Arctic cyclone happened in Kara Sea in July 2012. The nearshore Chl-a concentration increased more than that on the continental shelf, the continental shelf increases in turn being greater than the deep sea; however, the nearshore response time was shorter than that on the shelf and the deep sea. Compared with nearshore SST, which decreased more than 2 °C, the SST on the continental shelf and in the deep-sea area decreased slightly less. Besides, the left side of the cyclone cooled faster than the right side.
      PubDate: May 2019
      Issue No: Vol. 12, No. 5 (2019)
  • Detecting Sea Ice From TechDemoSat-1 Data Using Support Vector Machines
           With Feature Selection
    • Authors: Qingyun Yan;Weimin Huang;
      Pages: 1409 - 1416
      Abstract: In this paper, a framework that employs support vector machines (SVMs) is proposed for the classification of TechDemoSat-1 delay-Doppler maps (DDMs), specifically, for separating DDMs of sea ice from those of seawater. DDM data were first operated with a general data preprocessing procedure, which included noise floor subtraction and normalization. In addition, a simple and effective feature selection (FS) was devised so that the input size was significantly reduced (from 128 × 20 to 20) while the classification accuracy was enhanced. To be specific, the feature was selected as the mean value along the delay-axis (128 in length) at each Doppler bin (20 in all). Here, expected classification labels (sea ice/seawater) were obtained based on reference sea ice concentration data collected by multiple passive microwave sensors. In practice, this trained SVMs-FS algorithm showed improved accuracy but less data storage demands than the existing neural networks (NNs)- and convolutional neural networks-based methods. In addition, the designed FS was also proven to be effective for both SVMs and NNs.
      PubDate: May 2019
      Issue No: Vol. 12, No. 5 (2019)
  • Comparison of Compact and Fully Polarimetric SAR for Multitemporal Wetland
    • Authors: Mohammed Dabboor;Sarah Banks;Lori White;Brian Brisco;Amir Behnamian;Zhaohua Chen;Kevin Murnaghan;
      Pages: 1417 - 1430
      Abstract: Spaceborne Synthetic Aperture Radar (SAR) instruments are effective tools for monitoring and mapping wetlands. With the availability of SAR instruments providing various polarization options, the scope of this study is to evaluate the new compact polarization for wetland multitemporal change detection using simulated RADARSAT Constellation Mission (RCM) SAR data. A series of fully polarimetric (FP) SAR images were collected over a test site located in Ontario, Canada, and used to simulate RCM compact polarimetric (CP) data. The simulated data were evaluated for multitemporal change detection and the results were compared to those from FP SAR data. WorldView imagery and water level data were used for analysis and validation of the change detection results. The study shows potential for using the CP SAR for multitemporal change detection over three major wetland classes: shallow water, marsh, and swamp. Although FP SAR was slightly more effective in multitemporal change detection compared to CP SAR, the percentage of agreement between the change detection results of FP and CP SAR was always greater than 90% for all wetland classes. The highest overall percentage of agreement (98.4%) between the results of FP and CP SAR was observed over the shallow water, while the lowest (92.5%) was observed over swamp.
      PubDate: May 2019
      Issue No: Vol. 12, No. 5 (2019)
  • Refining a Polarimetric Decomposition of Multi-Angular UAVSAR Time Series
           for Soil Moisture Retrieval Over Low and High Vegetated Agricultural
    • Authors: Hongquan Wang;Ramata Magagi;Kalifa Goïta;Thomas Jagdhuber;
      Pages: 1431 - 1450
      Abstract: The model-based polarimetric decomposition under multi-angular condition is refined to estimate soil moisture over agricultural fields covered by different crops from Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) time series. The approach allows to disentangle the vegetation and ground scattering components in order to invert the latter for the retrieval of soil moisture. For the vegetation volume separation, the crop orientations were estimated from SAR observations acquired at different incidence angles, and the associated volume scattering component was subtracted from each acquisition individually. Afterward, the soil moisture was retrieved from both ground scattering components (surface, dihedral), using the developed multi-angular cost functions comprised of dominant Bragg surface (β) or Fresnel dihedral (α) scattering parameters. Compared to former soil moisture retrievals from model-based decomposition of multi-angular polarimetric SAR data, the present refined approach that integrated both ground components, surface and dihedral, is independent of the power attenuation and loss during the microwave propagation through the vegetation. In this way, the ambiguity in the dihedral scattering component (most prominent around 45° incidence angle) was overcome, enabling a more robust retrieval methodology by clearly decoupling the soil and vegetation dielectric constants. The proposed multi-angular approach for soil moisture retrieval was validated with respect to the ground measurements conducted during the Soil Moisture Active Passive Validation Experiment in 2012. Due to the increased number of valid dominant surface/dihedral components which are used to retrieve the soil moisture in the multi-angular approach, an overall retrieval rate of 90%, significantly higher than that of the single-angular condition (50%), is obtained. The results indicate an overall retrieval rmse of 0.07-0.09 m
      PubDate: May 2019
      Issue No: Vol. 12, No. 5 (2019)
  • A Forecasting Approach to Online Change Detection in Land Cover Time
    • Authors: Willem C. Olding;Jan C. Olivier;Brian P. Salmon;Waldo Kleynhans;
      Pages: 1451 - 1460
      Abstract: We present a method for online detection of land cover change based on remotely sensed time series. Change is detected by monitoring deviations between observations and forecasts made using the time series historical data and similar time series in the geographical region. This method and several others were applied to MODIS 8-day surface reflectance data for problems of detecting settlement expansion in Limpopo Province, South Africa, and detecting deforestation in New South Wales, Australia. The proposed method had significantly shorter median detection delay (DD) for equivalent rates of false alarms compared with the other evaluated methods. We obtained a median DD of seven samples for settlement detection and 14 samples for deforestation detection corresponding to 56 days and 112 days, respectively. This is compared with a median DD of 224 and 544 days for the best other methods evaluated. We suggest that the proposed method is an excellent candidate for land cover change detection where rapid detection is essential.
      PubDate: May 2019
      Issue No: Vol. 12, No. 5 (2019)
  • A Hierarchical Extraction Method of Impervious Surface Based on NDVI
           Thresholding Integrated With Multispectral and High-Resolution Remote
           Sensing Imageries
    • Authors: Shanshan Feng;Fenglei Fan;
      Pages: 1461 - 1470
      Abstract: Research on extraction of impervious surface has developed for decades, but it is still quite challenging to obtain impervious surface information with high accuracy, especially from multispectral remote sensing imageries. Linear spectral mixture analysis (LSMA) is a major method for estimating impervious surface areas. When LSMA is conducted, an important phenomenon is often neglected: the spectral signature is frequently interfered by the dominant component (impervious surface, vegetation, or soil) within a mixed pixel, namely the effects of mixed-pixel spectral interference. The primary objective of this study is to design a hierarchical extracting method based on normal difference vegetation index (NDVI) thresholding to eliminate these effects and improve mapping accuracy. In this method, NDVI was used and divided into different values within the range of 0.1-0.9 at intervals of 0.05. Every NDVI value was used to divide the Landsat OLI data into two segmented layers that were respectively unmixed using LSMA. The critical step was to seek out an optimal NDVI threshold by accuracy assessment, through which, a segmented layer that produced impervious surface extraction with best mapping accuracy could be identified. The results demonstrated that NDVI = 0.70 was the optimal threshold, indicating that Landsat image with NDVI ≤ 0.70 was not affected by spectral interference. Therefore, LSMA was implemented to extract impervious surface based on the Landsat image with NDVI ≤ 0.70, and the remaining area was classified by Gaofen-2 imagery to produce a complete map using object-based image analysis. This hierarchical method is effective to remove or minimize the effects of mixed-pixel spectral interference with a promising accuracy improvement of 29.3% on average. Furthermore, it provides a novel idea to enhance urban impervious surface mapping by LSMA based on NDVI thresholding.
      PubDate: May 2019
      Issue No: Vol. 12, No. 5 (2019)
  • Developing an Automatic Phenology-Based Algorithm for Rice Detection Using
           Sentinel-2 Time-Series Data
    • Authors: Amir Moeini Rad;Davoud Ashourloo;Hamid Salehi Shahrabi;Hamed Nematollahi;
      Pages: 1471 - 1481
      Abstract: Phenology-based classification methods have been developed with the goal of removing the need for ground sampling. In contrast, machine-learning-based classification methods are costly since they need extensive ground truth data to be collected, a process which is time-consuming and must be repeated on a seasonal or annual basis. Hence, in this research, we present a new automatic rule-based method, based on crop phenology, to detect rice using the time series of Sentinel-2 imagery. To do the research, the 10-m spatial resolution Sentinel-2 data acquired during the rice growing season in the red and near-infrared spectral bands for three regions in Iran were used. To develop the rules, the near-infrared band reflectance at the rice cultivation time, the red band reflectance close to the rice harvest time, and the temporal Normalized Difference Vegetation Index data were used to detect rice and discriminate it from other crops. Furthermore, the dates of data associated with the phenological stages used to develop the rice classification rules were extracted from the rice crop calendar. Although the rice fields had extensive intra-class temporal phenology variability, the algorithm performed with excellence in detecting them. The kappa coefficients obtained were 0.73, 0.94, and 0.70 for Marvdasht, Dargaz, and Qazvin, respectively.
      PubDate: May 2019
      Issue No: Vol. 12, No. 5 (2019)
  • Using Sentinel-1 Imagery for Soil Salinity Prediction Under the Condition
           of Coastal Restoration
    • Authors: Ren-Min Yang;Wen-Wen Guo;
      Pages: 1482 - 1488
      Abstract: Soil salinity is a major cause of land degradation in coastal environments and arid lands; in the first case due to sea water, and the second case due to precipitation/evaporation relationship. In coastal wetlands, soil salinity is very sensitive to plant invasion. In this context, it is necessary to obtain a better understanding of soil salinity variation to improve the management of coastal land resources. In this study, we explored the potential of Sentinel-1 data in predicting electrical conductivity (EC) at three depths. Also, we assessed the usefulness of the knowledge of the invasion process in EC prediction by comparing structural equation modeling (SEM), that included such knowledge, and linear regression model (LM), that simply modeled the relationships between EC and predictors. The case study was conducted in an invaded coastal wetland dominated by Spartina alterniflora Loisel in the east-central China coast. Before modeling, principal component analysis was used to reduce the multidimensionality of time series images. In SEM, the model explained 82% of EC variation in 0-30 cm, 99% in 30-60 cm, and 71% in 60-100 cm. The cross validation showed the SEM model provided good accuracy, with RPD (a ratio of performance to deviation) values of 1.41 in 0-30 cm, 1.51 in 30-60 cm, and 1.43 in 60-100 cm. In comparison to the poorer accuracy of LM models, we argued that modeling the relationships between the exotic plant and EC at different depths can be treated as a substantial advantage of the approach. These results provided useful indications about the strong potentials of Sentinel-1 imagery in quantitative prediction of soil salinity.
      PubDate: May 2019
      Issue No: Vol. 12, No. 5 (2019)
  • Hybrid SAR Speckle Reduction Using Complex Wavelet Shrinkage and Non-Local
           PCA-Based Filtering
    • Authors: Ramin Farhadiani;Saeid Homayouni;Abdolreza Safari;
      Pages: 1489 - 1496
      Abstract: In this paper, a new hybrid despeckling method, based on Undecimated Dual-Tree Complex Wavelet Transform (UDT-CWT) using maximum a posteriori (MAP) estimator and non-local Principal Component Analysis (PCA)-based filtering with local pixel grouping (LPG-PCA), was proposed. To achieve a heterogeneous-adaptive speckle reduction, SAR image is classified into three classes of point targets, details, or homogeneous areas. The despeckling is done for each pixel based on its class of information. Logarithm transform was applied to the SAR image to convert the multiplicative speckle into additive noise. Our proposed method contains two principal steps. In the first step, denoising was done in the complex wavelet domain via MAP estimator. After performing UDT-CWT, the noise-free complex wavelet coefficients of the log-transformed SAR image were modeled as a two-state Gaussian mixture model. Furthermore, the additive noise in the complex wavelet domain was considered as a zero-mean Gaussian distribution. In the second step, after applying inverse UDT-CWT, an iterative LPG-PCA method was used to smooth the homogeneous areas and enhance the details. The proposed method was compared with some state-of-the-art despeckling methods. The experimental results showed that the proposed method leads to a better speckle reduction in homogeneous areas while preserving details.
      PubDate: May 2019
      Issue No: Vol. 12, No. 5 (2019)
  • Multitemporal SAR RGB Processing for Sentinel-1 GRD Products: Methodology
           and Applications
    • Authors: Donato Amitrano;Raffaella Guida;Giuseppe Ruello;
      Pages: 1497 - 1507
      Abstract: The Sentinel-1 mission has finally reached its maturity with the launch of the second Sentinel radar. Among the products delivered by the agency, the ground range detected class is raising more and more interest among users due to its reduced computational demand for information extraction and availability on cloud exploitation platforms, like the Google Earth Engine. In this paper, we present a novel multitemporal processing chain, suitable to be applied to Sentinel-1 ground range detected products to obtain RGB images, using a series of single polarization detected images. These products aim at being the equivalent for the recently introduced Level-1α, exploiting a texture measure instead of the interferometric coherence, to properly render and enhance the presence of built-up areas. The discussion is supported by experiments showing the reliability of this newly introduced class of products in classic synthetic aperture radar applications like image photointerpretation, flood mapping, and long-term urban area monitoring.
      PubDate: May 2019
      Issue No: Vol. 12, No. 5 (2019)
  • A New Fast Factorized Back Projection Algorithm for Bistatic
           Forward-Looking SAR Imaging Based on Orthogonal Elliptical Polar
    • Authors: Song Zhou;Lei Yang;Lifan Zhao;Yuhao Wang;Huilin Zhou;Liangbing Chen;Mengdao Xing;
      Pages: 1508 - 1520
      Abstract: Due to the flexible bistatic configuration and complicated moving trajectory of radar platform, time-domain algorithms have significant focusing performance advantages for bistatic forward-looking synthetic aperture radar (BFSAR) applications. In this paper, a new fast factorized back projection (FFBP) based on orthogonal elliptical polar (OEP) coordinate is proposed for BFSAR imaging. Owing to the orthogonality of OEP system, the spectrum of BFSAR subimages can be compacted into the narrowest range and very low Nyquist sampling rate can be utilized in FFBP recursion. Comparing with the conventional FFBP based on original elliptical polar coordinate, the proposed OEP-based algorithm has prominently reduced burden in computation, especially for the BFSAR cases with large baseline geometry. Moreover, a new wavenumber decomposition-based strategy is presented to reveal two-dimensional Nyquist sample requirement without complicated calculation and mathematical derivation, which makes the FFBP process much more easier to design. Promising results from simulations and raw data experiments are presented to validate the advantages of the proposed algorithm.
      PubDate: May 2019
      Issue No: Vol. 12, No. 5 (2019)
  • Hierarchical-Biased Random Walk for Urban Remote Sensing Image
    • Authors: Xudong Zhao;Ran Tao;Xuejing Kang;Wei Li;
      Pages: 1521 - 1533
      Abstract: Random walk (RW) technique, with benefit of handling complicated boundaries, has recently drawn increasing attention in image segmentation. In this paper, RW is employed for urban remote sensing image segmentation. To deal with the complex spatial distribution with heterogeneous structures, a novel hierarchical-biased RW (HBRW) method is proposed. Firstly, edge regions extracted by fractional differential are combined with histograms to obtain plentiful features. Then, Dirichlet process mixture model is used to generate hierarchical global prior distribution and local seeds, which substitute manual scribbles. Moreover, the proposed model can adapt to different resolution segmentation tasks through adjusting the concentration parameter. Final segmentation output is obtained by biased RW. Experimental results on urban high-resolution remote sensing images demonstrate that the proposed algorithm achieves better performance than other state-of-the-art algorithms.
      PubDate: May 2019
      Issue No: Vol. 12, No. 5 (2019)
  • Hyper-Sharpening Based on Spectral Modulation
    • Authors: Xiaochen Lu;Junping Zhang;Xiangzhen Yu;Wenming Tang;Tong Li;Ye Zhang;
      Pages: 1534 - 1548
      Abstract: Hyperspectral (HS) image sharpening (namely hyper sharpening) with an auxiliary sensor, such as multispectral (MS) or panchromatic sensor, has attracted a great deal of attention for the past decade. A number of hyper-sharpening techniques, aiming at enhancing the spatial resolution of HS images, have been developed and demonstrated their effectiveness especially on synthetic or simulated data. Nevertheless, since the differences between different remote sensing systems or imaging conditions are complicate, it results in a serious spectral distortion when applying on real remote sensing data acquired by different sensors under different acquisition times or conditions. Unfortunately, very few works have considered this issue. In this paper, a new hyper-sharpening framework based on spectral modulation is proposed to better preserve spectral information when fusing with MS data acquired by a different sensor. The goal of this paper is to generate an adjusted MS image that would have been observed under the same imaging conditions with the corresponding HS sensor. Two approaches, originating in MS pan-sharpening field, are introduced as examples under this framework, namely high-pass details injection model and band-dependent spatial-detail model. Experiments on three HS and MS datasets acquired by different platforms demonstrate that the proposed framework is beneficial to the spectral fidelity of the fused image compared with some state-of-the-art hyper-sharpening techniques. Meanwhile, it is also easy to implement and has a certain advantage in enhancing the spatial details.
      PubDate: May 2019
      Issue No: Vol. 12, No. 5 (2019)
  • Pyramid Fully Convolutional Network for Hyperspectral and Multispectral
           Image Fusion
    • Authors: Feng Zhou;Renlong Hang;Qingshan Liu;Xiaotong Yuan;
      Pages: 1549 - 1558
      Abstract: Low spatial resolution hyperspectral (LRHS) and high spatial resolution multispectral (HRMS) image fusion has been recognized as an important technology for enhancing the spatial resolution of LRHS image. Recent advances in convolutional neural network have improved the performance of state-of-the-art fusion methods. However, it is still a challenging problem to effectively explore the spatial information of HRMS image. In this paper, we propose a pyramid fully convolutional network made up of an encoder sub-network and a pyramid fusion sub-network to address this issue. Specifically, the encoder sub-network aims to encode the LRHS image into a latent image. Then, this latent image, together with a HRMS image pyramid input, is used to progressively reconstruct the high spatial resolution hyperspectral image in a global-to-local manner. Furthermore, to sharpen the blurry predictions easily obtained by the standard l2 loss, we introduce the gradient difference loss as a regularization term. We evaluate the proposed method on three datasets acquired by three different satellite sensors. Experimental results demonstrate that the proposed method achieves better performance than several state-of-the-art methods.
      PubDate: May 2019
      Issue No: Vol. 12, No. 5 (2019)
  • Estimation of Forest Canopy Height in Hilly Areas Using Lidar Waveform
    • Authors: Lixin Dong;Shihao Tang;Min Min;Frank Veroustraete;
      Pages: 1559 - 1571
      Abstract: Forest canopy height (FCH) is a key parameter in the estimation of forest biomass and productivity. However, areas with hilly or mountainous terrain present a genuine challenge to extract the vertical structural parameters by using the large footprint Lidar full waveform data. In this study, a mathematical method based on the inflection point of Lidar waveform is developed and applied to process geoscience laser altimeter system data. Furthermore, an improved model, the centroid-terrain index model (CTIM), is proposed to estimate FCH of different forest types in hilly areas. The accuracy of the CTIM model is evaluated by using different field measurement data collected from multiple forest districts in China. For conifer and broadleaf forests, the RMSE is 3.8 m in areas with slope angles larger than 5°. Compared to the ground-based Lidar data, the accuracy is satisfactory in hilly areas. The proposed approach makes a significant contribution toward improving the FCH estimation in hilly areas from large footprint full waveform data, and toward the forest ecosystem monitoring at the global scale.
      PubDate: May 2019
      Issue No: Vol. 12, No. 5 (2019)
  • Generation of Horizontally Curved Driving Lines in HD Maps Using Mobile
           Laser Scanning Point Clouds
    • Authors: Lingfei Ma;Ying Li;Jonathan Li;Zilong Zhong;Michael A. Chapman;
      Pages: 1572 - 1586
      Abstract: This paper presents the development of a semiautomated driving line generation method using point clouds acquired by a mobile laser scanning system. Horizontally curved driving lines are a critical component for high-definition maps that are required by autonomous vehicles. The proposed method consists of three steps: Road surface extraction, road marking extraction, and driving line generation. First, the points covering road surfaces are extracted using the curb-based road surface extraction algorithms depending on both the elevation and slope differences. Then, road markings are identified and extracted according to a variety of algorithms consisting of georeferenced intensity imagery generation, multithreshold road marking extraction, and statistical outlier removal. Finally, the conditional Euclidean clustering algorithm is employed, followed by the cubic spline curve-fitting algorithm and equidistant line-based driving line generation algorithms for horizontally curved driving line generation. Our method is evaluated by six MLS point cloud datasets collected from various types of horizontally curved road corridors. Quantitative evaluations demonstrate that the proposed road marking extraction algorithm achieves an average recall, precision, and F1-score of 90.79%, 92.94%, and 91.85%, respectively. The generated driving lines are assessed by overlaying them on the manually interpreted reference buffers from 4-cm resolution unmanned aerial vehicle orthoimagery, and a 15 cm level navigation and localization accuracy is achieved.
      PubDate: May 2019
      Issue No: Vol. 12, No. 5 (2019)
  • A Real-Time Precipitable Water Vapor Monitoring System Using the National
           GNSS Network of China: Method and Preliminary Results
    • Authors: Hongxing Zhang;Yunbin Yuan;Wei Li;Baocheng Zhang;
      Pages: 1587 - 1598
      Abstract: The development of the International Global Navigation Satellite System (GNSS) Service (IGS) Real-Time Pilot Project shows promise for real-time GNSS-based precipitable water vapor (GNSS-PWV) retrieval. For better applications of real-time GNSS meteorology over China, a method is proposed to establish a real-time GNSS-PWV monitoring system using the national GNSS network of China. The prototype system generating the zenith tropospheric delay (ZTD) is based on the real-time precise point positioning technique, in which the real-time data streams and state-space-representative satellite orbit and clock corrections are processed. The parallel computing technology is embedded in the system for decoding and processing the real-time data streams, which enables the multistation processing mode and improves the computing efficiency. At the initial phase, a total of 215 global positioning system (GPS) stations from the crustal movement observation network of China are used to generate the real-time GPS-ZTDs. The pressure data for separating the zenith wet delay (ZWD) from the ZTD are obtained from the collocated meteorological sensors. An advanced weighted mean temperature model, namely, Gridded-Mixed Tm, is adopted in the system to determine the conversion factor “Π” (converting GPS-ZWD to GPS-PWV) in real-time mode with the measured temperatures input. The generated real-time GPS-PWV products have a time resolution of 5 min. To validate the system performance, comparisons between the real-time GPS-PWV and the PWVs derived from nearby radiosonde data (RS-PWV) and reanalysis data from the National Centers for Environmental Prediction (NCEP)-Department of Energy Reanalysis II (NCEP-II-PWV) are conducted over a period of 83 days. The comparisons show a mean bias of -0.1 mm with a root mean square (RMS) of 1.7 mm between the real-time GPS-PWV and RS-PWV. The agreement between the real-time GPS-PWV and NCEP-II-PWV is appr-ximately 2.0 mm in terms of RMS and has a mean bias of -0.8 mm. These results confirm that the established system can be used for real-time PWV monitoring across China.
      PubDate: May 2019
      Issue No: Vol. 12, No. 5 (2019)
  • Response of GNSS-R on Dynamic Vegetated Terrain Conditions
    • Authors: Orhan Eroglu;Mehmet Kurum;John Ball;
      Pages: 1599 - 1611
      Abstract: Global navigation satellite system reflectometry (GNSS-R) has the potential to offer a cost-effective solution for global land observations. In this study, we aim to understand GNSS-R sensitivity to changing land geophysical parameters. For this objective, we performed simulations of a ground-based receiver using a recently developed coherent bistatic vegetation scattering model (SCoBi-Veg) to detect GNSS-R signatures under varying soil moisture (SM), vegetation water content (VWC), and surface roughness during a full corn growing season. We modeled different corn growth stages by using in situ measurement data. We analyzed the simulated reflectivity and received power values based on the aforementioned variable input parameters. This study demonstrates that specular reflections dominate the diffusely scattered contribution in case of moderate roughness, regardless of the corn field row structure or the polarization. Significant correlations between VWC and cross-polarized reflectivity values are also shown. Furthermore, the study quantifies the effects of SM and surface roughness on GNSS-R deliverables.
      PubDate: May 2019
      Issue No: Vol. 12, No. 5 (2019)
  • Seismic Random Noise Attenuation Using Sparse Low-Rank Estimation of the
           Signal in the Time–Frequency Domain
    • Authors: Rasoul Anvari;Amin Roshandel Kahoo;Mokhtar Mohammadi;Nabeel Ali Khan;Yangkang Chen;
      Pages: 1612 - 1618
      Abstract: Suppression of random noise in seismic data is a challenging preprocessing task. We propose a new denoising method, which includes the following steps. First, the short-time Fourier transform of the noisy seismic signal is computed. Then, a sparse low-rank matrix is estimated based on solving a nonconvex penalty function. A threshold function associated with the penalty function based on the optimal shrinkage of the singular values is employed to solve the penalty function. The nonconvex penalty function induces sparsity to the time-frequency (TF) matrix, and the optimal shrinkage optimizes the thresholding function to extract the singular values. Finally, the seismic signal is synthesized based on the estimated sparse low-rank TF matrix. We evaluate the proposed method by using a synthetic seismic section contaminated by random noise and a prestack real seismic dataset from an oil field in the southwest of Iran. The synchrosqueezed wavelet transform-OptShrink, synchrosqueezed wavelet transform-Go Decomposition, and classical f-x singular spectrum analysis methods are selected to compare the performance of the proposed method. The results indicate the superiority of the proposed method compared to the other state-of-the-art noise suppression methods.
      PubDate: May 2019
      Issue No: Vol. 12, No. 5 (2019)
  • Shared Nearest Neighborhood Intensity Based Declustering Model for
           Analysis of Spatio-Temporal Seismicity
    • Authors: Rahul Kumar Vijay;Satyasai Jagannath Nanda;
      Pages: 1619 - 1627
      Abstract: Categorization of seismic events into correlated aftershocks (triggered by the mainshocks) and independent backgrounds (generated due to regular movements of tectonic plates) is essential to carry out reliable hazard analysis in a region of interest. In this paper, a shared nearest neighborhood intensity based declustering (SNN-IBD) model is proposed to categorize seismic events based on their magnitude, event location, and occurrence time. In this approach, events which lie within a spatial-cutoff (ϵs) and temporal-cutoff (ϵt) are considered as core points. Instead of using density (a significant number of events) in a space-time window, intensity (magnitude) indicated by core points is considered here in order to discover aftershock clusters. Effective selection of cutoffs (ϵs, ϵt, intensity/magnitude threshold SM) and classification accuracy in spatio-temporal domain are validated using statistical parameters: Coefficient of Variation (COV) and m-Morisita index. The regional earthquake catalogs of the Philippines (1973- 2012) and Iran (1966-2015) are analyzed using the proposed model. From the simulation studies, it is observed that background seismicity follows a homogeneous Poisson process in the time domain. In the spatial domain, background seismicity reflects multifractal behavior similar to true events of the catalog. The superior performance of the proposed method is demonstrated over tetra-stage clustering model and benchmark declustering algorithms.
      PubDate: May 2019
      Issue No: Vol. 12, No. 5 (2019)
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    • Pages: 1628 - 1628
      Abstract: Advertisement, IEEE.
      PubDate: May 2019
      Issue No: Vol. 12, No. 5 (2019)
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