for Journals by Title or ISSN
for Articles by Keywords
help

 A  B  C  D  E  F  G  H  I  J  K  L  M  N  O  P  Q  R  S  T  U  V  W  X  Y  Z  

       | Last   [Sort by number of followers]   [Restore default list]

  Subjects -> ELECTRONICS (Total: 152 journals)
Showing 1 - 200 of 277 Journals sorted alphabetically
Advances in Biosensors and Bioelectronics     Open Access   (Followers: 6)
Advances in Electrical and Electronic Engineering     Open Access   (Followers: 2)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 9)
Advances in Microelectronic Engineering     Open Access   (Followers: 13)
Advances in Power Electronics     Open Access   (Followers: 27)
Aerospace and Electronic Systems, IEEE Transactions on     Hybrid Journal   (Followers: 236)
American Journal of Electrical and Electronic Engineering     Open Access   (Followers: 24)
Annals of Telecommunications     Hybrid Journal   (Followers: 7)
Archives of Electrical Engineering     Open Access   (Followers: 12)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 8)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 23)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 17)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 32)
Biomedical Instrumentation & Technology     Hybrid Journal   (Followers: 6)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 10)
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  
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 43)
China Communications     Full-text available via subscription   (Followers: 7)
Circuits and Systems     Open Access   (Followers: 16)
Consumer Electronics Times     Open Access   (Followers: 6)
Control Systems     Hybrid Journal   (Followers: 192)
Edu Elektrika Journal     Open Access  
Electronic Design     Partially Free   (Followers: 83)
Electronic Markets     Hybrid Journal   (Followers: 8)
Electronic Materials Letters     Hybrid Journal   (Followers: 3)
Electronics     Open Access   (Followers: 67)
Electronics and Communications in Japan     Hybrid Journal   (Followers: 9)
Electronics For You     Partially Free   (Followers: 68)
Electronics Letters     Hybrid Journal   (Followers: 26)
Embedded Systems Letters, IEEE     Hybrid Journal   (Followers: 42)
Energy Harvesting and Systems : Materials, Mechanisms, Circuits and Storage     Hybrid Journal   (Followers: 4)
Energy Storage Materials     Full-text available via subscription   (Followers: 2)
EPJ Quantum Technology     Open Access  
EURASIP Journal on Embedded Systems     Open Access   (Followers: 12)
Facta Universitatis, Series : Electronics and Energetics     Open Access  
Foundations and Trends® in Communications and Information Theory     Full-text available via subscription   (Followers: 7)
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: 163)
Haptics, IEEE Transactions on     Hybrid Journal   (Followers: 3)
IEEE Antennas and Propagation Magazine     Hybrid Journal   (Followers: 81)
IEEE Antennas and Wireless Propagation Letters     Hybrid Journal   (Followers: 68)
IEEE Journal of Emerging and Selected Topics in Power Electronics     Hybrid Journal   (Followers: 35)
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: 52)
IEEE Transactions on Antennas and Propagation     Full-text available via subscription   (Followers: 59)
IEEE Transactions on Automatic Control     Hybrid Journal   (Followers: 53)
IEEE Transactions on Circuits and Systems for Video Technology     Hybrid Journal   (Followers: 17)
IEEE Transactions on Consumer Electronics     Hybrid Journal   (Followers: 36)
IEEE Transactions on Electron Devices     Hybrid Journal   (Followers: 16)
IEEE Transactions on Information Theory     Hybrid Journal   (Followers: 27)
IEEE Transactions on Power Electronics     Hybrid Journal   (Followers: 60)
IEEE Transactions on Signal and Information Processing over Networks     Full-text available via subscription   (Followers: 9)
IEICE - Transactions on Electronics     Full-text available via subscription   (Followers: 15)
IEICE - Transactions on Information and Systems     Full-text available via subscription   (Followers: 7)
IET Microwaves, Antennas & Propagation     Hybrid Journal   (Followers: 32)
IET Power Electronics     Hybrid Journal   (Followers: 33)
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: 11)
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)     Open Access  
Industrial Electronics, IEEE Transactions on     Hybrid Journal   (Followers: 42)
Industry Applications, IEEE Transactions on     Hybrid Journal   (Followers: 11)
Informatik-Spektrum     Hybrid Journal   (Followers: 2)
Instabilities in Silicon Devices     Full-text available via subscription  
Intelligent Transportation Systems Magazine, IEEE     Full-text available via subscription   (Followers: 8)
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: 8)
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: 4)
International Journal of Computer & Electronics Research     Full-text available via subscription   (Followers: 2)
International Journal of Control     Hybrid Journal   (Followers: 13)
International Journal of Electronics     Hybrid Journal   (Followers: 6)
International Journal of Electronics & Data Communication     Open Access   (Followers: 11)
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 Image, Graphics and Signal Processing     Open Access   (Followers: 10)
International Journal of Nano Devices, Sensors and Systems     Open Access   (Followers: 8)
International Journal of Nanoscience     Hybrid Journal   (Followers: 1)
International Journal of Numerical Modelling: Electronic Networks, Devices and Fields     Hybrid Journal   (Followers: 3)
International Journal of Power Electronics     Hybrid Journal   (Followers: 16)
International Journal of Sensors, Wireless Communications and Control     Hybrid Journal   (Followers: 8)
International Journal of Systems, Control and Communications     Hybrid Journal   (Followers: 3)
International Journal of Wireless and Microwave Technologies     Open Access   (Followers: 6)
International Journal on Communication     Full-text available via subscription   (Followers: 14)
International Journal on Electrical and Power Engineering     Full-text available via subscription   (Followers: 8)
International Transaction of Electrical and Computer Engineers System     Open Access   (Followers: 2)
Journal of Biosensors & Bioelectronics     Open Access   (Followers: 3)
Journal of Advanced Dielectrics     Open Access   (Followers: 1)
Journal of Artificial Intelligence     Open Access   (Followers: 9)
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: 19)
Journal of Electrical Bioimpedance     Open Access   (Followers: 2)
Journal of Electrical Engineering & Electronic Technology     Hybrid Journal   (Followers: 7)
Journal of Electromagnetic Analysis and Applications     Open Access   (Followers: 7)
Journal of Electromagnetic Waves and Applications     Hybrid Journal   (Followers: 6)
Journal of Electronics (China)     Hybrid Journal   (Followers: 4)
Journal of Energy Storage     Full-text available via subscription   (Followers: 3)
Journal of Field Robotics     Hybrid Journal   (Followers: 2)
Journal of Guidance, Control, and Dynamics     Full-text available via subscription   (Followers: 138)
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: 7)
Journal of Microwaves, Optoelectronics and Electromagnetic Applications     Open Access   (Followers: 9)
Journal of Nuclear Cardiology     Hybrid Journal  
Journal of Optoelectronics Engineering     Open Access   (Followers: 2)
Journal of Physics B: Atomic, Molecular and Optical Physics     Hybrid Journal   (Followers: 31)
Journal of Semiconductors     Full-text available via subscription   (Followers: 4)
Journal of Sensors     Open Access   (Followers: 24)
Journal of Signal and Information Processing     Open Access   (Followers: 9)
Jurnal Rekayasa Elektrika     Open Access  
Learning Technologies, IEEE Transactions on     Hybrid Journal   (Followers: 14)
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: 16)
Nanotechnology Magazine, IEEE     Full-text available via subscription   (Followers: 31)
Nanotechnology, Science and Applications     Open Access   (Followers: 6)
Networks: an International Journal     Hybrid Journal   (Followers: 5)
Open Journal of Antennas and Propagation     Open Access   (Followers: 8)
Optical Communications and Networking, IEEE/OSA Journal of     Full-text available via subscription   (Followers: 14)
Paladyn, Journal of Behavioral Robotics     Open Access   (Followers: 1)
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 Patents on Electrical & Electronic Engineering     Full-text available via subscription   (Followers: 9)
Recent Patents on Telecommunications     Full-text available via subscription   (Followers: 2)
Security and Communication Networks     Hybrid Journal   (Followers: 3)
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of     Hybrid Journal   (Followers: 51)
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: 5)
Software Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 72)
Solid-State Circuits Magazine, IEEE     Hybrid Journal   (Followers: 12)
Solid-State Electronics     Hybrid Journal   (Followers: 8)
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  
Telematique     Open Access  
TELKOMNIKA (Telecommunication, Computing, Electronics and Control)     Open Access   (Followers: 8)
Universal Journal of Electrical and Electronic Engineering     Open Access   (Followers: 6)
Visión Electrónica : algo más que un estado sólido     Open Access  
Wireless and Mobile Technologies     Open Access   (Followers: 5)
Women in Engineering Magazine, IEEE     Full-text available via subscription   (Followers: 13)
Електротехніка і Електромеханіка     Open Access  

       | Last   [Sort by number of followers]   [Restore default list]

Journal Cover Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of
  [SJR: 1.196]   [H-I: 37]   [51 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1939-1404
   Published by IEEE Homepage  [191 journals]
  • [Front cover]
    • Abstract: Presents the front cover for this issue of the publication.
      PubDate: Nov. 2017
      Issue No: Vol. 10, No. 11 (2017)
       
  • IEEE Geoscience and Remote Sensing Society
    • Abstract: Provides a listing of current staff, committee members and society officers.
      PubDate: Nov. 2017
      Issue No: Vol. 10, No. 11 (2017)
       
  • IEEE Geoscience and Remote Sensing Society
    • Abstract: Provides a listing of current committee members and society officers.
      PubDate: Nov. 2017
      Issue No: Vol. 10, No. 11 (2017)
       
  • Institutional listings
    • Abstract: Advertisements.
      PubDate: Nov. 2017
      Issue No: Vol. 10, No. 11 (2017)
       
  • Foreword to the Special Issue on Modeling and Simulation of Remote Sensing
           Data
    • Authors: J. P. Kerekes;J. Shi;L. Tsang;J.-P. Gastellu-Etchegorry;
      Pages: 4663 - 4665
      Abstract: The papers in this special issue focus on the deployment modeling and simulation in remote sensing applications which play important roles in the development and application of remote sensing technology. While modeling occurs in many aspects of remote sensing including in the representation of natural phenomena and in the development and use of data analysis algorithms, a particular role of models is in the creation of the signatures and signals that lead to the remotely sensed measurements from which we can extract information about the scene.
      PubDate: Nov. 2017
      Issue No: Vol. 10, No. 11 (2017)
       
  • On the Simulation of Complex Visibilities in Imaging Radiometry by
           Aperture Synthesis
    • Authors: Eric Anterrieu;François Cabot;Ali Khazaal;Yann H. Kerr;
      Pages: 4666 - 4676
      Abstract: The basic observables of an imaging interferometer by aperture synthesis are the complex visibilities. Under some conditions, they can be simulated with the aid of the van Cittert–Zernike theorem. However, owing to underlying assumptions, some important effects that may alter them cannot be taken into account. This paper is devoted to the numerical simulation of complex visibilities without any reference to the van Cittert–Zernike theorem, in such a way that these effects can be taken into account. The emission from an extended source is modeled using a linear superposition of random waves emitted by a collection of point sources, which are all assumed to behave like black bodies at thermal equilibrium. These random waves are numerically generated with the aid of white noises filtered in such a way that their power spectral densities follow the shape of Planck distributions at the temperature of the point sources over a wide range of frequencies. The radio signal is then transported to the antennas, where the voltage patterns are taken into account as well as the filters response of the bandpass receivers. It is, therefore, sent to the correlator unit for being cross-correlated. From emission to correlation, perturbing effects can be introduced at any time. To illustrate this modeling method, numerical simulations are carried out in the L-band around 1413.5 MHz in reference to the SMOS-next project led by the French Space Agency. The results are discussed and compared with the estimates provided by the van Cittert–Zernike theorem. Owing to the amount of calculations to be performed, massive parallel architectures like that found in GPU have been required.
      PubDate: Nov. 2017
      Issue No: Vol. 10, No. 11 (2017)
       
  • A Study of Wind Direction Effects on Sea Surface Specular Scattering for
           GNSS-R Applications
    • Authors: Jeonghwan Park;Joel T. Johnson;
      Pages: 4677 - 4685
      Abstract: A modeling study investigating the influence of wind direction on spaceborne global navigation satellite system reflectometry (GNSS-R) near-specular observations of the sea surface is reported. The study first focuses on a purely specular geometry under plane wave incidence, for which it is shown using the theorem of reciprocity and reflection symmetry that up-down wind variations are identically zero. It is also shown that “single-scattering” approximations of rough surface scattering predict no variations with wind direction of any kind for a purely specular geometry, while higher order approximations (such as the second-order small-slope approximation) can predict up/cross wind differences. Examples of these variations are reported and found to be small. Because the delay doppler maps (DDMs) measured in GNSS-R include some nonspecular contributions even for “specular” portions of the DDM, the second part of the study performs an examination of near-specular DDM variations with wind direction using the widely used geometrical optics approximation of surface scattering for a surface described with the non-Gaussian Cox–Munk slope probability density function. Variations with wind direction of the normalized radar cross section (NRCS) mapped onto the surface are examined, and again, it is shown that these variations are small for surface portions contributing to the near-specular portion of the DDM. In addition, it is shown that the dependencies of the bistatic NRCS on wind direction are such that differing portions of the surface “glistening zone” have differing phase shifts in their dependence on wind direction, causing the wind dependencies of the final near-specular DDM to be negligible. The final results of the study suggest that any wind direction dependence in spaceborne GNSS-R should be sought only in portions of the DDM away f-om the specular region. These results provide information to guide analyses of the wind direction information available in current GNSS-R missions such as TDS-1 and Cyclone Global Navigation Satellite System.
      PubDate: Nov. 2017
      Issue No: Vol. 10, No. 11 (2017)
       
  • Microwave Signatures of Snow Cover Using Numerical Maxwell Equations Based
           on Discrete Dipole Approximation in Bicontinuous Media and Half-Space
           Dyadic Green's Function
    • Authors: Shurun Tan;Jiyue Zhu;Leung Tsang;Son V. Nghiem;
      Pages: 4686 - 4702
      Abstract: A three-dimensional snowpack scattering and emission model is developed by numerically solving Maxwell's equations (NMM3D) over the entire snowpack on an underlying half-space. The solutions are crucial to microwave remote sensing that requires the preservation of coherent phase information. The heterogeneous snowpack is represented as a bicontinuous medium. Effects of the underlying half-space are included through a half-space Green's function in a volume integral equation formulation. The volume integral equation is then solved using the discrete dipole approximation. The fast Fourier transform is effectuated for all three dimensions with half-space Green's function rather than the conventional free space Green's function. To overcome the snow volume truncation in the finite numerical calculations, periodic boundary conditions are applied in the lateral extent. Thus, in NMM3D, the physical microwave scattering and emission problem is solved without using any radiative transfer equations. In this formulation, the scattering matrix of the snowpack accounts for both the magnitude and phase. The NMM3D simulations are demonstrated at Ku-band frequency for a snow cover up to 25-cm thick. The results are applicable to remote sensing of snow over sea ice, and thin layers of terrestrial snow. Quantitative values of backscattering and bistatic scattering coefficients are derived for active microwave remote sensing, and brightness temperatures for passive microwave remote sensing. The full wave simulation results are compared with those of the partially coherent approach of the dense media radiative transfer (DMRT). The NMM3D results capture effects of backscattering enhancement and coherent layering that are missed in DMRT. The full wave solution to Maxwell equations is important to advance radar polarimetry, interferometry, and tomography that require the preservation of the full -hase information and all interface interactions for applications to radar remote sensing of snow cover on land and on sea ice.
      PubDate: Nov. 2017
      Issue No: Vol. 10, No. 11 (2017)
       
  • Rough Surface and Volume Scattering of Soil Surfaces, Ocean Surfaces,
           Snow, and Vegetation Based on Numerical Maxwell Model of 3-D Simulations
    • Authors: Leung Tsang;Tien-Hao Liao;Shurun Tan;Huanting Huang;Tai Qiao;Kung-Hau Ding;
      Pages: 4703 - 4720
      Abstract: In this paper, we give an overview and an update on the recent progress of our research group in numerical model of Maxwell equations in three dimensions (NMM3D) on random rough surfaces and discrete random media and their applications in active and passive microwave remote sensing. The random rough surface models were applied to soil surfaces and ocean surfaces. The discrete random media models were applied to snow and vegetation. For rough surface scattering, we use the surface integral equations of Poggio–Miller–Chang–Harrington–Wu–Tsai that are solved by the method of moments using the Rao–Wilton–Glisson basis functions. The sparse matrix canonical grid method is used to accelerate the matrix column multiplications. In modeling the rough surfaces, we use the exponential correlation functions for soil surfaces and the Durden–Vesecky ocean spectrum for ocean surfaces. In scattering by terrestrial snow and snow on sea ice, we use the volume integral equations formulated with the dyadic half-space Green's function. The microstructure of snow is modeled by the bicontinuous media. In scattering by vegetation, we use the discrete scatterers of cylinder. The NMM3D formulation is based on the Foldy–Lax multiple scattering equations in conjunction with the body of revolution for a single scatterer. For rough surface scattering, simulations results are compared with advanced integral equation model, small slope approximation, small perturbation method, and two scale model. For volume scattering by snow, results are compared with the bicontinuous dense media radiative transfer. For scattering by vegetation, results are compared with distorted Born approximation and radiative transfer equation. Comparisons are also made with experiments.
      PubDate: Nov. 2017
      Issue No: Vol. 10, No. 11 (2017)
       
  • Bistatic Coherent Polarimetric Scattering of Randomly Corrugated Layered
           Snow Surfaces
    • Authors: Yu Liu;Kun-Shan Chen;Peng Xu;Zhao-Liang Li;
      Pages: 4721 - 4739
      Abstract: We analyzed the bistatic coherent scattering mechanism of a layered randomly corrugated snow surface, a typical rough surface, with radar polarimetry theory whose scattering matrix was obtained from a physical-based full wave numerical simulation by solving Maxwell's equations. The effects of top–bottom structure, layer thickness, frequency response, and angular dependence are illustrated by observing stokes vector, coherence matrix, and Kennaugh matrix. The results show that top–bottom structure and snow thickness change the state of polarization depending on frequency and bistatic configuration. Analyzing the bistatic polarimetric scattering mechanism based on numerical simulation and the polarimetry theory can be an efficacious source for configuring bistatic observation to detect and classify radar targets. For example, observation at a specular angle of 55° comparatively contains more information on surface structure, and wave entropy is more preferable over degree of polarization as a snow surface structure estimator. Moreover, parameters from Kennaugh decomposition can indicate top–bottom structure better than layer thickness. Last but not the least, we also found that the symmetry assumption commonly used in classical theory of polarization is generally not valid for bistatic observation, and the combination of some Huynen parameters can be reasonably good indicators of snow surface structural symmetry. We expect this paper to offer deeper understanding of the coherent imaging of snow surfaces and to help design a novel bistatic imaging system for layered snow surface.
      PubDate: Nov. 2017
      Issue No: Vol. 10, No. 11 (2017)
       
  • Depolarized Backscattering of Rough Surface by AIEM Model
    • Authors: Ying Yang;Kun-Shan Chen;Leung Tsang;Liu Yu;
      Pages: 4740 - 4752
      Abstract: This paper presents a new expression for multiple scattering by including the upward and downward propagation waves in the medium 1 and medium 2. Unlike the single scattering, the multiple scattering accounts for the interactions, up to second order, among all the spectral components of surface roughness spectrum. Though the derivation is mathematically intricate, but yet manageable, the final expression is compact and easy for numerical implementation, which only involves a series of two-dimensional integration. Some of special cases in depolarized backscattering are also derived and compared with known analytical model to partly validate the update AIEM model. Then, extensive comparisons with numerical simulations and field measurements are conducted to illustrate the model accuracy.
      PubDate: Nov. 2017
      Issue No: Vol. 10, No. 11 (2017)
       
  • Modeling L-Band Synthetic Aperture Radar Data Through Dielectric Changes
           in Soil Moisture and Vegetation Over Shrublands
    • Authors: Seung-bum Kim;Motofumi Arii;Thomas Jackson;
      Pages: 4753 - 4762
      Abstract: L-band airborne synthetic aperture radar observations were made over California shrublands to better understand the effects of soil and vegetation parameters on backscattering coefficient $(sigma ^{0})$. Temporal changes in $sigma ^{0}$ of up to 3 dB were highly correlated to surface soil moisture but not to vegetation, even though vegetation water content (VWC) varied seasonally by a factor of two. HH was always greater than VV, suggesting the importance of double-bounce scattering by the woody parts. However, the geometric and dielectric properties of the woody parts did not vary significantly over time. Instead the changes in VWC occurred primarily in thin leaves that may not meaningfully influence absorption and scattering. A physically based model for single scattering by discrete elements of plants successfully simulated the magnitude of the temporal variations in HH, VV, and HH/VV with a difference of less than 0.9 dB for both the mean and standard deviation when compared with the airborne data. In order to simulate the observations, the VWC input of the plant to the model was formulated as a function of plant's dielectric property (water fraction) while the plant geometry remains static in time. In comparison, when the VWC input was characterized by the geometry of a growing plant, the model performed poorly in describing the observed patterns in the $sigma ^{0}$ changes. The modeling results offer explanation of the observation that soil moisture correlated highly with $sigma ^{0}$: the dominant mechanisms for HH and VV are double-bounce scattering by trunk, and soil surface scattering, respectively. The time-series-inversion of the physical model was able to retrieve soil moisture with the difference of $- {text{0.037}}, text{m}^{3}{/ text{m}}^{3}$ (mean), ${text{0.025}}, text{m}^{3}{/ text{m}}^{3}$ (standard deviation), and 0.89 (correlation), which demonstrates the efficacy of the model-based time-series soil moisture retrieval for shrublands.
      PubDate: Nov. 2017
      Issue No: Vol. 10, No. 11 (2017)
       
  • A 3-D Joint Simulation Platform for Multiband Remote Sensing
    • Authors: Yang Zhang;Qinhuo Liu;Longfei Tan;Huaguo Huang;Wenjian Ni;Tiangang Yin;Wenhan Qin;Guoqing Sun;
      Pages: 4763 - 4778
      Abstract: Canopy radiation and scattering signals contain abundant vegetation information. Many biophysical parameters can be quantitatively retrieved with the help of canopy radiation and scattering models. Joint simulation of three-dimensional (3-D) models for multiband that combines the advantages of different spectral (frequency) domains could be a useful tool for validation in remote sensing. This manuscript presents a 3-D joint simulation platform (3-DMultiSim) that simulates spectral responses from visible to microwave bands. We validated our platform with the corn field experimental data at the Huailai testing site of the Chinese Academy of Sciences. The correlation coefficients between the validation data and the simulation results were higher than 0.92, while the relative mean deviation was 15%. For the thermal infrared band, the correlation coefficient was 0.91, but the variation of the simulated directional bright temperature from 2 π space was less than 0.4 °C. The reason may be due to the model limitation at high leaf area index (LAI). For the microwave bands, the simulation data and the validation data had the best consistency at L band, whereas the same trend but bigger deviation at X - and C-band. As an application of the platform, we performed sensitivity analyses of the radiation and scattering responses to LAI and incident-observation geometries at multiband. The simulation results were analyzed quantitatively. Further applications of the joint simulation platform are proposed.
      PubDate: Nov. 2017
      Issue No: Vol. 10, No. 11 (2017)
       
  • Simulation-Based Interpretation and Alignment of High-Resolution Optical
           and SAR Images
    • Authors: Stefan Auer;Isabel Hornig;Michael Schmitt;Peter Reinartz;
      Pages: 4779 - 4793
      Abstract: The successful alignment of optical and synthetic aperture radar (SAR) satellite data requires that we account for the effects of sensor-specific geometric distortion, which is a consequence of the different imaging concepts of the sensors. This paper introduces SimGeoI, a simulation framework for the object-related interpretation of optical and SAR images, as a solution to this problem. Using metainformation from the images and a digital surface model as input, the processor follows the steps of scene definition, ray tracing, image generation, geocoding, interpretation layer generation, and image part extraction. Thereby, for the first time, object-related sections of optical and SAR images are automatically identified and extracted in world coordinates under consideration of three-dimensional object shapes. A case study for urban scenes in Munich and London, based on WorldView-2 images and high-resolution TerraSAR-X data, confirms the potential of SimGeoI in the context of a perspective-independent and object-focused analysis of high-resolution satellite data.
      PubDate: Nov. 2017
      Issue No: Vol. 10, No. 11 (2017)
       
  • Overview of Passive Optical Multispectral and Hyperspectral Image
           Simulation Techniques
    • Authors: Sanghui Han;John P. Kerekes;
      Pages: 4794 - 4804
      Abstract: The simulation of optical images can play key roles in the development of new instruments, the quantitative evaluation of algorithms and in the training of both image analysis software and human analysts. Methods for image simulation include surrogate data collections, operations on empirical imagery, statistical generation techniques, and full physical modeling approaches. Each method offers advantages or disadvantages in terms of time, cost, and realism. Current state of the art suggests three-dimensional radiative transfer models capture most of the significant characteristics of real imagery and find valuable use in system development and evaluation programs. Emerging computational power available from multithreading, graphical processing units, and techniques from deep learning will continue to enable even more realistic simulations in the near future.
      PubDate: Nov. 2017
      Issue No: Vol. 10, No. 11 (2017)
       
  • Modeling and Simulation of Deciduous Forest Canopy and Its Anisotropic
           Reflectance Properties Using the Digital Image and Remote Sensing Image
           Generation (DIRSIG) Tool
    • Authors: Rajagopalan Rengarajan;John R. Schott;
      Pages: 4805 - 4817
      Abstract: Extraction of biophysical information from forest canopies using temporal analysis of multispectral and hyperspectral data can be significantly improved by understanding its anisotropic reflectance properties. However, limitations on the accessibility and data collection techniques in the field reduce the availability of high-resolution bidirectional reflectance measurements (BRDF) to a few datasets. These limitations can be mitigated in a virtual environment and this paper presents an approach to model the spectral BRDF of a forest canopy using the Digital Image and Remote Sensing Image Generation (DIRSIG) tool. The three-dimensional geometries of the trees were modeled using forest inventory data and OnyxTree, while the spectral properties of the geometric elements were assigned based on the field collected spectra and PROSPECT inversion model. The DIRSIG tool was used as a virtual goniometer to measure the BRDF observations for varying sun-view geometries and a full hemispherical BRDF model was constructed by fitting the measurements to a semiempirical BRDF model. This paper discusses the methods involved in modeling the forest canopy scene, sensitivity of the radiative transfer, BRDF sampling and modeling strategies, model accuracy and its effect on real-world simulations. The model fit results indicate a root mean square error of less than 5% relative to the forests reflectance in the VIS-NIR-SWIR region. The simulated BRDF matched to within 2% of the Landsat-8 surface reflectance product in the red and NIR bands. The results can be used directly to evaluate BRDF modeling algorithms and the proposed method can be easily extended for other biomes.
      PubDate: Nov. 2017
      Issue No: Vol. 10, No. 11 (2017)
       
  • DIRSIG5: Next-Generation Remote Sensing Data and Image Simulation
           Framework
    • Authors: Adam A. Goodenough;Scott D. Brown;
      Pages: 4818 - 4833
      Abstract: The digital imaging and remote sensing image generation model is a physics-based image and data simulation model that is primarily used to generate synthetic imagery across the visible to thermal infrared regions using engineering-driven descriptions of remote sensing systems. The model recently went through a major redesign and reimplementation effort to address changes in user requirements and numerical computation trends that have emerged in the 15 years since the last major development effort. The new model architecture adopts some of the latest light transport algorithms matured by the computer graphics community and features a framework that is easily parallelized at the microscale (multithreading) and macroscale (cluster-based computing). A detailed description of the framework is provided, including a novel method for efficiently storing, evaluating, integrating, and sampling spherical and hemispherical datasets appropriate for the representation of modeled or measured bidirectional scattering, reflectance, and transmission distribution functions. The capabilities of the model are then briefly demonstrated and cross-verified with scenarios of interest to the remote sensing community.
      PubDate: Nov. 2017
      Issue No: Vol. 10, No. 11 (2017)
       
  • A Large-Scale Emulation System for Realistic Three-Dimensional (3-D)
           Forest Simulation
    • Authors: Jianbo Qi;Donghui Xie;Dashuai Guo;Guangjian Yan;
      Pages: 4834 - 4843
      Abstract: The realistic reconstruction and radiometric simulation of a large-scale three-dimensional (3-D) forest scene have potential applications in remote sensing. Although many 3-D radiative transfer models concerning forest canopy have been developed, they mainly focused on homogeneous or relatively small heterogeneous scenes, which are not compatible with the coarse-resolution remote sensing observations. Due to the huge complexity of forests and the inefficiency of collecting precise 3-D data of large areas, realistic simulation over large-scale forest area remains challenging, especially in regions of complex terrain. In this study, a large-scale emulation system for realistic 3-D forest Simulation is proposed. The 3-D forest scene is constructed from a representative single tree database (SDB) and airborne laser scanning (ALS) data. ALS data are used to extract tree height, crown diameter and position, which are linked to the individual trees in SDB. To simulate the radiometric properties of the reconstructed scene, a radiative transfer model based on a parallelized ray-tracing code was developed. This model has been validated with an abstract and an actual 3-D scene from the radiation transfer model intercomparison website and it showed comparable results with other models. Finally, a 1 km $times$ 1 km scene with more than 100 000 realistic individual trees was reconstructed and a Landsat-like reflectance image was simulated, which kept the same spatial pattern as the actual Landsat 8 image.
      PubDate: Nov. 2017
      Issue No: Vol. 10, No. 11 (2017)
       
  • Simulating the Canopy Reflectance of Different Eucalypt Genotypes With the
           DART 3-D Model
    • Authors: Julianne de Castro Oliveira;Jean-Baptiste Féret;Flávio Jorge Ponzoni;Yann Nouvellon;Jean-Philippe Gastellu-Etchegorry;Otávio Camargo Campoe;José Luiz Stape;Luiz Carlos Estraviz Rodriguez;Guerric le Maire;
      Pages: 4844 - 4852
      Abstract: Finding suitable models of canopy reflectance in forward simulation mode is a prerequisite for their use in inverse mode to characterize canopy variables of interest, such as leaf area index (LAI) or chlorophyll content. In this study, the accuracy of the three-dimensional reflectance model DART (Discrete Anisotropic Radiative Transfer) was assessed for canopies of different genotypes of Eucalyptus, having distinct biophysical and biochemical characteristics, to improve the knowledge on how these characteristics are influencing the reflectance signal as measured by passive orbital sensors. The first step was to test the model suitability to simulate reflectance images in the visible and near infrared. We parameterized DART model using extensive measurements from Eucalyptus plantations including 16 contrasted genotypes. Forest inventories were conducted and leaf, bark, and forest floor optical properties were measured. Simulation accuracy was evaluated by comparing the mean top of canopy (TOC) bidirectional reflectance of DART with TOC reflectance extracted from a Pleiades very high resolution satellite image. Results showed a good performance of DART with mean reflectance absolute error lower than 2%. Intergenotype reflectance variability was correctly simulated, but the model did not succeed at catching the slight spatial variation for a given genotype, excepted when large gaps appeared due to tree mortality. The second step consisted of sensitivity analysis to explore which biochemical or biophysical characteristics influenced more the canopy reflectance between genotypes. Perspectives for using DART model in inversion mode in these ecosystems were discussed.
      PubDate: Nov. 2017
      Issue No: Vol. 10, No. 11 (2017)
       
  • Crop Biophysical Properties Estimation Based on LiDAR Full-Waveform
           Inversion Using the DART RTM
    • Authors: Sahar Ben Hmida;Abdelaziz Kallel;Jean-Philippe Gastellu-Etchegorry;Jean-Louis Roujean;
      Pages: 4853 - 4868
      Abstract: This paper presents the results of a three-dimensional (3-D) model inversion in order to demonstrate the potential of small footprint light detection and ranging (LiDAR) waveforms for estimating crop biophysical properties. For such, we consider the height, leaf area index (LAI), and ground spectral reflectance of two maize and wheat fields. Crop structure spatial variability that is observed per measured waveform is a source of inaccuracy for the inversion of LiDAR small footprint waveforms. For example, in the maize field, standard deviation is 0.16 m for height and 0.6 for LAI. To mitigate this issue, all measured waveforms are first classified into maize and wheat clusters. Then, biophysical properties are assessed per cluster using a look-up table of waveforms that are simulated by the discrete anisotropic radiative transfer model that works with the LiDAR configuration and realistic crop 3-D mock-ups with varied properties. Results were tested against in situ measurements. Crop height is very well estimated, with root-mean-square error (RMSE) $approx {0.07}$ and 0.04 m for maize and wheat, respectively. LAI estimate is also accurate (RMSE = 0.17) for maize except for wheat last growth stage (RMSE = 0.5), possibly due to the wheat low LAI value. Finally, the field spatial heterogeneity justifies the selection of many clusters to get accurate results.
      PubDate: Nov. 2017
      Issue No: Vol. 10, No. 11 (2017)
       
  • GPU Accelerated FFT-Based Registration of Hyperspectral Scenes
    • Authors: Álvaro Ordóñez;Francisco Argüello;Dora B. Heras;
      Pages: 4869 - 4878
      Abstract: Registration is a fundamental previous task in many applications of hyperspectrometry. Most of the algorithms developed are designed to work with RGB images and ignore the execution time. This paper presents a phase correlation algorithm on GPU to register two remote sensing hyperspectral images. The proposed algorithm is based on principal component analysis, multilayer fractional Fourier transform, combination of log-polar maps, and peak processing. It is fully developed in CUDA for NVIDIA GPUs. Different techniques such as the efficient use of the memory hierarchy, the use of CUDA libraries, and the maximization of the occupancy have been applied to reach the best performance on GPU. The algorithm is robust achieving speedups in GPU of up to ${text{240}}.{text{6}}times$.
      PubDate: Nov. 2017
      Issue No: Vol. 10, No. 11 (2017)
       
  • On the Evaluation of Different High-Performance Computing Platforms for
           Hyperspectral Imaging: An OpenCL-Based Approach
    • Authors: Raúl Guerra;Ernestina Martel;Jehandad Khan;Sebastián López;Peter Athanas;Roberto Sarmiento;
      Pages: 4879 - 4897
      Abstract: Hyperspectral imaging systems are a powerful tool for obtaining surface information in many different spectral channels that can be used in many different applications. Nevertheless, the huge amount of information provided by hyperspectral images also has a downside, since it has to be processed and analyzed. For such purpose, parallel hardware devices, such as field-programmable gate arrays (FPGAs) and graphic processing units (GPUs), are typically used, especially for hyperspectral imaging applications under real-time constraints. However, developing hardware applications typically requires expertise in the specific targeted device, as well as in the tools and methodologies that can be used to perform the implementation of the desired algorithms in that device. In this scenario, the Open Computing Language (OpenCL) emerges as a very interesting solution in which a single high-level language can be used to efficiently develop applications in multiple and different hardware devices. In this work, the parallel Fast Algorithm for Linearly Unmixing Hyperspectral Images (pFUN) has been implemented in two different NVIDIA GPUs, the GeForce GTX 980 and the Tesla K40c, using OpenCL. The obtained results are compared with the results provided by the previously developed NVIDIA CUDA implementation of the pFUN algorithm for the same GPU devices for comparing the efficiency of OpenCL against a more specific synthesis design language for the targeted hardware devices, such as CUDA is for NVIDIA GPUs. Moreover, the FUN algorithm has also been implemented into a Bitware Stratix V Altera FPGA, using OpenCL, for comparing the results that can be obtained using OpenCL when targeting different devices and architectures. The obtained results demonstrate the suitability of the followed methodology in the sense that it allows the achievement of efficient FPGA and GPU implementations able to cope with the stringent requirements imposed by hyperspectral imaging systems.
      PubDate: Nov. 2017
      Issue No: Vol. 10, No. 11 (2017)
       
  • A Cloud Detection Method Based on Relationship Between Objects of Cloud
           and Cloud-Shadow for Chinese Moderate to High Resolution Satellite Imagery
           
    • Authors: Bo Zhong;Wuhan Chen;Shanlong Wu;Longfei Hu;Xiaobo Luo;Qinhuo Liu;
      Pages: 4898 - 4908
      Abstract: Cloud detection of satellite imagery is very important for quantitative remote sensing research and remote sensing applications. However, many satellite sensors do not have enough bands for a quick, accurate, and simple detection of clouds. Particularly, the newly launched moderate to high spatial resolution satellite sensors of China, such as the charge-coupled device on-board the Chinese Huan Jing 1 (HJ-1/CCD) and the wide field of view (WFV) sensor on-board the Gao Fen 1 (GF-1), only have four available bands including blue, green, red, and near infrared bands, which are far from the requirements of most could detection methods. In order to solve this problem, an improved and automated cloud detection method for Chinese satellite sensors called object-oriented cloud and cloud-shadow matching method (OCM) is presented in this paper. It first modified the automatic cloud cover assessment (ACCA) method, which was developed for Landsat-7 data, to get an initial cloud map. The modified ACCA method is mainly based on threshold and different threshold settings produce different cloud maps. Subsequently, a strict threshold is used to produce a cloud map with high confidence and large amount of cloud omission and a loose threshold is used to produce a cloud map with low confidence and large amount of commission. Second, a corresponding cloud-shadow map is also produced using the threshold of near-infrared band. Third, the cloud maps and cloud-shadow map are transferred to cloud objects and cloud-shadow objects. Cloud and cloud-shadow are usually in pairs; consequently, the final cloud and cloud-shadow maps are made based on the relationship between cloud and cloud-shadow objects. The OCM method was tested using almost 200 HJ-1/CCD and GF-1/WFV images across China and the overall accuracy of cloud detection is close to 90%.
      PubDate: Nov. 2017
      Issue No: Vol. 10, No. 11 (2017)
       
  • Surface Water Mapping by Deep Learning
    • Authors: Furkan Isikdogan;Alan C. Bovik;Paola Passalacqua;
      Pages: 4909 - 4918
      Abstract: Mapping of surface water is useful in a variety of remote sensing applications, such as estimating the availability of water, measuring its change in time, and predicting droughts and floods. Using the imagery acquired by currently active Landsat missions, a surface water map can be generated from any selected region as often as every 8 days. Traditional Landsat water indices require carefully selected threshold values that vary depending on the region being imaged and on the atmospheric conditions. They also suffer from many false positives, arising mainly from snow and ice, and from terrain and cloud shadows being mistaken for water. Systems that produce high-quality water maps usually rely on ancillary data and complex rule-based expert systems to overcome these problems. Here, we instead adopt a data-driven, deep-learning-based approach to surface water mapping. We propose a fully convolutional neural network that is trained to segment water on Landsat imagery. Our proposed model, named DeepWaterMap, learns the characteristics of water bodies from data drawn from across the globe. The trained model separates water from land, snow, ice, clouds, and shadows using only Landsat bands as input. Our code and trained models are publicly available at http://live.ece.utexas.edu/research/deepwatermap/.
      PubDate: Nov. 2017
      Issue No: Vol. 10, No. 11 (2017)
       
  • Landsat 8/OLI Two Bands Ratio Algorithm for Chlorophyll-A Concentration
           Mapping in Hypertrophic Waters: An Application to West Lake in Hanoi
           (Vietnam)
    • Authors: Nguyen Thi Thu Ha;Katsuaki Koike;Mai Trong Nhuan;Bui Dinh Canh;Nguyen Thien Phuong Thao;Michael Parsons;
      Pages: 4919 - 4929
      Abstract: Monitoring chlorophyll-a concentration (Chl-a) in inland waters, particularly hypertrophic lake waters in megacities, is a critically important environmental issue. To enable long-term Chl-a monitoring using Landsat series sensors, development of a Chl-a estimation algorithm for the new Landsat sensor is requisite. This study aims to identify the most accurate algorithm for Chl-a estimation in hypertrophic waters using Landsat 8 images and in situ Chl-a data from West Lake and nine other hypertrophic lakes in Hanoi (Vietnam's capital). The best estimation was obtained by the ratio of two reflectances at 562 and 483 nm, corresponding to the ratio of the OLI band 3 versus band 2, termed the GrB2 algorithm. The GrB2 values using the reflectances of water samples and the Landsat images were correlated with the Chl-a by an exponential function (r2 = 0.64 to 0.82), and the estimated Chl-a were verified by the smallness of standard error (smaller than 10%) and degree of conformity with recent fish-kill phenomena that commonly occur in those lakes in summer and early spring. Because the availability of GrB2 is limited to waters with low levels of inorganic suspended matter, its extension to waters with much higher levels requires further investigation.
      PubDate: Nov. 2017
      Issue No: Vol. 10, No. 11 (2017)
       
  • Incorporating Open Source Data for Bayesian Classification of Urban Land
           Use From VHR Stereo Images
    • Authors: Mengmeng Li;Kirsten M. de Beurs;Alfred Stein;Wietske Bijker;
      Pages: 4930 - 4943
      Abstract: This study investigates the incorporation of open source data into a Bayesian classification of urban land use from very high resolution (VHR) stereo satellite images. The adopted classification framework starts from urban land cover classification, proceeds to building-type characterization, and results in urban land use. For urban land cover classification, a preliminary classification distinguishes trees, grass, and shadow objects using a random forest at a fine segmentation level. Fuzzy decision trees derived from hierarchical Bayesian models separate buildings from other man-made objects at a coarse segmentation level, where an open street map provides prior building information. A Bayesian network classifier combining commonly used land use indicators and spatial arrangement is used for the urban land use classification. The experiments were conducted on GeoEye stereo images over Oklahoma City, USA. Experimental results showed that the urban land use classification using VHR stereo images performed better than that using a monoscopic VHR image, and the integration of open source data improved the final urban land use classification. Our results also show a way of transferring the adopted urban land use classification framework, developed for a specific urban area in China, to other urban areas. The study concludes that incorporating open source data by Bayesian analysis improves urban land use classification. Moreover, a pretrained convolutional neural network fine tuned on the UC Merced land use dataset offers a useful tool to extract additional information for urban land use classification.
      PubDate: Nov. 2017
      Issue No: Vol. 10, No. 11 (2017)
       
  • Land Surface Temperature Retrieval From FY-3C/VIRR Data and Its
           Cross-Validation With Terra/MODIS
    • Authors: Caixia Gao;Shi Qiu;En-Yu Zhao;Chuanrong Li;Ling-Li Tang;Ling-Ling Ma;Xiaoguang Jiang;Yonggang Qian;Yongguang Zhao;Ning Wang;Lu Ren;
      Pages: 4944 - 4953
      Abstract: Accurate inversion of land surface temperature (LST) from remote sensing data is an essential and challenging topic for earth observation applications. This paper successfully retrieves the LST from FY-3C/VIRR data with split-window method. With the simulated data, the algorithm coefficients are acquired with root mean square errors lower than 1.0 K for all subranges when view zenith angle (VZA) < 30° and the water vapor content (WVC) < 4.25 g/cm2 , as well as those in which the VZA < 30° and the LST < 307.5 K. In addition, a detailed sensitivity analysis is carried out. The analysis result indicates that the total LST uncertainty caused by the standard error of the algorithm, the uncertainties of land surface emissivity and WVC, and the instrument noise would be 1.22 K and 0.94 K for dry and wet atmosphere, respectively. Furthermore, LST retrieval method is applied to the visible and infrared radiometer measurements over the study area covering the geographical latitude of 31.671°N to 44.211°N and longitude of 10.739°W to 1.898°E, and the derived LST is cross-validated with Terra/MODIS LST product. The preliminary validation result shows that the split-window method determines the LST within 2.0 K for vegetation and soil areas.
      PubDate: Nov. 2017
      Issue No: Vol. 10, No. 11 (2017)
       
  • Regional Carbon Predictions in a Temperate Forest Using Satellite Lidar
    • Authors: Alexander S. Antonarakis;Alejandro Guizar Coutiño;
      Pages: 4954 - 4960
      Abstract: Large uncertainties in terrestrial carbon stocks and sequestration predictions result from insufficient regional data characterizing forest structure. This study uses satellite waveform lidar from ICESat to estimate regional forest structure in central New England, where each lidar waveform estimates fine-scale forest heterogeneity. ICESat is a global sampling satellite, but does not provide wall-to-wall coverage. Comprehensive, wall-to-wall ecosystem state characterization is achieved through spatial extrapolation using the random forest machine-learning algorithm. This forest description allows for effective initialization of individual-based terrestrial biosphere models making regional carbon flux predictions. Within 42/43.5 N and 73/71.5 W, aboveground carbon was estimated at 92.47 TgC or 45.66 MgC ha−1, and net carbon fluxes were estimated at 4.27 TgC yr−1 or 2.11 MgC ha−1 yr−1. This carbon sequestration potential was valued at 47% of fossil fuel emissions in eight central New England counties. In preparation for new lidar and hyperspectral satellites, linking satellite data and terrestrial biosphere models are crucial in improving estimates of carbon sequestration potential counteracting anthropogenic sources of carbon.
      PubDate: Nov. 2017
      Issue No: Vol. 10, No. 11 (2017)
       
  • Trends in Phenological Parameters and Relationship Between Land Surface
           Phenology and Climate Data in the Hyrcanian Forests of Iran
    • Authors: Khadije Kiapasha;Ali Asghar Darvishsefat;Yves Julien;Jose A. Sobrino;Nosratoallah Zargham;Pedram Attarod;Michael E. Schaepman;
      Pages: 4961 - 4970
      Abstract: Vegetation activity may be changed in response to climate variability by affecting seasonality and phenological events. Monitoring of land surface phenological changes play a key role in understanding feedback of ecosystem dynamics. This study focuses on the analysis of trends in land surface phenology derived parameters using normalized difference vegetation index time series based on Global Inventory Monitoring and Mapping Studies data in the Hyrcanian forests of Iran covering the period 1981–2012. First, we applied interpolation for data reconstruction in order to remove outliers and cloud contamination in time series. Phenological parameters were retrieved by using the midpoint approach, whereas trends were estimated using the Theil–Sen approach. Correlation coefficients were evaluated from multiple linear regression between phenological parameters against temperature and precipitation time series. Significant Mann–Kendall test analysis indicate average start of season (SOS) and end of season (EOS) increased by −0.16 and +0.14 days per year, respectively. Results of significant trend analysis showed that later EOS was associated with increasing temperature trends and we found strongest relationships between temperature and phenological parameters in the west of the Hyrcanian forests, where precipitation was abundant. Moreover, SOS correlated strongly with total precipitation and mean temperature. This study allows us to better estimate the drivers affecting the vegetation dynamics in the Hyrcanian forests of Iran.
      PubDate: Nov. 2017
      Issue No: Vol. 10, No. 11 (2017)
       
  • Semisupervised PolSAR Image Classification Based on Improved Cotraining
    • Authors: Wenqiang Hua;Shuang Wang;Hongying Liu;Kun Liu;Yanhe Guo;Licheng Jiao;
      Pages: 4971 - 4986
      Abstract: In order to obtain good classification performance of polarimetric synthetic aperture radar (PolSAR) images, many labeled samples are needed for training. However, it is difficult, expensive, and time-consuming to obtain labeled samples in practice. On the other hand, unlabeled samples are substantially cheaper and more plentiful than labeled ones. In addressing this issue, semisupervised learning techniques are proposed. In this paper, a novel semisupervised algorithm based on an improved cotraining process is proposed for PolSAR image classification. First, we propose an indirect analysis strategy to analyze the nature of sufficiency and independence between two different views for cotraining. Then, an improved cotraining process with a new sample selection strategy is presented, which can effectively take advantage of unlabeled samples to improve the performance of classification, particularly when labeled samples are limited. Finally, a new postprocess method based on a similarity principle and a superpixel algorithm is developed to improve the consistency of the classification. Experimental results on three real PolSAR images show that our proposed method is an effective classification method, and is superior to other traditional methods.
      PubDate: Nov. 2017
      Issue No: Vol. 10, No. 11 (2017)
       
  • Airborne DInSAR Results Using Time-Domain Backprojection Algorithm: A Case
           Study Over the Slumgullion Landslide in Colorado With Validation Using
           Spaceborne SAR, Airborne LiDAR, and Ground-Based Observations
    • Authors: Ning Cao;Hyongki Lee;Evan Zaugg;Ramesh Shrestha;William Carter;Craig Glennie;Guoquan Wang;Zhong Lu;Juan Carlos Fernandez-Diaz;
      Pages: 4987 - 5000
      Abstract: The major impediment to accurate airborne repeat-pass differential synthetic aperture radar (SAR) interferometry (DInSAR) is compensating for aircraft motion caused by air turbulence. Various motion compensation (MoCo) procedures have been used in the airborne DInSAR processing to acquire reliable deformation mapping. In this paper, we present the use of time-domain backprojection (BP) algorithm for SAR focusing in an airborne DInSAR survey: No MoCo procedure is needed because the BP algorithm is inherently able to compensate for platform motion. In this study, we present the results of a pilot study aimed at demonstrating the feasibility of deformation mapping with an airborne SAR system based on the monitoring of the Slumgullion landslide in Colorado, USA between July 3 and 10 of 2015. The employed airborne SAR system is an Artemis SlimSAR that is a compact, modular, and multi-frequency radar system. Airborne light detection and ranging and global navigation satellite system (GNSS) observations, as well as spaceborne DInSAR results using COSMO-SkyMed (CSK) images, were used to verify the performance of the airborne SAR system. The surface velocities of the landslide derived from the airborne DInSAR observations showed good agreement with the GNSS and spaceborne DInSAR estimates. A three-dimensional deformation map of the Slumgullion landslide was also generated, which displayed distinct correlation between the landslide motion and topographic variation. This study shows that an inexpensive airborne L-band DInSAR system has the potential to measure centimeter level deformation with flexible temporal and spatial baselines.
      PubDate: Nov. 2017
      Issue No: Vol. 10, No. 11 (2017)
       
  • A Rise-Dimensional Modeling and Estimation Method for Flight Trajectory
           Error in Bistatic Forward-Looking SAR
    • Authors: Wei Pu;Junjie Wu;Yulin Huang;Ke Du;Wenchao Li;Jianyu Yang;Haiguang Yang;
      Pages: 5001 - 5015
      Abstract: Bistatic forward-looking synthetic aperture radar (BFSAR) is a kind of bistatic SAR system that can image forward-looking terrain in the flight direction of a moving platform. In BFSAR, compensation of the flight trajectory errors is of great significance to get a well-focused image. To accomplish an accurate motion compensation in image processing, a high-precision navigation system is needed. However, in many cases, due to the accuracy limit of such systems, flight trajectory errors are hard to be compensated correctly, causing mainly the resolution decrease in final images. In order to cope with such a problem, we propose a rise-dimensional modeling and estimation for flight trajectory error based on raw BFSAR data in this paper. To apply this method, we first carry out a preprocessing named azimuth-slowtime decoupling to deal with the spatially variant flight trajectory error before estimation. Then, an optimization model for flight trajectory estimation under the criterion of maximum image intensity is built. The solution to the optimization model is the accurate flight trajectory. Then, block coordinate descent technique is used to solve this optimization model. The processing of BFSAR data shows that the algorithm can obtain a more accurate estimation results, and generate better focused images compared with the existing trajectory estimation method.
      PubDate: Nov. 2017
      Issue No: Vol. 10, No. 11 (2017)
       
  • Narrow-Band Interference Suppression via RPCA-Based Signal Separation in
           Time–Frequency Domain
    • Authors: Jia Su;Haihong Tao;Mingliang Tao;Ling Wang;Jian Xie;
      Pages: 5016 - 5025
      Abstract: Narrow-band interference (NBI) is a critical issue for synthetic aperture radar (SAR), in which the imaging quality can be degraded severely. To suppress NBI effectively, a novel interference suppression algorithm using robust principal component analysis (RPCA) based signal separation in time–frequency domain is proposed. The RPCA algorithm is introduced for signal separation in the time–frequency domain for the first time. The fundamental assumption of RPCA is that a matrix can be modeled as a combination of a low-rank matrix and a sparse counterpart. In terms of the SAR echo, the short time Fourier transformation (STFT) matrix of mixed signals (i.e., useful SAR signals and NBIs) well fits the assumption of RPCA. Based on this property, radar echoes are first transformed into the time–frequency domain by STFT to form an STFT matrix. Then, the RPCA algorithm is used to decompose the STFT matrix into a low-rank matrix (i.e., NBIs) and a sparse matrix (i.e., useful signals). Finally, the NBIs can be reconstructed and subtracted from the echoes to realize the interference suppression. The experimental results of simulated and measured data demonstrate that the proposed algorithm not only can suppress interference effectively, but also preserve the useful information as much as possible.
      PubDate: Nov. 2017
      Issue No: Vol. 10, No. 11 (2017)
       
  • Performance Comparison Between Reflection Symmetry Metric and Product of
           Multilook Amplitudes for Ship Detection in Dual-Polarization SAR Images
    • Authors: Gui Gao;Gongtao Shi;Gaosheng Li;Jianghua Cheng;
      Pages: 5026 - 5038
      Abstract: The reflection symmetry metric (RSM) and product of multilook amplitudes (PMA) detectors, which were proposed recently, have been demonstrated to be promising methods for processing dual-polarimetric synthetic aperture radar (SAR) data for ship detection. The improvements in ship detection performance by using the RSM, compared to that using the PMA, are investigated in this paper. As the ship-sea contrast (or the signal-clutter-ratio, SCR) is a central index to assess the performance of a detection method, the SCRs in the RSM and PMA are first defined and compared. Next, a theoretical explanation for why the RSM outperforms the PMA in detection performance is provided. The detection performance is then characterized by calculating the receiver operating characteristic (ROC) curves. The preliminary experimental results performed on measured RADARSAT-2, ALOS-PALSAR, and NASA/JPL AIRSAR images verify the accuracy of the theoretical analysis.
      PubDate: Nov. 2017
      Issue No: Vol. 10, No. 11 (2017)
       
  • Improvement of a Pansharpening Method Taking Into Account Haze
    • Authors: Hui Li;Linhai Jing;
      Pages: 5039 - 5055
      Abstract: Pansharpening is an important technique used to generate high-quality high-spatial-resolution multispectral (MS) bands by fusing low-spatial-resolution MS bands and a panchromatic (PAN) band obtained by the same sensor. A PAN-modulation (PM)-based pansharpening method taking account of haze, which is referred as Haze- and Ratio- based (HR) method, has been demonstrated to yield good performances, indicating that the impact of haze should be considered in PM-based methods. It is obvious that the haze values used in the HR fusion influence the spectral vectors of fused pixels, thus affect the spectral distortion of fused images. In order to reach stable and good performances of the HR method, the determination of the optimal haze values is discussed in this study. First, six approaches for haze values determination, which are variations of the histogram minimal approach and the darkest pixel approach employed by the image-based dark-object subtraction method for atmospheric correction of remote-sensed images, are compared. Then, an improved approach for haze values determination is proposed. The proposed approach is proved to be effective for improving the performance of the HR method. This is very important for the employment of the HR method in practical applications and by more researchers.
      PubDate: Nov. 2017
      Issue No: Vol. 10, No. 11 (2017)
       
  • On-Orbit Line Spread Function Estimation of the SNPP VIIRS Imaging System
           From Lake Pontchartrain Causeway Bridge Images
    • Authors: James C. Tilton;Robert E. Wolfe;Guoqing Lin;
      Pages: 5056 - 5072
      Abstract: The visible infrared imaging radiometer suite (VIIRS) instrument was launched on October 28, 2011 onboard the Suomi National Polar-Orbiting Partnership (SNPP) satellite. The VIIRS instrument is a whiskbroom system with 22 spectral and thermal bands split between 16 moderate resolution bands (M-bands), five imagery resolution bands (I-bands), and a day–night band. In this study, we estimate the along-scan line spread function (LSF) of the I-bands and M-bands based on measurements performed on images of the Lake Pontchartrain Causeway Bridge. In doing so, we develop a model for the LSF that closely matches the prelaunch laboratory measurements. We utilize VIIRS images co-geolocated with a Landsat TM image to precisely locate the bridge linear feature in the VIIRS images as a linear best fit to a straight line. We then utilize nonlinear optimization to compute the best fit equation of the VIIRS image measurements in the vicinity of the bridge to the developed model equation. From the found parameterization of the model equation, we derive the full-width at half-maximum as an approximation of the sensor field of view for all bands, and compare these on-orbit measured values with prelaunch laboratory results.
      PubDate: Nov. 2017
      Issue No: Vol. 10, No. 11 (2017)
       
  • A Hybrid Subpixel Mapping Framework for Hyperspectral Images Using
           Collaborative Representation
    • Authors: Yifan Zhang;Xiaoqin Xue;Ting Wang;Mingyi He;
      Pages: 5073 - 5086
      Abstract: Subpixel mapping with a low-resolution hyperspectral image as the only input is widely applicable due to the fact that auxiliary image is not always available in practice. In this paper, the collaborative representation-based subpixel mapping (CRSPM) framework is proposed to acquire an improved classification map at subpixel scale with only a low-resolution hyperspectral image available. To efficiently extract and utilize spatial information in this case without auxiliary image, the low-resolution hyperspectral (LHS) image is processed in a hybrid framework in two different ways to generate two subpixel scale classification maps. One is obtained by classifying the upsampled LHS image using collaborative representation-based (CR-based) classifier. The other is available using CR-based classification combined with spectral unmixing and subpixel spatial attraction model. Specifically, to enclose the contextual spatial information for higher classification accuracy, a spatially joint as well as post-partitioning CR-based classifier, JCRT-based classifier, is proposed and applied in this work. To achieve better classification performance, decision fusion is applied to determine class label from the two classification maps for each subpixel by the voting of the neighboring subpixels. Experimental results illustrate that the proposed CRSPM approach clearly outperforms some state-of-the-art subpixel mapping approaches by producing smoother classification map with less misclassification.
      PubDate: Nov. 2017
      Issue No: Vol. 10, No. 11 (2017)
       
  • Fast and Robust Self-Representation Method for Hyperspectral Band
           Selection
    • Authors: Weiwei Sun;Long Tian;Yan Xu;Dianfa Zhang;Qian Du;
      Pages: 5087 - 5098
      Abstract: In this paper, a fast and robust self-representation (FRSR) method is proposed to select a proper band subset from hyperspectral imagery (HSI). The FRSR assumes the separability structure of the HSI band set and transforms the problem of separable nonnegative matrix factorization into the robust self-representation (RSR) model. Then, the FRSR incorporates structured random projections into the RSR model to improve computational efficiency. The solution of FRSR is formulated into optimizing a convex problem and the augmented Lagrangian multipliers are adopted to estimate the proper factorization localizing matrix in the FRSR. The selected band subset is constituted with the bands corresponding to the r largest diagonal entries of the factorization localizing matrix. The experimental results show that FRSR outperforms state-of-the-art techniques in classification accuracy with lower computational cost.
      PubDate: Nov. 2017
      Issue No: Vol. 10, No. 11 (2017)
       
  • Hierarchical Subspace Learning Based Unsupervised Domain Adaptation for
           Cross-Domain Classification of Remote Sensing Images
    • Authors: Biplab Banerjee;Subhasis Chaudhuri;
      Pages: 5099 - 5109
      Abstract: We address the problem of automatic updating of land-cover maps by using remote sensing images under the notion of domain adaptation (DA) in this paper. Essentially, unsupervised DA techniques aim at adapting a classifier modeled on the source domain by considering the available ground truth and evaluate the same on a related yet diverse target domain consisting only of test samples. Traditional subspace learning based strategies in this respect inherently assume the existence of a single subspace spanning the data from both the domains. However, such a constraint becomes rigid in many scenarios considering the diversity in the statistical properties of the underlying semantic classes and problem due to data overlapping in the feature space. As a remedy, we propose an automated binary-tree based hierarchical organization of the semantic classes and subsequently introduce the notion of node-specific subspace learning from the learned tree. We validate the method on hyperspectral, medium-resolution, and very high resolution datasets, which exhibits a consistently improved performance in comparison to standard single subspace learning based strategies as well as other representative techniques from the literature.
      PubDate: Nov. 2017
      Issue No: Vol. 10, No. 11 (2017)
       
  • POSEIDON: An Analytical End-to-End Performance Prediction Model for
           Submerged Object Detection and Recognition by Lidar Fluorosensors in the
           Marine Environment
    • Authors: Stefania Matteoli;Laura Zotta;Marco Diani;Giovanni Corsini;
      Pages: 5110 - 5133
      Abstract: An analytical end-to-end model is developed to predict the performance of underwater object recognition by means of light detection and ranging (lidar) fluorosensors, as an aid to underwater lidar mission planning and system design. The proposed Performance prediction mOdel for Submerged object dEtection and recognitIon by liDar fluOrosensors in the marine eNvironment (POSEIDON) reproduces the overall end-to-end fluorescence lidar system chain—from signal generation, to signal propagation, acquisition, and processing. The goal is assessing the performance that may be obtained for spectral recognition of an underwater object in various operational scenarios in terms of several different performance metrics. In addition to the performance prediction models developed in the literature for airborne lidar bathymetry, POSEIDON embeds a novel comprehensive signal simulator that accounts for inelastic scattering phenomena as well as a signal processing module designed ad hoc to accomplish spectral recognition of an underwater object with respect to a data base of objects of interest spectrally characterized by their fluorescence spectral signatures. Test cases with a lidar system arranged in two configurations and several objects submerged at various depths in different Cases I and II waters were reproduced and explored. Results obtained within a Monte Carlo simulation framework provide proof-of-concept of POSEIDON performance forecasting capabilities for underwater object recognition.
      PubDate: Nov. 2017
      Issue No: Vol. 10, No. 11 (2017)
       
  • Automatic Detection of Long Period Events Based on Subband-Envelope
           Processing
    • Authors: Luz García;Isaac Álvarez;Manuel Titos;Alejandro Díaz-Moreno;M. Carmen Benítez;Ángel de la Torre;
      Pages: 5134 - 5142
      Abstract: This work presents a novel approach to automatic detection of long period events (LP) in continuous seismic records. Without any supervised learning, the proposal is based on a simple processing to search for the LP characteristic shape, duration, and band of activity. Continuous raw signals from the seismometer are first filtered into three frequency bands separating lower, central, and upper frequency components. These new signals are then processed in parallel to extract subband envelopes and create a characteristic function that enhances LP features. Experiments to test the proposal are presented using: 1) 2 h of continuous recordings of the Volcano of Deception Island, Antarctica, containing LP events artificially contaminated with seismic background noise to create low signal-to-noise ratio scenarios and 2) a set of earthquake-like computer generated signals, randomly produced and inserted in the continuous records to recreate a testing environment as challenging as possible. A receiver operating curve analysis of the results compared to those of a classical short/long time average approach, provides positive conclusions on the performance of the technique presented.
      PubDate: Nov. 2017
      Issue No: Vol. 10, No. 11 (2017)
       
  • Corrections to “Change Detection in Full and Dual Polarization, Single-
           and Multi-Frequency SAR Data”
    • Authors: Allan A. Nielsen;Knut Conradsen;Henning Skriver;
      Pages: 5143 - 5144
      Abstract: When the covariance matrix formulation is used for multi-look polarimetric synthetic aperture radar (SAR) data, the complex Wishart distribution applies. Based on this distribution a test statistic for equality of two complex variance-covariance matrices and an associated asymptotic probability of obtaining a smaller value of the test statistic are given. In a case study airborne EMISAR C- and L-band SAR images from the spring of 1998 covering agricultural fields and wooded areas near Foulum, Denmark, are used in single- and bi-frequency, bi-temporal change detection with full and dual polarimetry data.
      PubDate: Nov. 2017
      Issue No: Vol. 10, No. 11 (2017)
       
  • Proceedings of the IEEE
    • Pages: 5147 - 5147
      Abstract: Advertisement: For over 100 years, Proceedings of the IEEE has been the leading journal for engineers looking for in-depth tutorial, survey, and review coverage of the technical developments that shape our world. Offering practical, fully referenced articles, Proceedings of the IEEE serves as a bridge to help readers understand important technologies in the areas of electrical engineering and computer science.
      PubDate: Nov. 2017
      Issue No: Vol. 10, No. 11 (2017)
       
  • Become a published author in 4 to 6 weeks
    • Pages: 5148 - 5148
      Abstract: Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.
      PubDate: Nov. 2017
      Issue No: Vol. 10, No. 11 (2017)
       
 
 
JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
Fax: +00 44 (0)131 4513327
 
Home (Search)
Subjects A-Z
Publishers A-Z
Customise
APIs
Your IP address: 54.198.246.116
 
About JournalTOCs
API
Help
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-2016