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  Subjects -> ELECTRONICS (Total: 160 journals)
Showing 1 - 200 of 277 Journals sorted alphabetically
Advances in Biosensors and Bioelectronics     Open Access   (Followers: 7)
Advances in Electrical and Electronic Engineering     Open Access   (Followers: 5)
Advances in Electronics     Open Access   (Followers: 63)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 9)
Advances in Microelectronic Engineering     Open Access   (Followers: 12)
Advances in Power Electronics     Open Access   (Followers: 29)
Aerospace and Electronic Systems, IEEE Transactions on     Hybrid Journal   (Followers: 269)
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: 8)
Archives of Electrical Engineering     Open Access   (Followers: 12)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 9)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 26)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 21)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 37)
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: 42)
China Communications     Full-text available via subscription   (Followers: 7)
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: 219)
Edu Elektrika Journal     Open Access  
Electronic Design     Partially Free   (Followers: 93)
Electronic Markets     Hybrid Journal   (Followers: 8)
Electronic Materials Letters     Hybrid Journal   (Followers: 3)
Electronics     Open Access   (Followers: 78)
Electronics and Communications in Japan     Hybrid Journal   (Followers: 9)
Electronics For You     Partially Free   (Followers: 80)
Electronics Letters     Hybrid Journal   (Followers: 27)
Embedded Systems Letters, IEEE     Hybrid Journal   (Followers: 46)
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: 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: 176)
Haptics, IEEE Transactions on     Hybrid Journal   (Followers: 5)
IEEE Antennas and Propagation Magazine     Hybrid Journal   (Followers: 89)
IEEE Antennas and Wireless Propagation Letters     Hybrid Journal   (Followers: 74)
IEEE Journal of Emerging and Selected Topics in Power Electronics     Hybrid Journal   (Followers: 42)
IEEE Journal of the Electron Devices Society     Open Access   (Followers: 9)
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits     Hybrid Journal  
IEEE Power Electronics Magazine     Full-text available via subscription   (Followers: 60)
IEEE Transactions on Antennas and Propagation     Full-text available via subscription   (Followers: 66)
IEEE Transactions on Automatic Control     Hybrid Journal   (Followers: 53)
IEEE Transactions on Circuits and Systems for Video Technology     Hybrid Journal   (Followers: 18)
IEEE Transactions on Consumer Electronics     Hybrid Journal   (Followers: 36)
IEEE Transactions on Electron Devices     Hybrid Journal   (Followers: 18)
IEEE Transactions on Information Theory     Hybrid Journal   (Followers: 26)
IEEE Transactions on Power Electronics     Hybrid Journal   (Followers: 64)
IEEE Transactions on Signal and Information Processing over Networks     Full-text available via subscription   (Followers: 11)
IEICE - Transactions on Electronics     Full-text available via subscription   (Followers: 13)
IEICE - Transactions on Information and Systems     Full-text available via subscription   (Followers: 5)
IET Cyber-Physical Systems : Theory & Applications     Open Access  
IET Microwaves, Antennas & Propagation     Hybrid Journal   (Followers: 31)
IET Power Electronics     Hybrid Journal   (Followers: 40)
IET Wireless Sensor Systems     Hybrid Journal   (Followers: 18)
IETE Journal of Education     Open Access   (Followers: 4)
IETE Journal of Research     Open Access   (Followers: 10)
IETE Technical Review     Open Access   (Followers: 13)
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)     Open Access   (Followers: 2)
Industrial Electronics, IEEE Transactions on     Hybrid Journal   (Followers: 45)
Industry Applications, IEEE Transactions on     Hybrid Journal   (Followers: 16)
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: 12)
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: 3)
International Journal of Control     Hybrid Journal   (Followers: 11)
International Journal of Electronics     Hybrid Journal   (Followers: 6)
International Journal of Electronics and Telecommunications     Open Access   (Followers: 14)
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: 11)
International Journal of Microwave and Wireless Technologies     Hybrid Journal   (Followers: 7)
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: 19)
International Journal of Sensors, Wireless Communications and Control     Hybrid Journal   (Followers: 9)
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: 12)
International Journal on Electrical and Power Engineering     Full-text available via subscription   (Followers: 9)
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: 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: 21)
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: 7)
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: 142)
Journal of Information and Telecommunication     Open Access  
Journal of Intelligent Procedures in Electrical Technology     Open Access   (Followers: 3)
Journal of Low Power Electronics     Full-text available via subscription   (Followers: 6)
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: 27)
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: 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: 17)
Nanotechnology Magazine, IEEE     Full-text available via subscription   (Followers: 30)
Nanotechnology, Science and Applications     Open Access   (Followers: 6)
Nature Electronics     Hybrid Journal  
Networks: an International Journal     Hybrid Journal   (Followers: 6)
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)
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: 10)
Security and Communication Networks     Hybrid Journal   (Followers: 2)
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of     Hybrid Journal   (Followers: 50)
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: 74)
Solid-State Circuits Magazine, IEEE     Hybrid Journal   (Followers: 12)
Solid-State Electronics     Hybrid Journal   (Followers: 8)
Superconductor Science and Technology     Hybrid Journal   (Followers: 3)
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: 8)
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: 5)
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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Journal Cover Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of
  [SJR: 1.196]   [H-I: 37]   [50 followers]  Follow
   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 2018
      Issue No: Vol. 11, No. 5 (2018)
  • IEEE Journal of Selected Topics in Applied Earth Observations and Remote
           Sensing publication information
    • 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 2018
      Issue No: Vol. 11, No. 5 (2018)
  • IEEE Journal of Selected Topics in Applied Earth Observations and Remote
    • Abstract: These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.
      PubDate: May 2018
      Issue No: Vol. 11, No. 5 (2018)
  • Institutional Listings
    • Abstract: Presents a listing of institutional institutions relevant for this issue of the publication.
      PubDate: May 2018
      Issue No: Vol. 11, No. 5 (2018)
  • Open Data for Global Multimodal Land Use Classification: Outcome of the
           2017 IEEE GRSS Data Fusion Contest
    • Authors: Naoto Yokoya;Pedram Ghamisi;Junshi Xia;Sergey Sukhanov;Roel Heremans;Ivan Tankoyeu;Benjamin Bechtel;Bertrand Le Saux;Gabriele Moser;Devis Tuia;
      Pages: 1363 - 1377
      Abstract: In this paper, we present the scientific outcomes of the 2017 Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society. The 2017 Contest was aimed at addressing the problem of local climate zones classification based on a multitemporal and multimodal dataset, including image (Landsat 8 and Sentinel-2) and vector data (from OpenStreetMap). The competition, based on separate geographical locations for the training and testing of the proposed solution, aimed at models that were accurate (assessed by accuracy metrics on an undisclosed reference for the test cities), general (assessed by spreading the test cities across the globe), and computationally feasible (assessed by having a test phase of limited time). The techniques proposed by the participants to the Contest spanned across a rather broad range of topics, and of mixed ideas and methodologies deriving from computer vision and machine learning but also deeply rooted in the specificities of remote sensing. In particular, rigorous atmospheric correction, the use of multidate images, and the use of ensemble methods fusing results obtained from different data sources/time instants made the difference.
      PubDate: May 2018
      Issue No: Vol. 11, No. 5 (2018)
  • Characterizing the Impact of Aerosols on Pre-Hurricane Sandy
    • Authors: Andrew T. Fontenot;Hesham Mohamed El-Askary;Michael J. Garay;James R. Campbell;Olga V. Kalashnikova;
      Pages: 1378 - 1386
      Abstract: This study focuses on the role that African dust over the Atlantic had on the persistence of the tropical system that eventually became Hurricane Sandy in October 2012. On October 8, a Saharan dust event in the Mauritania region of West Africa transported significant amounts of mineral dust into the troposphere and along the path of an easterly wave created by a break in the Intertropical Convergence Zone (ITCZ). The Terra/Aqua-MODIS satellite observations clearly define the spatial distribution of the coarse/fine aerosols, while the CALIPSO observations of the total attenuated backscatter at 532 nm provide a detailed view of the vertical structure and aerosol types in the dust-laden layer. European Centre for Medium-Range Weather Forecasts and Modern-Era Retrospective Analysis for Research and Applications, Version 2 reanalysis data show the distribution of aerosols along the path of the pre-Sandy wave as well as a second wave that formed north of the ITCZ under different condition. The second wave, which started in an area of relatively larger aerosol optical depth (AOD), moved into an area with abnormally low convective available potential energy and AOD, subsequently dying out, while the wave that became Sandy had light aerosol loading (AOD between 0.15–0.5) along a majority of its path. The evidence suggests that aerosols played a nontrivial role in the maintenance of this system until it moved into an environment favorable for cyclogenesis.
      PubDate: May 2018
      Issue No: Vol. 11, No. 5 (2018)
  • Performance of the Standardized Precipitation Index Based on the TMPA and
           CMORPH Precipitation Products for Drought Monitoring in China
    • Authors: Jing Lu;Li Jia;Massimo Menenti;Yuping Yan;Chaolei Zheng;Jie Zhou;
      Pages: 1387 - 1396
      Abstract: This paper evaluated the accuracy of multiple satellite-based precipitation products including the tropical rainfall measuring mission multisatellite precipitation analysis (TMPA) (TMPA 3B42RT and TMPA 3B42 version 7) and the Climate Prediction Center MORPHing technique (CMORPH) (CMORPH RAW and CMORPH BLD version 1.0) datasets and investigated the impact of the accuracy and temporal coverage of these data products on the reliability of the standardized precipitation index (SPI) estimates. The satellite-based SPI was compared with the SPI estimate using in situ precipitation observations from 2221 meteorological observation sites across China from 1998 to 2014. The SPI values calculated from the products calibrated with rain gauge measurements (TMPA 3B42 and CMORPH BLD) are generally more consistent with the SPI obtained with in situ measurements than those obtained using noncalibrated products (TMPA 3B42RT and CMORPH RAW products). The short data record of satellite precipitation data products is not the primary source of large errors in the SPI estimates, suggesting that the SPI estimate using satellite precipitation data products can be applied to drought assessment and monitoring. Satellite-based SPI estimates are more accurate in eastern China than in western China because of larger uncertainties in precipitation retrievals in western China, characterized by arid and semiarid climate conditions and complex landscapes. The satellite-based SPI can capture typical drought events throughout China, with the limitation that it is based on precipitation only and that different durations of antecedent precipitation are only suitable for specific drought conditions.
      PubDate: May 2018
      Issue No: Vol. 11, No. 5 (2018)
  • Assessment of the Impacts From the World's Largest Floating
           Macroalgae Blooms on the Water Clarity at the West Yellow Sea Using MODIS
           Data (2002–2016)
    • Authors: Lin Li;Qianguo Xing;Xuerong Li;Dingfeng Yu;Jun Zhang;Jingqiu Zou;
      Pages: 1397 - 1402
      Abstract: Water clarity (Secchi disk depth, SDD) is a very important factor for marine ecological environment. The world's largest “green tide” caused by the macroalgal blooms (MABs) of green macroalgae has occurred every summer in the Yellow Sea since 2008. In this study, we first present the effects of MABs on the water clarity in the west Yellow Sea. A regional empirical retrieval algorithm of SDD on the basis of moderate resolution imaging spectroradiometer (MODIS) remote sensing reflectance is evaluated with the field data and satellite reflectance data: the spectral simulation with the end-member reflectance of sea water and macroalgae, and the MODIS Level-2 standard products of the remote sensing reflectance. The results show that the mixture of sea water and macroalgae will lead to decreased water clarity when the SDD is larger than 1.2 m and increased chlorophyll-a, i.e., false values in the standard products for pure sea water which therefore should be used with caution for the regions with large scale of floating macroalgae blooms. The long-term SDD in June and July (2002–2016) over the Yellow Sea is investigated and analyzed with the presence of “green tide.” The significant decrease in the SDD by 2.6 m and with 12 544 km2 of sea surface in total in July while no pronouncing changes in June suggests that the water clarity in the west Yellow Sea has been strongly affected from the period of 2002–2007 (the pre-MAB phase) to the period of 2008–2016 (the MAB phase).
      PubDate: May 2018
      Issue No: Vol. 11, No. 5 (2018)
  • Evaluation of Feature Ranking and Regression Methods for Oceanic
           Chlorophyll-a Estimation
    • Authors: Katalin Blix;Torbjørn Eltoft;
      Pages: 1403 - 1418
      Abstract: This paper evaluates two alternative regression techniques for oceanic chlorophyll-a (Chl-a) content estimation. One of the investigated methodologies is the recently introduced Gaussian process regression (GPR) model. We explore two feature ranking methods derived for the GPR model, namely sensitivity analysis (SA) and automatic relevance determination (ARD). We also investigate a second regression method, the partial least squares regression (PLSR) for oceanic Chl-a content estimation. Feature relevance in the PLSR model can be accessed through the variable importance in projection (VIP) feature ranking algorithm. This paper thus analyzes three feature ranking models, SA, ARD, and VIP, which are all derived from different fundamental principles, and uses the ranked features as inputs to the GPR and PLSR to assess regression strengths. We compare the regression performances using some common performance measures, and show how the feature ranking methods can be used to find the lowest number of features to estimate oceanic Chl-a content by using the GPR and PLSR models, while still producing comparable performance to the state-of-the-art algorithms. We evaluate the models on a global MEdium Resolution Imaging Spectrometer matchup dataset. Our results show that the GPR model has the best regression performance for most of the input feature sets we used, and our conclusion is this model can favorably be used for Chl-a content retrieval, already with two features, ranked by either the SA or ARD methods.
      PubDate: May 2018
      Issue No: Vol. 11, No. 5 (2018)
  • Semiautomated Segmentation of Sentinel-1 SAR Imagery for Mapping Sea Ice
           in Labrador Coast
    • Authors: Weikai Tan;Jonathan Li;Linlin Xu;Michael A. Chapman;
      Pages: 1419 - 1432
      Abstract: This study aims at proposing a semiautomated sea ice segmentation workflow utilizing Sentinel-1 synthetic aperture radar imagery. The workflow consists of two main steps. First, preferable features in sea ice interpretation were determined with a random forest feature selection method. Second, an unsupervised graph-cut image segmentation was performed. The workflow was tested on 13 Sentinel-1A images from January to June 2016, and the results were evaluated on open water segmentation per ice charts provided by Canada Ice Service. The results showed that the proposed workflow was able to segment Sentinel-1 images in to appropriate number of classes, and the potential water identification rate reached 95%.
      PubDate: May 2018
      Issue No: Vol. 11, No. 5 (2018)
  • Snow Cover Mapping for Complex Mountainous Forested Environments Based on
           a Multi-Index Technique
    • Authors: Xiaoyan Wang;Jian Wang;Tao Che;Xiaodong Huang;Xiaohua Hao;Hongyi Li;
      Pages: 1433 - 1441
      Abstract: Seasonal snow cover is a critical component of the energy and water budgets of mountainous watersheds. Capturing the snow cover in complex environments is crucial for monitoring and understanding the temporal and spatial effects of climate change on alpine snow cover. The normalized difference snow index (NDSI) can be used to effectively and accurately estimate snow cover information from satellite images. However, the NDSI has limited utility for estimating the snow cover in heavily forested areas and relating this information to snowmelt-based runoff. In this study, a new algorithm based on a multi-index technique is proposed. The technique combines the NDSI, the normalized difference forest snow index, and the normalized difference vegetation index, and decision rules are established to increase the accuracy of snow mapping in forested areas. The new algorithm based on a multi-index technique is tested in the mountainous forested areas of North Xinjiang, China. In a winter image with full snow and a spring image with patchy snow, most of the forest snow, which is underestimated by the NDSI, is recognized by the multi-index technique. The accuracy of snow detection in forested areas is more than 90%. Additionally, in an experiment using a summer image without snow in forested areas no commission errors were detected. The snow detection algorithm based on a multi-index technique uses a simple set of decision rules for snow and can be run automatically without a priori knowledge of the surface characteristics.
      PubDate: May 2018
      Issue No: Vol. 11, No. 5 (2018)
  • Atmospheric Correction of Sea Ice Concentration Retrieval for 89 GHz
           AMSR-E Observations
    • Authors: Junshen Lu;Georg Heygster;Gunnar Spreen;
      Pages: 1442 - 1457
      Abstract: An improved sea ice concentration (SIC) retrieval algorithm named ASI2 that uses weather corrected polarization difference (PD) of brightness temperatures (TBs) at 89 GHz measured by AMSR-E/2 is developed. Effects of wind, total water vapor, liquid water path, and surface temperature on the TBs are evaluated through a radiative transfer model. TBs of open ocean yield higher sensitivity to the atmospheric water due to its low emissivity, whereas that of sea ice is more influenced by the surface conditions such as temperature and ice type. The weather effects are corrected by simulating changes in TBs caused by the atmospheric water absorption/emission and wind roughened ocean surface using numerical weather prediction reanalysis data fields as atmospheric profiles. ASI2 is validated on a collection of AMSR-E observations over open water and 100% SIC. The correction significantly reduces the standard deviation and bias of SIC over open water, yet yields little change over 100% SIC. Combined with an improved weather filter based on the corrected TBs at lower frequencies, ASI2 allows retrieval of low ice concentration and resolves a more exact ice concentration gradient across the ice edge compared to the original ASI algorithm.
      PubDate: May 2018
      Issue No: Vol. 11, No. 5 (2018)
  • Mapping Global Bamboo Forest Distribution Using Multisource Remote Sensing
    • Authors: Huaqiang Du;Fangjie Mao;Xuejian Li;Guomo Zhou;Xiaojun Xu;Ning Han;Shaobo Sun;Guolong Gao;Lu Cui;Yangguang Li;Dien Zhu;Yuli Liu;Liang Chen;Weiliang Fan;Pingheng Li;Yongjun Shi;Yufeng Zhou;
      Pages: 1458 - 1471
      Abstract: Bamboo forest has great potential in climate change mitigation. However, the spatiotemporal pattern of carbon storage of global bamboo forest is still cannot be accurately estimated, because the lack of an accurate global bamboo forest distribution information. In this paper, the global bamboo forest distribution was mapped with the following steps. To begin with, training samples were obtained based on investigation data, statistic data, and literature data. Then, a decision tree was constructed for mapping the global bamboo forest distribution by integrating Landsat 8 OLI and MODIS data. Finally, the global bamboo forest area was estimated using a pixel unmixing algorithm. The constructed decision tree succeeds in extracting global bamboo forest based on remote sensing data, where the overall accuracy of classification was 78.81%. The estimated total global bamboo forest area was 30538.35 × 103 ha, with a low root-mean-square error of 611.1 × 103 ha. The estimated bamboo forest area of each province in China and each country were high consistent with the National Forest Inventory in China and Food and Agriculture Organization of the United Nations statistic results (average R2 > 0.9), respectively. Therefore, the global bamboo forest map yielded a satisfactory accuracy in both classification and area estimation, and could provide accurate and significant support for global bamboo forest resource management and carbon cycle research.
      PubDate: May 2018
      Issue No: Vol. 11, No. 5 (2018)
  • Scaling Correction of Remotely Sensed Leaf Area Index for Farmland
           Landscape Pattern With Multitype Spatial Heterogeneities Using Fractal
           Dimension and Contextural Parameters
    • Authors: Ling Wu;Xiangnan Liu;Qiming Qin;Bingyu Zhao;Yujia Ma;Mengxue Liu;Tian Jiang;
      Pages: 1472 - 1481
      Abstract: High-accuracy retrieval of the crop leaf area index (LAI) in farmlands via remote sensing is the premise of reflecting the true growth condition of the crop. This paper aimed at scaling correction of LAI retrieval and developed an LAI scaling transfer model for farmland landscape pattern with multitype spatial heterogeneities according to the multiple types of farmland underlying surfaces in China. The interclass heterogeneity (caused by the alternate distribution of different cover types) and intraclass heterogeneity (caused by the difference in growth conditions within the same crop) both exist in the farmland landscape. The contextural parameters (fractions of components) and fractal dimension of the up-scaling pixel were used to quantitatively describe and correct the scaling effect caused by the two types of spatial heterogeneity, respectively. A scaling transfer model of inversed LAI was built by comprehensively considering intraclass and interclass heterogeneities. Results indicated that the LAI scaling bias of the up-scaling mixed pixel was mainly caused by the interclass heterogeneity even when the areal proportion of the noncrop component was low. The scaling transfer model corrected the scaling effect of LAI, with the root-mean-square error and mean absolute percentage error decreasing from 0.599 and 10.00% to 0.077 and 1.11%, respectively. The developed method based on fractal theory and contextural parameters effectively weakened the influence of the scaling effect on the accuracy of LAI retrieval.
      PubDate: May 2018
      Issue No: Vol. 11, No. 5 (2018)
  • Vegetation Indices Combining the Red and Red-Edge Spectral Information for
           Leaf Area Index Retrieval
    • Authors: Qiaoyun Xie;Jadu Dash;Wenjiang Huang;Dailiang Peng;Qiming Qin;Hugh Mortimer;Raffaele Casa;Stefano Pignatti;Giovanni Laneve;Simone Pascucci;Yingying Dong;Huichun Ye;
      Pages: 1482 - 1493
      Abstract: Leaf area index (LAI) is a crucial biophysical variable for agroecosystems monitoring. Conventional vegetation indices (VIs) based on red and near infrared regions of the electromagnetic spectrum, such as the normalized difference vegetation index (NDVI), are commonly used to estimate the LAI. However, these indices commonly saturate at moderate-to-dense canopies (e.g., NDVI saturates when LAI exceeds three). Modified VIs have then been proposed to replace the typical red/green spectral region with the red-edge spectral region. One significant and often ignored aspect of this modification is that the reflectance in the red-edge spectral region is comparatively sensitive to chlorophyll content which is highly variable between different crops and different phenological states. In this study, three improved indices are proposed combining reflectance both in the red and red-edge spectral regions into the NDVI, the modified simple ratio index (MSR), and the green chlorophyll index ( ${text{CI}}_{{text{green}}}$) formula. These improved indices are termed ${text{NDVI}}_{{text{red}& text{RE}}}$ (red and red-edge NDVI), ${text{MSR}}_{{text{red}& text{RE}}}$ (red and red-edge MSR index), and ${text{CI}}_{{text{red}& text{RE}}}$ (red and red-edge CI). The indices were tested using RapidEye images and in-situ data from campaigns at Maccarese Farm (Central Rome, Italy), in which four crop types at four different growth stages were measured. We investigated the predictive power of nine VIs for crop LAI estimation, including NDVI, MSR, and ${text{CI}}_{{text{green}}}$; the red-edge -odified indices: ${text{NDVI}}_{{text{Red-edge}}}$, ${text{MSR}}_{{text{Red-edge}}}$, and ${text{CI}}_{{text{Red-edge}}}$ (generally represented by ${text{VI}}_{{text{Red-edge}}}$); and the newly improved indices: ${text{NDVI}}_{{text{red}& text{RE}}}$ , ${text{MSR}}_{{text{red}& text{RE}}}$ , and ${text{CI}}_{{text{red}& text{RE}}}$ (generally represented by VI ${}_{text{red}& text{RE}}$). The results show that ${text{VI}}_{{text{red}& text{RE}}}$ improves the coefficient of determination (R2) for LAI estimation by 10% in comparison to ${text{VI}}_{{text{Red-edge}}}$. The newly improved indices prove to be the powerful alternatives for the LAI estimation of crops with wide chlorophyll range, and may provide valuable information for satellites equipped with red-edge channels (such as Sentinel-2) when applied to precision agriculture.
      PubDate: May 2018
      Issue No: Vol. 11, No. 5 (2018)
  • Analyzing the Influence of Wet Biomass Changes in Polarimetric
           Differential SAR Interferometry at L-Band
    • Authors: Virginia Brancato;Irena Hajnsek;
      Pages: 1494 - 1508
      Abstract: The displacement estimated with differential SAR interferometry (DInSAR) might not be unique when more than one polarization channel is available. For the case of agricultural areas, these ambiguities have been mainly related to complex vegetation dynamics, i.e., vegetation growth. This study intends to explore the potential of a synergistic use of DInSAR with SAR Polarimetry (PolDInSAR) in tracking changes within agricultural vegetation covers. The connection between the PolDInSAR observables (i.e., herein, the DInSAR phases at various polarization channels and/or their linear combinations) with wet biomass and soil water content changes is empirically investigated with linear regression techniques. This is done in the frame of an L-band airborne DInSAR dataset. The impact of vegetation vigor differs depending on the type of crop analyzed. For those crops exhibiting a birefringent electromagnetic propagation (i.e., barley, wheat, and rapeseed), the influence of wet biomass is particularly pronounced in the VV DInSAR phase but also in the HH–VV phase difference. Contrarily to the former, the latter shows also a scarce sensitivity to changes in soil water content. Therefore, this PolDInSAR observable is used to generate biomass maps of the analyzed test site. The predicted biomass variations are in good agreement with the collected in situ measurements, i.e., the coefficient of determination varies between 0.8 and 0.9.
      PubDate: May 2018
      Issue No: Vol. 11, No. 5 (2018)
  • Land Subsidence in Taiyuan, China, Monitored by InSAR Technique With
           Multisensor SAR Datasets From 1992 to 2015
    • Authors: Yuanyuan Liu;Chaoying Zhao;Qin Zhang;Chengsheng Yang;Jing Zhang;
      Pages: 1509 - 1519
      Abstract: Taiyuan city has been suffering significant subsidence during last two to three decades, mainly due to the effects of groundwater withdrawal and urban construction. The purpose of this study is to map the spatial-temporal variations of land subsidence over Taiyuan and analyze the causes of the observed deformations by using the interferometric point target analysis (IPTA) technique with multisensor SAR datasets during 1992 and 2015. The InSAR-derived deformations are then compared to the leveling measurements and groundwater data. The observed deformation based on ERS-1 datasets has mapped regional subsidence rate ranging from 30 to 60 mm/a in the northern and central Taiyuan from 1992 to 1993. InSAR measurements from Envisat ASAR, TerraSAR-X, and Radarsat-2 data reveal land subsidence rate up to 80 mm/a in the southern suburb during 2009 to 2015, whereas a rebound rate more than 10 mm/a in northern Taiyuan from 2004 to 2005. The time series deformation maps from 2009 to 2010 present slight nonlinear periodic variations, which might be caused by the seasonal groundwater fluctuations. The observed InSAR results indicate that the pattern of ground deformation is nearly concentric around locations of intense groundwater withdrawal, and the spatial extent of the subsiding area has been shrinking and moving toward the central and southern Taiyuan after 2003. Furthermore, differential subsidence rates are identified on both sides of Tianzhuang fault from observed deformation maps during the period of 2009–2010, 2012–2013, and 2014–2015, which could be explained that the fault acts as the barrier to the groundwater flow. Our results could provide significant evidence to serve the decision-making on land subsidence mitigation in Taiyuan, China.
      PubDate: May 2018
      Issue No: Vol. 11, No. 5 (2018)
  • Landslide Inventory Mapping From Bitemporal High-Resolution Remote Sensing
           Images Using Change Detection and Multiscale Segmentation
    • Authors: Zhi Yong Lv;Wenzhong Shi;Xiaokang Zhang;Jón Atli Benediktsson;
      Pages: 1520 - 1532
      Abstract: Landslide inventory mapping (LIM) plays an important role in hazard assessment and hazard relief. Even though much research has taken place in past decades, there is space for improvements in accuracy and the usability of mapping systems. In this paper, a new landslide inventory mapping framework is proposed based on the integration of the majority voting method and the multiscale segmentation of a postevent images, making use of spatial feature of landslide. Compared with some similar state-of-the-art methods, the proposed framework has three advantages: 1) the generation of LIM is almost automatic; 2) the framework can achieve more accurate results because it takes into account the landslide spatial information in an irregular manner through multisegmentation of the postevent image and object-based majority voting (MV); and 3) it needs less parameter tuning. The proposed framework was applied to four landslide sites on Lantau Island, Hong Kong. Compared with existing methods, including region level set evolution (RLSE), edge level set evolution (ELSE) and change detection Markov random field (CDMRF) methods, quantitative evaluation shows the proposed framework is competitive in terms of Completeness. The framework outperformed RLSE, ELSE, and CDMRF for the four experiments by more than 9% in Correctness and by 8% in Quality. To the authors’ knowledge, this is the first-time that landslide spatial information has been utilized through the integration of multiscale segmentation of postevent image with the MV approach to obtain LIM using high spatial resolution remote sensing images. The approach is also of wide generality and applicable to other kinds of land cover change detection using remote sensing images.
      PubDate: May 2018
      Issue No: Vol. 11, No. 5 (2018)
  • A Deep Neural Networks Approach to Automatic Recognition Systems for
           Volcano-Seismic Events
    • Authors: Manuel Titos;Angel Bueno;Luz García;Carmen Benítez;
      Pages: 1533 - 1544
      Abstract: Deep neural networks (DNNs) could help to identify the internal sources of volcano-seismic events. However, direct applications of DNNs are challenging, given the multiple seismic sources and the small size of available datasets. In this paper, we propose a novel approach in the field of volcano seismology to classify volcano-seismic events based on fully connected DNNs. Two DNN architectures with different weights scheme initialization are studied: stacked denoising autoencoders and deep belief networks. Using a combined feature vector of linear prediction coefficients and statistical properties, we evaluate classification performance on seven different classes of isolated seismic events. These proposed architectures are compared to multilayer perceptron, support vector machine, and random forest. Experimental results show that DNNs can efficiently capture complex relationships of volcano-seismic data and achieve better classification performance with faster convergence when compared to classical models.
      PubDate: May 2018
      Issue No: Vol. 11, No. 5 (2018)
  • Least-Square-Based Nonuniform Borehole SAR Imaging for Subsurface Sensing
    • Authors: Haining Yang;Na Li;Tingjun Li;Qing Huo Liu;
      Pages: 1545 - 1555
      Abstract: This paper presents the least-square-based nonuniform borehole synthetic aperture radar (SAR) imaging method with cosine accuracy factor for subsurface sensing. Based on the Stolt migration, the frequency-wavenumber spectrum of nonuniform data is efficiently approximated in the least-square-sense for the target space generation. The nonuniform power exponent basis is interpolated into several uniform power exponent bases with cosine accuracy factors, and then a virtual uniform sample set with a larger scale is generated for frequency-wavenumber spectrum approximation and imaging process. The proposed method can give accurate subsurface image result with nonuniform data at a greatly reduced computational cost. The approximation error and computational cost of the proposed method are analyzed and compared with those of Gaussian nonuniform imaging method. The imaging capabilities of the proposed method are theoretically simulated and experimentally demonstrated for distributed targets. The results show that the normalized mean square error and normalized maximum error of the proposed method are at least 8.07 dB and 4.29 dB, respectively, lower than those of conventional Stolt migration method. The imaging properties of this proposed method are shown to be superior to the conventional Stolt migration method, Gaussian nonuniform imaging method and Kirchhoff migration method, which is suitable for large nonuniform SAR imaging applications.
      PubDate: May 2018
      Issue No: Vol. 11, No. 5 (2018)
  • Feature Extraction From Multitemporal SAR Images Using Selforganizing Map
           Clustering and Object-Based Image Analysis
    • Authors: Donato Amitrano;Francesca Cecinati;Gerardo Di Martino;Antonio Iodice;Pierre-Philippe Mathieu;Daniele Riccio;Giuseppe Ruello;
      Pages: 1556 - 1570
      Abstract: We introduce a new architecture for feature extraction from multitemporal synthetic aperture radar (SAR) data. Its the purpose is to combine classic SAR processing and geographical object-based image analysis to provide a robust unsupervised tool for information extraction from time series images. The architecture takes advantage from the characteristics of the recently introduced RGB products of the Level-1 $alpha$ and Level-1$beta$ families, and employs self-organizing map clustering and object-based image analysis. In particular, the input products are clustered using color homogeneity and automatically enriched with a semantic attribute referring to clusters’ color, providing a preclassification mask. Then, in the frame of an application-oriented object-based image analysis, opportune layers measuring scattering and geometric properties of candidate objects are evaluated, and an appropriate rule-set is implemented in a fuzzy system to extract the feature of interest. The obtained results have been compared with those given by existing techniques and turned out to provide high degree of accuracy and negligible false alarms. The discussion is supported by an example concerning small reservoir mapping in semiarid environment.
      PubDate: May 2018
      Issue No: Vol. 11, No. 5 (2018)
  • SAR Image Land Cover Datasets for Classification Benchmarking of Temporal
    • Authors: Corneliu Octavian Dumitru;Gottfried Schwarz;Mihai Datcu;
      Pages: 1571 - 1592
      Abstract: The increased availability of high-resolution synthetic aperture radar (SAR) satellite images has led to new civil applications of these data. Among them is the systematic classification of land cover types based on the patterns of settlements or agriculture recorded by SAR imagers, in particular the identification and quantification of temporal changes. A systematic (re)classification shall allow the assignment of continuously updated semantic content labels to local image patches. This necessitates a careful selection of well-defined and discernible categories being contained in the image data that have to be trained and validated. These steps are well-established for optical images, while the peculiar imaging characteristics of SAR sensors often prevent a comparable approach. Especially, the vast range of SAR imaging parameters and the diversity of local targets impact the image product characteristics and need special care. In the following, we present guidelines and practical examples of how to obtain reliable image patch classification results for time series data with a limited number of given training data. We demonstrate that one can avoid the generation of simulated training data if we decompose the classification task into physically meaningful subsets of characteristic target properties and important imaging parameters. Then, the results obtained during training can serve as benchmarking figures for subsequent image classification. This holds for typical remote sensing examples such as coastal monitoring or the characterization of urban areas where we want to understand the transitions between individual land cover categories. For this purpose, a representative dataset can be obtained from the authors. A final proof of our concept is the comparison of classification results of selected target areas obtained by rather different SAR instruments. Despite the instrumental differences, the final results are surprisingly similar.
      PubDate: May 2018
      Issue No: Vol. 11, No. 5 (2018)
  • Monitoring Line-Infrastructure With Multisensor SAR Interferometry:
           Products and Performance Assessment Metrics
    • Authors: Ling Chang;Rolf P. B. J. Dollevoet;Ramon F. Hanssen;
      Pages: 1593 - 1605
      Abstract: Satellite radar interferometry (InSAR) is an emerging technique to monitor the stability and health of line-infrastructure assets, such as railways, dams, and pipelines. However, InSAR is an opportunistic approach as the location and occurrence of its measurements (coherent scatterers) cannot be guaranteed, and the quality of the InSAR products is not uniform. This is a problem for operational asset managers, who are used to surveying techniques that provide results with uniform quality at predefined locations. Therefore, advanced integrated products and generic performance assessment metrics are necessary. Here, we propose several new monitoring products and quality metrics for a-priori and a-posteriori performance assessment using multisensor InSAR. These products and metrics are demonstrated on a 125 km railway line-infrastructure asset in the Netherlands.
      PubDate: May 2018
      Issue No: Vol. 11, No. 5 (2018)
  • Polarimetric SAR Image Classification Using Geodesic Distances and
           Composite Kernels
    • Authors: Xiangli Yang;Wen Yang;Hui Song;Pingping Huang;
      Pages: 1606 - 1614
      Abstract: The covariance/coherence matrices are the most common way of representing polarimetric information in the polarimetric synthetic aperture radar (PolSAR) data and have been extensively used in PolSAR classification. Since PolSAR covariance and coherence matrices are Hermitian positive-definite, they form a nonlinear manifold, rather than Euclidean space. Though the geodesic distance measures defined on a manifold are suitable for describing similarities of PolSAR matrix data, the nonlinearity of the manifold often makes the involved optimization problems awkward. To address this problem, we propose to embed the manifold-based PolSAR data into a high (infinite)-dimensional reproducing kernel Hilbert space by Stein kernel and log-Euclidean kernel. Besides, we introduce the composite kernel into the sparse representation classification in order to exploit the spatial context information of PolSAR data. The proposed method is assessed using different PolSAR datasets. Experimental results demonstrate the superior performance compared with the methods without the use of contextual information.
      PubDate: May 2018
      Issue No: Vol. 11, No. 5 (2018)
  • Efficient InSAR Phase Noise Reduction via Compressive Sensing in the
           Complex Domain
    • Authors: Xiaomei Luo;Xiangfeng Wang;Yuhao Wang;Shengqi Zhu;
      Pages: 1615 - 1632
      Abstract: Two novel phase noise filtering algorithms for interferometric synthetic aperture radar (InSAR) are presented in this paper. Aiming at the nonlocal high self-similarity existing in the InSAR phase, we establish the phase noise filtering formulations with the $l_0$-norm regularizer and the $l_1$-norm regularizer, respectively. Although these two original formulations are nonconvex, we attempt to solve them by successive upper bound minimization combined with dictionary learning method. Specifically, for the noise reduction formulation with the $l_0$-norm regularizer, we first divide the original problem into a series of decoupled subproblems. Second, we obtain the approximate subproblem, which is locally tight upper bound of each subproblem by using a majorization–minimization technique. Third, we compute the sparse parameter vector for each approximate subproblem, followed by a matrix form update for the dictionary. The three steps are tackled cyclically until a satisfying solution is attained. The noise reduction problem with the $l_1$-norm regularizer is handled in a similar approach. We also establish the computational complexities of these two methods and summarize their distinct performance. Simulation results based on both synthetic data and simulated InAR data show that these two new InSAR phase noise reduction methods have much better performance than several existing phase filtering methods.
      PubDate: May 2018
      Issue No: Vol. 11, No. 5 (2018)
  • Symmetrical Dense-Shortcut Deep Fully Convolutional Networks for Semantic
           Segmentation of Very-High-Resolution Remote Sensing Images
    • Authors: Guanzhou Chen;Xiaodong Zhang;Qing Wang;Fan Dai;Yuanfu Gong;Kun Zhu;
      Pages: 1633 - 1644
      Abstract: Semantic segmentation has emerged as a mainstream method in very-high-resolution remote sensing land-use/land-cover applications. In this paper, we first review the state-of-the-art semantic segmentation models in both computer vision and remote sensing fields. Subsequently, we introduce two semantic segmentation frameworks: SNFCN and SDFCN, both of which contain deep fully convolutional networks with shortcut blocks. We adopt an overlay strategy as the postprocessing method. Based on our frameworks, we conducted experiments on two online ISPRS datasets: Vaihingen and Potsdam. The results indicate that our frameworks achieve higher overall accuracy than the classic FCN-8s and SegNet models. In addition, our postprocessing method can increase the overall accuracy by about 1%–2% and help to eliminate “salt and pepper” phenomena and block effects.
      PubDate: May 2018
      Issue No: Vol. 11, No. 5 (2018)
  • Dehazing for Multispectral Remote Sensing Images Based on a Convolutional
           Neural Network With the Residual Architecture
    • Authors: Manjun Qin;Fengying Xie;Wei Li;Zhenwei Shi;Haopeng Zhang;
      Pages: 1645 - 1655
      Abstract: Multispectral remote sensing images are often contaminated by haze, which causes low image quality. In this paper, a novel dehazing method based on a deep convolutional neural network (CNN) with the residual structure is proposed for multispectral remote sensing images. First, multiple CNN individuals with the residual structure are connected in parallel and each individual is used to learn a regression from the hazy image to the clear image. Then, the outputs of CNN individuals are fused with weight maps to produce the final dehazing result. In the designed network, the CNN individuals, mining multiscale haze features through multiscale convolutions, are trained using different levels of haze samples to achieve different dehazing abilities. In addition, the weight maps change with the haze distribution, and the fusion of the CNN individuals is adaptive. The designed network is end-to-end, and putting a hazy image into it, the clear scene can be restored. To train the network, a wavelength-dependent haze simulation method is proposed to generate labeled data, which can synthesize hazy multispectral images highly close to real conditions. Experimental results show that the proposed method can accurately remove the haze in each band of multispectral images under different scenes.
      PubDate: May 2018
      Issue No: Vol. 11, No. 5 (2018)
  • Remote Sensing Image Fusion With Deep Convolutional Neural Network
    • Authors: Zhenfeng Shao;Jiajun Cai;
      Pages: 1656 - 1669
      Abstract: Remote sensing images with different spatial and spectral resolution, such as panchromatic (PAN) images and multispectral (MS) images, can be captured by many earth-observing satellites. Normally, PAN images possess high spatial resolution but low spectral resolution, while MS images have high spectral resolution with low spatial resolution. In order to integrate spatial and spectral information contained in the PAN and MS images, image fusion techniques are commonly adopted to generate remote sensing images at both high spatial and spectral resolution. In this study, based on the deep convolutional neural network, a remote sensing image fusion method that can adequately extract spectral and spatial features from source images is proposed. The major innovation of this study is that the proposed fusion method contains a two branches network with the deeper structure which can capture salient features of the MS and PAN images separately. Besides, the residual learning is adopted in our network to thoroughly study the relationship between the high- and low-resolution MS images. The proposed method mainly consists of two procedures. First, spatial and spectral features are respectively extracted from the MS and PAN images by convolutional layers with different depth. Second, the feature fusion procedure utilizes the extracted features from the former step to yield fused images. By evaluating the performance on the QuickBird and Gaofen-1 images, our proposed method provides better results compared with other classical methods.
      PubDate: May 2018
      Issue No: Vol. 11, No. 5 (2018)
  • A New On-Orbit Geometric Self-Calibration Approach for the High-Resolution
           Geostationary Optical Satellite GaoFen4
    • Authors: Mi Wang;Yufeng Cheng;Yuan Tian;Luxiao He;Yanli Wang;
      Pages: 1670 - 1683
      Abstract: With the successful launch of GaoFen4 (GF4), on-orbit high accuracy geometric calibration for the high-resolution geostationary optical satellite will be a new research topic. With the improvement in the geometric resolution from geostationary orbit, it will become more and more difficult to meet the requirements of both high geometric resolution and large coverage for the available reference data. The purpose of this paper is to explore a new self-calibration mode for GF4 and future high-resolution geostationary optical area array cameras based on the fewest ground control points (GCPs). To overcome the problems of overparameterization, strong correlation and lower significance of the traditional rigorous imaging model, the simplified physical internal model is proposed, and its effectiveness in describing and compensating for the camera internal distortion is verified. Based on the simplified physical internal model, the self-calibration method based on two GCPs and evenly distributed tie points of two images is proposed for the high accuracy estimation of the calibration parameters. The GCPs can be used to provide the absolute geographical constraints for scale information, and the tie points can be used to provide the global constraints for optimum estimation. After calibration, the internal distortion is well compensated, and the positioning accuracy with relatively few GCPs is shown to be better than 1.0 pixels for both the panchromatic and near-infrared sensor and the intermediate infrared sensor. This paper will provide a new usable concept and approach for the future higher resolution geostationary area array optical camera to overcome the stringent requirements of both high resolution and a large area of reference data for the traditional calibration method.
      PubDate: May 2018
      Issue No: Vol. 11, No. 5 (2018)
  • Detection of Saturation in High-Resolution Pushbroom Satellite Imagery
    • Authors: Chunli Dai;Ian M. Howat;
      Pages: 1684 - 1693
      Abstract: Over the last decade, DigitalGlobe has launched a series of commercial Earth imaging satellites. These high-resolution satellite imageries provide an increasingly abundant data source for remote mapping of the Earth surface and its temporal variability. Among the factors affecting image quality is saturation of the charge-coupled device due to improper setting of the time delay integration level for the imaged surface, which results in along-track striping over areas of high radiance. We present and demonstrate an algorithm for the local detection of saturation striping by a wavelet transform, used to detect periodic variations of brightness (i.e., striping) with varying frequencies at different locations, combined with the use of unidirectional brightness gradients. The algorithm is applicable to raw, orthorectified, and resampled imagery. We test the algorithm using panchromatic images acquired by the GeoEye-1 and WorldView 1–3 sensors over polar regions. Saturation area classification masks generated by the algorithm agree well with manually identified areas of saturation. Manual validation of the algorithm applied to over 6000 images in Iceland reveals a high (>80%) success rate when the saturation levels are 2% or higher. Our general methodology may be widely applicable to periodic noise detection in imagery.
      PubDate: May 2018
      Issue No: Vol. 11, No. 5 (2018)
  • Simultaneous System Calibration of a Multi-LiDAR Multicamera Mobile
           Mapping Platform
    • Authors: Radhika Ravi;Yun-Jou Lin;Magdy Elbahnasawy;Tamer Shamseldin;Ayman Habib;
      Pages: 1694 - 1714
      Abstract: Mobile light detection and ranging (LiDAR) systems are widely used to generate precise 3-D spatial information, which in turn aids a variety of applications such as digital building model generation, transportation corridor asset management, telecommunications, precision agriculture, and infrastructure monitoring. Integrating such systems with one or more cameras would allow forward and backward projection between imagery and LiDAR data, thus facilitating several other high-level data processing activities, such as reliable feature extraction and colorization of point cloudsv. To increase the registration accuracy of point clouds derived from LiDAR data and imagery, along with their accuracy with respect to the ground truth, a simultaneous estimation of the mounting parameters relating the different laser scanners and cameras to the onboard global navigation satellite system (GNSS)/inertial navigation system (INS) unit is vital. This paper proposes a calibration procedure that directly estimates the mounting parameters for several spinning multibeam laser scanners and cameras onboard an airborne or terrestrial mobile platform. This procedure is based on the use of conjugate points and linear/planar features in overlapping images and point clouds derived from different drive-runs/flight lines. In order to increase the efficiency of semi-automatic conjugate feature extraction from the LiDAR data, specifically designed calibration boards covered by highly reflective surfaces that could be easily deployed and set up within an outdoor environment are used in this study. An experimental setup is used to evaluate the performance of the proposed calibration procedure for both airborne and terrestrial mobile mapping systems through the a posteriori variance factor of the least squares adjustment procedure and the quality of fit of the adjusted LiDAR point cloud and image points to linear/planar surfaces before and after the calibration process.
      PubDate: May 2018
      Issue No: Vol. 11, No. 5 (2018)
  • Robust Traffic-Sign Detection and Classification Using Mobile LiDAR Data
           With Digital Images
    • Authors: Haiyan Guan;Wanqian Yan;Yongtao Yu;Liang Zhong;Dilong Li;
      Pages: 1715 - 1724
      Abstract: This study aims at building a robust method for detecting and classifying traffic signs from mobile LiDAR point clouds and digital images. First, this method detects traffic signs from mobile LiDAR point clouds with regard to a prior knowledge of road width, pole height, reflectance, geometrical structure, and traffic-sign size. Then, traffic-sign images are segmented by projecting the detected traffic-sign points onto the digital images. Afterward, the segmented traffic-sign images are normalized for automatic classification with a given image size. Finally, a traffic-sign classifier is proposed based on a supervised Gaussian–Bernoulli deep Boltzmann machine model. We evaluated the proposed method using datasets acquired by a RIEGL VMX-450 system. The traffic-sign detection accuracy of 86.8% was achieved; through parameter sensitivity analysis, the overall performance of traffic-sign classification achieved a recognition rate of 93.3%. The computational performance showed that our method provides a promising solution to traffic-sign detection and classification using mobile LiDAR point clouds and digital images.
      PubDate: May 2018
      Issue No: Vol. 11, No. 5 (2018)
  • The Total Electron Content From InSAR and GNSS: A Midlatitude Study
    • Authors: Elvira Musicò;Claudio Cesaroni;Luca Spogli;John Peter Merryman Boncori;Giorgiana De Franceschi;Roberto Seu;
      Pages: 1725 - 1733
      Abstract: The total electron content (TEC) measured from the interferometric synthetic aperture radar (InSAR) and from a dense network of global navigation satellite system (GNSS) receivers are used to assess the capability of InSAR to retrieve ionospheric information, when the tropospheric contribution to the interferometric phase is reasonably negligible. With this aim, we select three night-time case studies over Italy and investigate the correlation between TEC from advanced land observing satellite-phased array type L-band synthetic aperture radar (ALOS-PALSAR) and from the Rete Integrata Nazionale GPS (RING) network, the latter considered as the reference true ionospheric TEC. To retrieve the TEC variability from ALOS-PALSAR, we first investigate the correlation between the integral of the azimuth shifts and the interferometric phase in the absence of ground motions (e.g., earthquakes) and/or heavy rain events. If correlation exists (as in two out of three case studies under investigation), we can assume the tropospheric contribution to the interferometric phase as negligible and the TEC variability from L-band InSAR can be retrieved. For these two case studies, the comparison between the TEC from the InSAR images and from the RING network is quite encouraging as the correlation coefficient is R ∼ 0.67 in the first case and R ∼ 0.83 in the second case. This result highlights the potential to combine InSAR and GNSS experimental measurements to investigate small-scale spatial variability of TEC, in particular over regions scarcely covered by ground-based GNSS receivers.
      PubDate: May 2018
      Issue No: Vol. 11, No. 5 (2018)
  • Boresight Calibration of GNSS/INS-Assisted Push-Broom Hyperspectral
           Scanners on UAV Platforms
    • Authors: Ayman Habib;Tian Zhou;Ali Masjedi;Zhou Zhang;John Evan Flatt;Melba Crawford;
      Pages: 1734 - 1749
      Abstract: Low-cost unmanned aerial vehicles (UAVs) utilizing push-broom hyperspectral scanners are poised to become a popular alternative to conventional remote sensing platforms such as manned aircraft and satellites. In order to employ this emerging technology in fields such as high-throughput phenotyping and precision agriculture, direct georeferencing of hyperspectral data using onboard integrated global navigation satellite systems (GNSSs) and inertial navigation systems (INSs) is required. Directly deriving the scanner position and orientation requires the spatial and rotational relationship between the coordinate systems of the GNSS/INS and hyperspectral scanner to be measured. The spatial offset (lever arm) between the scanner and GNSS/INS unit can be measured manually. However, the angular relationship (boresight angles) between the scanner and GNSS/INS coordinate systems, which is more critical for accurate generation of georeferenced products, is difficult to establish. This paper presents three calibration approaches to estimate the boresight angles relating hyperspectral push-broom scanner and GNSS/INS coordinate systems. For reliable/practical estimation of the boresight angles, this paper starts with establishing the optimal/minimal flight and control/tie point configuration through a bias impact analysis starting from the point positioning equation. Then, an approximate calibration procedure utilizing tie points in overlapping scenes is presented after making some assumptions about the flight trajectory and topography of covered terrain. Next, two rigorous approaches are introduced – one using ground control points and other using tie features. The approximate/rigorous approaches are based on enforcing the collinearity and coplanarity of the light rays connecting the perspective centers of the imaging scanner, object point, and the respective image points. To evaluate the accuracy of the proposed approaches, estimated boresight angles are u-ed for orthorectification of six hyperspectral UAV dataset acquired over an agricultural field. Qualitative and quantitative evaluations of the results have shown significant improvement in the derived orthophotos to a level equivalent to the ground sampling distance of the used scanner (namely, 3–5 cm when flying at 60 m).
      PubDate: May 2018
      Issue No: Vol. 11, No. 5 (2018)
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      Issue No: Vol. 11, No. 5 (2018)
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      Issue No: Vol. 11, No. 5 (2018)
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      Issue No: Vol. 11, No. 5 (2018)
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