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  Subjects -> ELECTRONICS (Total: 194 journals)
Showing 1 - 200 of 277 Journals sorted alphabetically
Acta Electronica Malaysia     Open Access  
Advanced Materials Technologies     Hybrid Journal  
Advances in Electrical and Electronic Engineering     Open Access   (Followers: 9)
Advances in Electronics     Open Access   (Followers: 94)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 8)
Advances in Power Electronics     Open Access   (Followers: 39)
Advancing Microelectronics     Hybrid Journal  
Aerospace and Electronic Systems, IEEE Transactions on     Hybrid Journal   (Followers: 352)
American Journal of Electrical and Electronic Engineering     Open Access   (Followers: 28)
Annals of Telecommunications     Hybrid Journal   (Followers: 9)
APSIPA Transactions on Signal and Information Processing     Open Access   (Followers: 9)
Archives of Electrical Engineering     Open Access   (Followers: 15)
Australian Journal of Electrical and Electronics Engineering     Hybrid Journal  
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 8)
Batteries     Open Access   (Followers: 7)
Batteries & Supercaps     Hybrid Journal   (Followers: 3)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 31)
Bioelectronics in Medicine     Hybrid Journal  
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 22)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 38)
Biomedical Instrumentation & Technology     Hybrid Journal   (Followers: 6)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 13)
BULLETIN of National Technical University of Ukraine. Series RADIOTECHNIQUE. RADIOAPPARATUS BUILDING     Open Access   (Followers: 2)
Bulletin of the Polish Academy of Sciences : Technical Sciences     Open Access   (Followers: 1)
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 47)
China Communications     Full-text available via subscription   (Followers: 9)
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: 301)
ECTI Transactions on Computer and Information Technology (ECTI-CIT)     Open Access  
ECTI Transactions on Electrical Engineering, Electronics, and Communications     Open Access   (Followers: 2)
Edu Elektrika Journal     Open Access   (Followers: 1)
Electrica     Open Access  
Electronic Design     Partially Free   (Followers: 123)
Electronic Markets     Hybrid Journal   (Followers: 7)
Electronic Materials Letters     Hybrid Journal   (Followers: 4)
Electronics     Open Access   (Followers: 104)
Electronics and Communications in Japan     Hybrid Journal   (Followers: 10)
Electronics For You     Partially Free   (Followers: 103)
Electronics Letters     Hybrid Journal   (Followers: 26)
Elkha : Jurnal Teknik Elektro     Open Access  
Embedded Systems Letters, IEEE     Hybrid Journal   (Followers: 55)
Energy Harvesting and Systems     Hybrid Journal   (Followers: 4)
Energy Storage     Hybrid Journal   (Followers: 1)
Energy Storage Materials     Full-text available via subscription   (Followers: 4)
EPE Journal : European Power Electronics and Drives     Hybrid Journal  
EPJ Quantum Technology     Open Access   (Followers: 1)
EURASIP Journal on Embedded Systems     Open Access   (Followers: 11)
Facta Universitatis, Series : Electronics and Energetics     Open Access  
Foundations and Trends® in Communications and Information Theory     Full-text available via subscription   (Followers: 6)
Foundations and Trends® in Signal Processing     Full-text available via subscription   (Followers: 10)
Frequenz     Hybrid Journal   (Followers: 1)
Frontiers of Optoelectronics     Hybrid Journal   (Followers: 1)
Geoscience and Remote Sensing, IEEE Transactions on     Hybrid Journal   (Followers: 218)
Haptics, IEEE Transactions on     Hybrid Journal   (Followers: 4)
IACR Transactions on Symmetric Cryptology     Open Access  
IEEE Antennas and Propagation Magazine     Hybrid Journal   (Followers: 100)
IEEE Antennas and Wireless Propagation Letters     Hybrid Journal   (Followers: 81)
IEEE Journal of Emerging and Selected Topics in Power Electronics     Hybrid Journal   (Followers: 51)
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: 75)
IEEE Transactions on Antennas and Propagation     Full-text available via subscription   (Followers: 73)
IEEE Transactions on Automatic Control     Hybrid Journal   (Followers: 59)
IEEE Transactions on Circuits and Systems for Video Technology     Hybrid Journal   (Followers: 26)
IEEE Transactions on Consumer Electronics     Hybrid Journal   (Followers: 44)
IEEE Transactions on Electron Devices     Hybrid Journal   (Followers: 19)
IEEE Transactions on Information Theory     Hybrid Journal   (Followers: 26)
IEEE Transactions on Power Electronics     Hybrid Journal   (Followers: 78)
IEEE Transactions on Signal and Information Processing over Networks     Full-text available via subscription   (Followers: 14)
IEICE - Transactions on Electronics     Full-text available via subscription   (Followers: 12)
IEICE - Transactions on Information and Systems     Full-text available via subscription   (Followers: 5)
IET Cyber-Physical Systems : Theory & Applications     Open Access   (Followers: 1)
IET Energy Systems Integration     Open Access   (Followers: 1)
IET Microwaves, Antennas & Propagation     Hybrid Journal   (Followers: 35)
IET Nanodielectrics     Open Access  
IET Power Electronics     Hybrid Journal   (Followers: 57)
IET Smart Grid     Open Access   (Followers: 1)
IET Wireless Sensor Systems     Hybrid Journal   (Followers: 18)
IETE Journal of Education     Open Access   (Followers: 4)
IETE Journal of Research     Open Access   (Followers: 11)
IETE Technical Review     Open Access   (Followers: 13)
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)     Open Access   (Followers: 3)
Industrial Electronics, IEEE Transactions on     Hybrid Journal   (Followers: 74)
Industrial Technology Research Journal Phranakhon Rajabhat University     Open Access  
Industry Applications, IEEE Transactions on     Hybrid Journal   (Followers: 38)
Informatik-Spektrum     Hybrid Journal   (Followers: 2)
Instabilities in Silicon Devices     Full-text available via subscription   (Followers: 1)
Intelligent Transportation Systems Magazine, IEEE     Full-text available via subscription   (Followers: 13)
International Journal of Advanced Research in Computer Science and Electronics Engineering     Open Access   (Followers: 18)
International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems     Open Access   (Followers: 12)
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: 5)
International Journal of Control     Hybrid Journal   (Followers: 11)
International Journal of Electronics     Hybrid Journal   (Followers: 7)
International Journal of Electronics and Telecommunications     Open Access   (Followers: 13)
International Journal of Granular Computing, Rough Sets and Intelligent Systems     Hybrid Journal   (Followers: 3)
International Journal of High Speed Electronics and Systems     Hybrid Journal  
International Journal of Hybrid Intelligence     Hybrid Journal  
International Journal of Image, Graphics and Signal Processing     Open Access   (Followers: 16)
International Journal of Microwave and Wireless Technologies     Hybrid Journal   (Followers: 10)
International Journal of Nanoscience     Hybrid Journal   (Followers: 1)
International Journal of Numerical Modelling: Electronic Networks, Devices and Fields     Hybrid Journal   (Followers: 4)
International Journal of Power Electronics     Hybrid Journal   (Followers: 25)
International Journal of Review in Electronics & Communication Engineering     Open Access   (Followers: 4)
International Journal of Sensors, Wireless Communications and Control     Hybrid Journal   (Followers: 10)
International Journal of Systems, Control and Communications     Hybrid Journal   (Followers: 4)
International Journal of Wireless and Microwave Technologies     Open Access   (Followers: 6)
International Transaction of Electrical and Computer Engineers System     Open Access   (Followers: 2)
JAREE (Journal on Advanced Research in Electrical Engineering)     Open Access  
Journal of Biosensors & Bioelectronics     Open Access   (Followers: 4)
Journal of Advanced Dielectrics     Open Access   (Followers: 1)
Journal of Artificial Intelligence     Open Access   (Followers: 12)
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: 36)
Journal of Electrical Bioimpedance     Open Access  
Journal of Electrical Bioimpedance     Open Access   (Followers: 2)
Journal of Electrical Engineering & Electronic Technology     Hybrid Journal   (Followers: 7)
Journal of Electrical, Electronics and Informatics     Open Access  
Journal of Electromagnetic Analysis and Applications     Open Access   (Followers: 8)
Journal of Electromagnetic Waves and Applications     Hybrid Journal   (Followers: 9)
Journal of Electronic Design Technology     Full-text available via subscription   (Followers: 6)
Journal of Electronic Science and Technology     Open Access  
Journal of Electronics (China)     Hybrid Journal   (Followers: 5)
Journal of Energy Storage     Full-text available via subscription   (Followers: 4)
Journal of Engineered Fibers and Fabrics     Open Access   (Followers: 2)
Journal of Field Robotics     Hybrid Journal   (Followers: 3)
Journal of Guidance, Control, and Dynamics     Hybrid Journal   (Followers: 182)
Journal of Information and Telecommunication     Open Access   (Followers: 1)
Journal of Intelligent Procedures in Electrical Technology     Open Access   (Followers: 3)
Journal of Low Power Electronics     Full-text available via subscription   (Followers: 10)
Journal of Low Power Electronics and Applications     Open Access   (Followers: 10)
Journal of Microelectronics and Electronic Packaging     Hybrid Journal  
Journal of Microwave Power and Electromagnetic Energy     Hybrid Journal   (Followers: 3)
Journal of Microwaves, Optoelectronics and Electromagnetic Applications     Open Access   (Followers: 11)
Journal of Nuclear Cardiology     Hybrid Journal  
Journal of Optoelectronics Engineering     Open Access   (Followers: 4)
Journal of Physics B: Atomic, Molecular and Optical Physics     Hybrid Journal   (Followers: 30)
Journal of Power Electronics & Power Systems     Full-text available via subscription   (Followers: 11)
Journal of Semiconductors     Full-text available via subscription   (Followers: 5)
Journal of Sensors     Open Access   (Followers: 26)
Journal of Signal and Information Processing     Open Access   (Followers: 9)
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer     Open Access  
Jurnal Rekayasa Elektrika     Open Access  
Jurnal Teknik Elektro     Open Access  
Jurnal Teknologi Elektro     Open Access  
Kinetik : Game Technology, Information System, Computer Network, Computing, Electronics, and Control     Open Access  
Learning Technologies, IEEE Transactions on     Hybrid Journal   (Followers: 12)
Magnetics Letters, IEEE     Hybrid Journal   (Followers: 7)
Majalah Ilmiah Teknologi Elektro : Journal of Electrical Technology     Open Access   (Followers: 2)
Metrology and Measurement Systems     Open Access   (Followers: 6)
Microelectronics and Solid State Electronics     Open Access   (Followers: 28)
Nanotechnology Magazine, IEEE     Full-text available via subscription   (Followers: 42)
Nanotechnology, Science and Applications     Open Access   (Followers: 6)
Nature Electronics     Hybrid Journal   (Followers: 1)
Networks: an International Journal     Hybrid Journal   (Followers: 5)
Open Electrical & Electronic Engineering Journal     Open Access  
Open Journal of Antennas and Propagation     Open Access   (Followers: 9)
Optical Communications and Networking, IEEE/OSA Journal of     Full-text available via subscription   (Followers: 16)
Paladyn. Journal of Behavioral Robotics     Open Access   (Followers: 1)
Power Electronics and Drives     Open Access   (Followers: 2)
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: 4)
Recent Advances in Electrical & Electronic Engineering     Hybrid Journal   (Followers: 11)
Research & Reviews : Journal of Embedded System & Applications     Full-text available via subscription   (Followers: 6)
Revue Méditerranéenne des Télécommunications     Open Access  
Security and Communication Networks     Hybrid Journal   (Followers: 2)
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of     Hybrid Journal   (Followers: 56)
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: 79)
Solid State Electronics Letters     Open Access  
Solid-State Circuits Magazine, IEEE     Hybrid Journal   (Followers: 13)
Solid-State Electronics     Hybrid Journal   (Followers: 9)
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: 9)
Transactions on Electrical and Electronic Materials     Hybrid Journal   (Followers: 1)
Universal Journal of Electrical and Electronic Engineering     Open Access   (Followers: 7)
Ural Radio Engineering Journal     Open Access   (Followers: 1)
Visión Electrónica : algo más que un estado sólido     Open Access   (Followers: 1)
Wireless and Mobile Technologies     Open Access   (Followers: 6)
Wireless Power Transfer     Full-text available via subscription   (Followers: 4)
Women in Engineering Magazine, IEEE     Full-text available via subscription   (Followers: 11)
Електротехніка і Електромеханіка     Open Access   (Followers: 1)

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Similar Journals
Journal Cover
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of
Journal Prestige (SJR): 1.547
Citation Impact (citeScore): 4
Number of Followers: 56  
  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: Nov. 2019
      Issue No: Vol. 12, No. 11 (2019)
  • IEEE Geoscience and Remote Sensing Society
    • 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: Nov. 2019
      Issue No: Vol. 12, No. 11 (2019)
  • Information for Authors
    • Abstract: These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.
      PubDate: Nov. 2019
      Issue No: Vol. 12, No. 11 (2019)
  • Institutional Listings
    • Abstract: Presents listing on institutions associated with this publication.
      PubDate: Nov. 2019
      Issue No: Vol. 12, No. 11 (2019)
  • Foreword Remote Sensing for Environmental Sustainability in the
           Asian–Pacific Region
    • Pages: 4172 - 4174
      Abstract: The papers in this special section examine the use of remote sensing technology to promote environmental sustainability in Asia-Pacific regions. Worldwide urbanization and deforestation are the two main interconnected ways that human activities are continually changing and reshaping the earth’s surface. How earth observation and remote sensing technologies can contribute to improve the knowledge of the productivity and sustainability of natural and human ecosystems is an important theme in the global change community. In China, for instance, rapid economic growth and urbanization over the past three decades have resulted in dramatic changes in land use and land cover and have led to severe environmental consequences, which have made China’s sustainable development a grand challenge. In the meantime, during the past few decades, environmental changes in the Asian–Pacific region have posed significant challenges to the scientific community. Therefore, the global problem of how earth observation and remote sensing technologies may be applied to assessing, monitoring, modeling, and simulating ecosystems, environments, and resources at various spatial and temporal scales translates into peculiar and very urgent questions and applications in this colossal and dynamic geographical region.
      PubDate: Nov. 2019
      Issue No: Vol. 12, No. 11 (2019)
  • Estimation of Forest Structural Parameters Using UAV-LiDAR Data and a
           Process-Based Model in Ginkgo Planted Forests
    • Authors: Lin Cao;Kun Liu;Xin Shen;Xiangqian Wu;Hao Liu;
      Pages: 4175 - 4190
      Abstract: Developing an accurate model for estimating the forest structural parameters of planted forests is crucial for forest productivity predictions and can provide a better understanding of the carbon cycle under climate change. Unmanned aerial vehicle-light detecting and ranging (UAV-LiDAR) systems represents a promising active remote sensing technology that has the potential to be used for forest inventories. In addition, the process-based model, physiological principles predicting growth (3-PG), which is based on physiological principles and environmental factors, has been applied to estimate the growth of even-aged, mono-specific forests under the effect of different management levels, site conditions, and climate change. In this study, the performance of UAV-LiDAR metrics was assessed and applied to estimate forest structural parameters using a multivariate linear regression (MLR) method. The 3-PG was parameterized and used to simulate the diameter at breast height, stem density, volume and above-ground biomass of a planted ginkgo forest in eastern China. In addition, a sensitivity analysis was conducted on the 3-PG model's input parameters. The results demonstrated that both the MLR based on UAV-LiDAR data and a progress model of the 3-PG have a promising potential for estimating forest structural parameters (R2> 0.70, relative root squared error>20%). A sensitivity analysis of the 3-PG parameters also confirmed that the parameter “age at canopy cover” (fullCanAge) is vital for the 3-PG model, and positively correlation with the simulated results. The method presented here represents an improvement on traditional methods for estimating forest structural parameters because it more explicitly accounts for climatic effects included in the 3-PG model.
      PubDate: Nov. 2019
      Issue No: Vol. 12, No. 11 (2019)
  • The Influence of Seasonal and Interannual Variability on Surface
           Chlorophyll-a Off the Western Lesser Sunda Islands
    • Authors: Riza Yuliratno Setiawan;Eko Setyobudi;Anindya Wirasatriya;Andi Syahid Muttaqin;Lilik Maslukah;
      Pages: 4191 - 4197
      Abstract: Novel and long-term satellite data (2003–2017) are analyzed to investigate the variability of ocean surface chlorophyll-a (Chl-a) concentration off the Western Lesser Sunda Islands (WLSI) under the influence of the Indonesian Australian monsoon, the El Niño-Southern Oscillation, and the Indian Ocean Dipole (IOD). In this article, we first analyzed the seasonal variability of Chl-a, and then describe the relationship among the sea surface Chl-a, sea surface temperature (SST), and sea surface wind stress in the region. Our results demonstrate that prevailing southeasterly wind stress plays a pivotal role in generating the Chl-a maxima off the WLSI. Particularly on seasonal time scale, the strengthening of southeasterly wind stress (up to ∼0.01 N·m−2) during the southeast monsoon season produces enhanced Chl-a concentrations (0.59 mg·m−3) associated with sea surface cooling (∼28.8 °C) in the area of study. In contrast, the Chl-a maxima completely vanished during the northwest monsoon season. On interannual time scale, the largest positive Chl-a and wind stress anomalies and the coolest SST anomaly are observed in 2006 when El Niño and positive IOD events occur at the same time. Meanwhile, the greatest negative Chl-a anomaly is prevailed during the 2016 negative IOD event. This article demonstrates that wind variability is the essential factor in determining the magnitude of the Chl-a maxima off the WLSI.
      PubDate: Nov. 2019
      Issue No: Vol. 12, No. 11 (2019)
  • Remote Monitoring of PSD Slope Under the Influence of Sand Dredging
           Activities in Lake Hongze Based on Landsat-8/OLI Data and VIIRS/DNB
           Night-Time Light Composite Data
    • Authors: Shaohua Lei;Jie Xu;Yunmei Li;Chenggong Du;Meng Mu;Shuai Zeng;Song Miao;Shun Bi;Qiao Wang;Heng Lyu;
      Pages: 4198 - 4212
      Abstract: Particle size distribution (PSD) is an important characterization parameter of the particle size structure of suspended particles, which is vulnerable to human activities such as sand dredging. In this study, sand dredging numbers were evaluated by the accumulated radiance algorithm using the night-time light composite data of the visible infrared imaging radiometer suite, and the PSD slope was derived by band ratio algorithm of Landsat-8/operational land imager (OLI) imagery. Then, the influence of sand mining on PSD slope was analyzed. We draw several crucial findings. First, the ratio of ${boldsymbol{R}_{{{bf rs}}({{{bf Red}}})}}$ to${boldsymbol{R}_{{{bf rs}}({{{bf Green}}})}}$ based on OLI imagery was capable of retrieving PSD slope with good robustness (R2 = 0.71, p < 0.01, n = 41) and verified acceptance (mean absolute percentage error = 4.29%, root mean square error = 0.19, n = 20), and the PSD slope in HZL was lower in summer than in the other seasons from 2014 to 2017. Second, The night active vessels extracted from the night-time images show that sand dredging activities began at the outlet of Chengzi Lake (CZL) in April 2012, spread to the whole lake between 2014 and 2016, and then stopped in the northeast region in March 2017 due to the strict control of the local government. Third, the PSD slope is strongly influenced by sand mining, which is positively correlated with the number of sand dredgers (R2 = 0.92), i.e., the PSD slope increases 0.21 per 100 working sand dredgers in CZL.
      PubDate: Nov. 2019
      Issue No: Vol. 12, No. 11 (2019)
  • Analysis of the Spatial and Temporal Variations of Land Surface
           Temperature Based on Local Climate Zones: A Case Study in Nanjing, China
    • Authors: Jia Hu;Yingbao Yang;Xin Pan;Qin Zhu;Wenfeng Zhan;Yong Wang;Wenyu Ma;Weizhong Su;
      Pages: 4213 - 4223
      Abstract: The concept of local climate zone (LCZ) has standardized the calculation of urban heat island (UHI) intensity (UHII) and established the connection among urban morphology, surface property, and UHI. In addition, LCZ has provided a new insight into the studies on urban thermal environment. This study selected Nanjing, China, as the study area and utilized a combined method that comprised remote sensing based and geographic information system based methods based on random forest classifier for LCZ classification. Overall accuracy reached 92%, and kappa coefficient was 0.911. In addition, the seasonal and diurnal differences of land surface temperature (LST) were analyzed via LST retrieval from Landsat data and computational fluid dynamics model simulation, respectively. Results confirmed the warmest and coldest zones in four seasons. The LST distribution characteristics of land cover and built types were basically the same during the four seasons. Moreover, the UHII difference of an LCZ class in various seasons and the UHII difference of a season in various LCZs were investigated. The daily LSTs of the simulated LCZs (1 to 6) within 24 h showed the same variation law but different variation extents in a day. The LST variations of built types were related to building elements, such as building height, building density, building layout, and green ratio. This study identified an existing relation between LST and LCZ and analyzed LST on the basis of LCZs from seasonal and diurnal scales, which provides guidance for future researchers.
      PubDate: Nov. 2019
      Issue No: Vol. 12, No. 11 (2019)
  • Changes of Wiang Nong Lom and Nong Luang Wetlands in Chiang Saen Valley
           (Chiang Rai Province, Thailand) During the Period 1988–2017
    • Authors: Nuttiga Hempattarasuwan;George Christakos;Jiaping Wu;
      Pages: 4224 - 4238
      Abstract: Pressure on the Wiang Nong Lom and Nong Luang wetland resources in the Chiang Saen Valley of Chiang Rai Province (Thailand) has increased in recent years with the expansion of farmlands and other major sources of wetland conflict related to public land encroachment. Both of these wetlands have been designated as strategic ecosystems. Yet, there is a limited understanding of the way different wetlands respond to change drivers (agriculture, climate, population, etc.), and currently no scientifically valid protocols exist for local wetland mapping and monitoring. Distinguishing between small wetlands and land use and land cover (LULC) components is a challenging affair due to the highly heterogeneous landscape and spectral similarity of compositionally different types of tropical regions. The goals of this article are both technological and substantive, i.e., it aims to (A) propose a synthesis of quantitative techniques that can improve LULC mapping using remotely sensed data (Landsat TM, ETM+, OLI), and (B) assess the wetland changes during the last three decades and better understand the interaction between wetland changes, human population, and the environment. In regards to goal (A), the proposed classification approach employed a synthesis of techniques of decision tree classification (DTC), maximum likelihood classification (MLC), and Mahalanobis distance classification (MDC), with different bands and ancillary data inputs. The results demonstrated that the implementation of the DTC algorithms to address LULC mapping problems exhibited an overall mapping accuracy of 83.9%, which is significantly higher than that of MLC and MDC. It was found that the DTC technique performs well when combined with visible, near-infrared (NIR), and shortwave-infrared bands, a digital elevation model and normalized difference vegetation index layers. Subsequently, the postclassification analysis using DTC showed a notable improvement of approxi-ately 88.0% classification accuracy. Regarding goal (B), our results showed that during the last 30 years, wetland areas in the Chiang Saen Valley have experienced a dramatic decrease of 30.5%, whereas forest landscape surrounding the wetlands has decreased by an astonishing 50.9%. Contrarily, we found that agricultural land size has increased by 24.3%. We suggest that ground data can be linked to the etiology of these results, including the gradual conversion of wetlands to rice cultivation fields as a result of the government rice pledging scheme. Large areas in the study region have been cultivated by para-rubber, palm oil, and pineapple agribusiness production since 2003. In addition, short-term subsidizing government policies promote intensive production for commercial agriculture prompting farmers to transition from subsistence to commercial farming, further impacting wetland conversion. As a result, and in further view of the fact that rapidly expanding agricultural areas have contributed significantly to the decrease of wetland areas during the last three decades, the Chiang Saen Valley wetlands have been designated as wetlands of international importance. The overall recommendation of the present work is that special land-use policy and relevant regulation and/or legislation are critical components of any effort to achieve wetland sustainability.
      PubDate: Nov. 2019
      Issue No: Vol. 12, No. 11 (2019)
  • Integration of Analytical Hierarchy Process and Landslide Susceptibility
           Index Based Landslide Susceptibility Assessment of the Pearl River Delta
           Area, China
    • Authors: Haoran Zhang;Guifang Zhang;Qiwen Jia;
      Pages: 4239 - 4251
      Abstract: Landslide is one of the most disastrous geological hazards in the Pearl River Delta area. In the article, landslide susceptibility index (LSI) and analytical hierarchy process (AHP) have been adopted to assess landslide susceptibility (LS) of the Pearl River Delta area. A total of 294 historical landslide sites were extracted from China Geological Survey Result CGS2015-008 and remote sensing images as landslide inventory. In each experiment, 198 landslide points were randomly selected as training samples and the remaining 96 as verification samples. Nine influencing factors, i.e., standard deviation (STD) of elevation, terrain roughness, curvature, lithology, fault, land use, water density, road density, and aspect, were obtained through ASTER GDEM, China Geological Survey Result CGS2015-008, Geographical Information Monitoring Cloud Platform, Open Street Map, and Google Earth. LSI was used to represent the contribution of each category within the influencing factors to the occurrence of landslides (LSIi), and AHP was used to calculate the weights between different influencing factors (Wi). Finally, the summation of the product of LSIi by Wi was used to represent the LS value for each pixel. Then, the study area was grouped into five susceptibility classes based on the landslide susceptibility. It is indicated that terrain factors have the greatest impact on landslides, followed by engineering geological lithology and land use types. The high and very high susceptibility classes were mainly distributed in the area of: 1) STD of elevation of 0.8–2.5, roughness of 1.004–1.030, and curvature 2.7–7.2; 2) sand-shale stone and coal, bedded clastic rock, and bedded epimetamorphic rock; and 3) woodland and the urban and rural land. The receiver operating characteristic curves of many experimental results prove that the model has fine prediction performance and good stability. This article coul- provide a basis for landslide prevention and land development in the Pearl River Delta area.
      PubDate: Nov. 2019
      Issue No: Vol. 12, No. 11 (2019)
  • Urban Observation: Integration of Remote Sensing and Social Media Data
    • Authors: Lin Qi;Jie Li;Ying Wang;Xinbo Gao;
      Pages: 4252 - 4264
      Abstract: Urban and regional research is crucial for many applications such as public participation, land use, disaster management, and environmental monitoring, but it is a time-consuming and expensive procedure to collect necessary data. Remote sensing is an effective technology for urban observation, however, the spatial, spectral, and temporal resolution of observation sensors limits many situations in which remote sensing data cannot be fully utilized, especially in the field of urban observation. Recently, with the popularity of internet and smart mobile devices, social media containing spatial information is evolving rapidly, such as tweets, Flickr photos, and geolocated posts. These location-based social media data are leading new research areas, new technologies and methods, and new insights into urban observation. In this paper, we provide an overview on the integration and joint analysis of remote sensing and social media data in urban observation applications. We describe four opportunities in exploiting social media data: to investigate the relationship among humans, environment and urban, to help urban planning, to manage urban disaster, and to monitor urban environment. Although significant possibilities for a combination of remote sensing and social media data can be seen, our survey suggests that the fusion of these data sources will continue to profoundly change these technologies.
      PubDate: Nov. 2019
      Issue No: Vol. 12, No. 11 (2019)
  • Improving Impervious Surface Estimation by Using Remote Sensed Imagery
           Combined With Open Street Map Points-of-Interest (POI) Data
    • Authors: Wei Fan;Changshan Wu;Jin Wang;
      Pages: 4265 - 4274
      Abstract: As a key indicator of urban environments, the accurate mapping of impervious surface is essential. With the availability of high spatial resolution remote sensing imagery, such as Sentinel-2, it is feasible to produce fine-scale impervious surface maps. For mapping high-resolution impervious surfaces, object-based image analysis (OBIA) classification method demonstrated its efficiency and accuracy by combining spectral information and spatial information. Although with some success, impervious surface estimation remains challenging because different land cover types share similar spectral information. With the emergence of affordable GPS-enabled devices (such as a smartphone) and web 2.0 development, more and more people are getting involved in sharing their locations with others or posting on the Internet. These volunteered geographical information (VGI) data provide a brand-new prospect for mapping urban impervious surfaces. This research proposes an optimized method for impervious surface mapping based on Sentinel-2 multispectral imagery and open street map (OSM) points-of-interest (POI) data. The proposed method was tested in Milwaukee county, US, and the results show that the overall accuracy of the proposed OBIA increases from 82.57% to 87.02% compared with the conventional OBIA. Thus, this study provides an effective means of combining OBIA and the relatively new VGI POIs data to extract impervious surface with higher spatial resolution.
      PubDate: Nov. 2019
      Issue No: Vol. 12, No. 11 (2019)
  • Delineating Seasonal Relationships Between Suomi NPP-VIIRS Nighttime Light
           and Human Activity Across Shanghai, China
    • Authors: Zuoqi Chen;Bailang Yu;Na Ta;Kaifang Shi;Chengshu Yang;Congxiao Wang;Xizhi Zhao;Shunqiang Deng;Jianping Wu;
      Pages: 4275 - 4283
      Abstract: The nighttime light (NTL) remote-sensing data have been widely applied in several applications for analyzing the urbanization process. The relationship between NTL intensity and human activity becomes a solid foundation for the applications using NTL data. However, there is no research, so far, revealing how the human activity seasonality could impact the seasonal change of NTL intensity. In this paper, a comparative analysis, box plot, and random forest algorithm were applied to NTL remote-sensing data and points of interest (POIs) data within Shanghai, China. The results show that in spring and autumn, the NTL is much brighter than that in summer and winter, especially within high human activity density area. The NTL intensity can be partly (approximately 40%) explained as the joint effects of the five POI categories. By analyzing the contributions of each POI category to NTL intensity, we found that the National Polar-Orbiting Partnership-Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) could be used to dig more information about gross domestic product (GDP) and traffic-based applications with consideration of NTL seasonality.
      PubDate: Nov. 2019
      Issue No: Vol. 12, No. 11 (2019)
  • Convective Initiation Nowcasting Over China From Fengyun-4A Measurements
           Based on TV-L1 Optical Flow and BP_Adaboost Neural Network Algorithms
    • Authors: Fenglin Sun;Danyu Qin;Min Min;Bo Li;Fu Wang;
      Pages: 4284 - 4296
      Abstract: Convective initiation (CI) nowcasting over China has a problem of a high false-alarm rate (FAR) due to the local convective processes, most of which do not produce severe weather. In order to focus on those CIs with severe weather, a new 0–2 h CI nowcasting system is developed in this article, named rapidly developed convection monitoring system (RDCMS), using the data from the advanced geosynchronous radiation imager onboard China Fengyun-4A(FY-4A) satellite. The RDCMS is not only used to identify CIs at the early stage, but also to prevent the relative high FAR. The key solutions of RDCMS are to introduce the total variation L1 norm (TV-L1) optical flow method for more tracking efficiency, and to use a supervised learning method named BP_Adaboost neural network for severe convection checking. Case studies show that the RDCMS’ skills have been improved as expected in four regions of China, which are Qinghai-Tibet Plateau, the East China, Northeast China, and South China. In the southern China region, the CI lead time is 17–40 min, and the best probability of detection is as high as 0.80, with the FAR lower than 0.34.
      PubDate: Nov. 2019
      Issue No: Vol. 12, No. 11 (2019)
  • Dynamic Waterline Mapping of Inland Great Lakes Using Time-Series SAR Data
           From GF-3 and S-1A Satellites: A Case Study of DJK Reservoir, China
    • Authors: Ning Li;Shilin Niu;Zhengwei Guo;Lin Wu;Jianhui Zhao;Lin Min;Daqing Ge;Jiaqi Chen;
      Pages: 4297 - 4314
      Abstract: Great inland freshwater lakes play an important role in regulating inland water resources, and the usage of synthetic aperture radar (SAR) images for the accurate waterline mapping is an effective technical means to study the dynamic changes of great inland lakes. In this article, the Danjiangkou (DJK) reservoir is selected as a study case, and a novel waterline mapping method with four main parts is proposed to monitor the water area dynamically. First, a coarse segmentation method is implemented to extract the initial waterline. Second, a strategy of division in local regions is given to speed up the subsequent processes. Third, a combination of a speckle filter and an improved geometric active contour model is used for refined segmentation. Finally, a change detection method is used to study the changing lake. Furthermore, six SAR images obtained by the Gaofen-3 (GF-3) and Sentinel-1A (S-1A) satellites in the DJK reservoir, Hongze lake and Poyang lake are tested to verify the universality of the proposed water area extraction method. The results demonstrate excellent performances with an accuracy of over 97% and an average contour offset under 0.7 pixels. Besides, the time-series analysis of the DJK reservoir is applied based on the mapped waterlines of 37 SAR images collected from January to December in 2017. Comparing with the changing tendency of the water level surveyed in the DJK reservoir, the waterline mapping results and the filed survey data have great consistency, which further proves the validity of the proposed method, also presents the significant potential of the GF-3 and S-1A SAR images for managing the water resource.
      PubDate: Nov. 2019
      Issue No: Vol. 12, No. 11 (2019)
  • Water Detection in SWOT HR Images Based on Multiple Markov Random Fields
    • Authors: Sylvain Lobry;Loïc Denis;Brent Williams;Roger Fjørtoft;Florence Tupin;
      Pages: 4315 - 4326
      Abstract: One of the main objectives of the surface water and ocean topography (SWOT) mission, scheduled for launch in 2021, is to measure inland water levels using synthetic aperture radar (SAR) interferometry. A key step toward this objective is to precisely detect water areas. In this article, we present a method to detect water in SWOT images. Water is detected based on the relative brightness of the water and nonwater surfaces. Water brightness varies throughout the swath because of system parameters (i.e., the antenna pattern), as well as the phenomenology such as wind speed and surface roughness. To handle the effects of brightness variability, we propose to model the problem with one Markov random field (MRF) on the binary classification map, and two other MRFs to regularize the estimation of the class parameters (i.e., the land and water background power images). Our experiments show that the proposed method is more robust to the expected variations in SWOT images than traditional approaches.
      PubDate: Nov. 2019
      Issue No: Vol. 12, No. 11 (2019)
  • A Multitemporal Remote Sensing Image Registration Method Based on Water
           Bodies for the Lake-Rich Region
    • Authors: Zhanfeng Shen;Junli Li;Yongwei Sheng;Timothy A. Warner;Lifang Zhao;
      Pages: 4327 - 4341
      Abstract: Accurate multitemporal remotely sensed image registration is essential for water body change detection and analysis of lake dynamics. However, lake-rich regions, such as Siberia, are mostly dominated by rivers and lakes, with few stable geometry features such as those typically used as control points for image registration. Lakes in Arctic regions are generally not static; their shorelines tend to expand and shrink seasonally, and may change substantially between different years, making it difficult to find consistent features for image registration. Consequently, traditional image-to-image registration methods, and even many sophisticated registration algorithms, rarely achieve accurate geometric correction in Arctic regions due to a lack of sufficient control points. In this article, we proposed a summary lake spatial attribute, the inferred deepest point of the lake (DPL), as a feature that is relatively insensitive to lake area changes, and therefore useful for registration of multitemporal images. The central focus of the DPL estimation algorithm is the identification of the largest inner circle (LIC) of the lake polygon. First, the internal Voronoi diagram of a lake polygon is calculated by the ``divide-and-conquer” method. The medial axis (MA) is then calculated by Voronoi diagram simplification, and finally, the LIC center is obtained by computing the distance from the MA intersection to all polygon edges. The approach was used to register HJ-1 and Landsat multispectral scanner (MSS) images in Siberia, where water bodies dominate the landscape and change significantly over time. The proposed method found a large number of control points from the extracted water bodies. Subpixel registration accuracy of 0.62 pixels (18.5 m) and 0.33 pixels (19.6 m) root mean square error (RMSE) was obtained for the HJ-1 and MSS images, respectively. In comparison, the alternative method of using lake centroids, only achieved -.75 pixels (22.4 m) and 0.49 pixels (29.3 m) RMSE. This registration accuracy improvement is potentially important for large-scale regional cartography and change detection applications.
      PubDate: Nov. 2019
      Issue No: Vol. 12, No. 11 (2019)
  • Precision and Bias Comparison Between Laser and Radar Altimetry Data in
           the Amundsen Sea Embayment and the Lambert-Amery System of Antarctica
    • Authors: Huan Xie;Wenjia Du;Gang Hai;Lei Chen;Hong Tang;Xiaohua Tong;
      Pages: 4342 - 4350
      Abstract: This article focuses on the precision and bias of laser altimetry data [Ice, Cloud, and Land Elevation Satellite (ICESat)] and radar altimetry data (Envisat) L2 products during the contemporary period from 2003 to 2008 in the Amundsen Sea Embayment (ASE) in West Antarctica (an ice loss region) and the Lambert-Amery System (LAS) in East Antarctica (an ice gain region). We used the crossover method to obtain the elevation differences between ICESat tracks, Envisat tracks, and between ICESat and Envisat tracks. The crossover points were generated and the difference of each crossover pair was calculated as raw data. The standard deviations were then computed from the raw data in a grid cell for both ICESat and Envisat. The precision of both satellites varied as a function of the surface slope in the abovementioned two regions, from 6.6 to 16.6 cm for the ICESat data and from 0.11 to 0.35 m for the Envisat data. The crossover points from ICESat-Envisat showed a mean bias of 0.55 ± 4.00 m for the ASE and 0.45 ± 0.99 m for the LAS, in accordance with the penetration depth of the radar altimetry. The relationship between the precision of the satellite measurements with the slope of the ice sheet and the ice velocity in the study area showed that the regions with gentle slopes and low velocity obtained a better precision of altimetry data.
      PubDate: Nov. 2019
      Issue No: Vol. 12, No. 11 (2019)
  • Monitoring of a Sea-Dike in Northern Germany by Means of ERS-1,
           ENVISAT/ASAR, and Sentinel-1 SAR Interferometry
    • Authors: Moritz Seidel;Philip Marzahn;Ralf Ludwig;
      Pages: 4351 - 4360
      Abstract: Flood events are one of the most severe natural hazards threatening billions of people living in coastal areas. Synthetic aperture radar (SAR) interferometry can contribute significantly to civil security by providing a highly accurate tool for spatially explicit monitoring of flood protection structures. This article analyzes multitemporal SAR interferometry techniques like persistent scatterer interferometry and small baseline subset (SBAS) for C-band radar data in order to show the potential for a precise dike monitoring. These techniques not only achieve accuracies of few millimeters, but they also enable to reconstruct the deformation process in time. Thus, critical deformation processes can be identified timely and precautions can be taken to prevent dike bursts during flood events. In this article, sea dikes located at the North Sea coast in Germany are analyzed in order to show the potential but also the difficulties of this approach, using 34 ERS-1/2, 55 Envisat ASAR, and 44 Sentinel-1 scenes. All results point out that long-term deformation processes can be measured quite well. Due to decorrelation effects, the results on vegetated dikes are ambiguous. Applying SBAS to Sentinel-1 data is very promising to serve as a tool to precisely detect and understand deformation processes of dike bodies on large spatial scales.
      PubDate: Nov. 2019
      Issue No: Vol. 12, No. 11 (2019)
  • Sea Surface Salinity Products Validation Based on Triple Match Method
    • Authors: Jin Wang;Weifu Sun;Jie Zhang;
      Pages: 4361 - 4366
      Abstract: Since satellites have observed the sea surface temperature (SSS) from space for years, the scientific community has devoted many efforts to the validation of satellite SSS products. Typically, this validation procedure is based on the “double match” method between the in situ and remote-sensed measurements. However, this direct comparison has its limitations because it does not take into account sampling error of different SSS sources. Actually, the in situ method presents the pointwise measurements and the satellite data are the spatial average within its footprint, so the in situ data contain the true small-scale SSS signal which cannot be resolved by satellite data. Researchers introduce the representativeness error to describe the small-scale signal. However, the estimation of representativeness error remains challenging. In this study, based on the constancy of salinity variance, we develop a new method to estimate the representativeness error and apply it to the triple collocation dataset of Argo data and L3 SSS product of soil moisture active/passive (SMAP) and soil moisture and ocean salinity (SMOS). The representativeness error is estimated to be 0.093 psu2 in global oceans. The random error of Argo data is better than 0.21 psu which is superior to SMAP and SMOS. Considering the different sampling resolution of SMAP and SMOS, the quality of SMAP SSS product (0.33 psu) is slightly better than SMOS (0.41 psu).
      PubDate: Nov. 2019
      Issue No: Vol. 12, No. 11 (2019)
  • Effect of Bispectrum on Radar Backscattering From Non-Gaussian Sea Surface
    • Authors: Dengfeng Xie;Kun-Shan Chen;Xiaofeng Yang;
      Pages: 4367 - 4378
      Abstract: The upwind–downwind asymmetry in normalized radar backscattering cross section (NRBCS) from ocean surface is well known; one acceptable and convincing reasoning is explained by the fact that the surface height distribution deviates from Gaussian one, which causes a nonzero skewness function, and consequently affects the radar cross section in up and down wind directions. Specific forms of skewness function, in between Gaussian and exponential, have been proposed in previous studies to account for the upwind/downwind asymmetry in radar backscatter. Attempt is made, through numerical simulation, in this article to examine the impact of these two types of skewness functions on NRBCS. The simulated NBRCSs, with and without skewness contributions, are compared with measured data in upwind and downwind directions at L-, C-, and Ku-bands for different wind speeds. The results indicate that the Gaussian-type skewness function works better to account for the upwind/downwind asymmetry of NBRCS by choosing an appropriate root mean square (RMS) height appearing in skewness function and as the wind speed or radar frequency increases, the RMS height increases but regardless of wind direction and radar incidence angle.
      PubDate: Nov. 2019
      Issue No: Vol. 12, No. 11 (2019)
  • Ship Velocity Estimation From Ship Wakes Detected Using Convolutional
           Neural Networks
    • Authors: Ki-mook Kang;Duk-jin Kim;
      Pages: 4379 - 4388
      Abstract: Accurately tracking marine traffic considering security and commercial activities is still challenging despite its increasing global importance. Recently, space-borne synthetic aperture radar (SAR) is being considered to accurately monitor maritime traffic, and techniques to detect the position of ships and estimate their velocity have become essential. Here, we investigated the potential for automatic estimation of ship velocity using the azimuth offset between ships and wakes detected using convolutional neural network (CNN) coupled with SAR imagery. We found that azimuth offset is proportional to the Doppler shift effect of the back-scattered signal in SAR, thus, it relates to the radial velocity of a moving target. Consequently, we propose a method whereby a CNN is applied to automatically detect ship wakes from TanDEM-X data. In this method, ship velocity is calculated using the azimuthal distance (i.e., azimuth offset) between the stern of the detected ship and the vertex of the detected V-shape wake—determined as the intersection of two lines obtained through edge filtering and Radon transforms. The location and number of detected ships are then compared with an automatic identification system (AIS), and the calculated velocity of the ship is compared with the velocity obtained via along-track interferometry and AIS. Results show that our method automatically detects ships and wakes with accuracies of 91.0% and 93.2%, respectively, and estimates the velocity of ships with an accuracy of 0.13 m/s. This method is effective when wind velocities are not substantially higher than 5.5 m/s and ship velocities are not extremely low.
      PubDate: Nov. 2019
      Issue No: Vol. 12, No. 11 (2019)
  • Modeling the Vertical Backscattering Distribution in the Percolation Zone
           of the Greenland Ice Sheet With SAR Tomography
    • Authors: Georg Fischer;Marc Jäger;Konstantinos P. Papathanassiou;Irena Hajnsek;
      Pages: 4389 - 4405
      Abstract: The penetration of microwave signals into snow and ice, especially in dry conditions, introduces a bias in digital elevation models generated by means of synthetic aperture radar (SAR) interferometry. This bias depends directly on the vertical backscattering distribution in the subsurface. At the same time, the sensitivity of interferometric SAR measurements on the vertical backscattering distribution provides the potential to derive information about the subsurface of glaciers and ice sheets from SAR data, which could support the assessment of their dynamics. The aim of this article is to improve the interferometric modeling of the vertical backscattering distribution in order to support subsurface structure retrieval and penetration bias estimation. Vertical backscattering distributions are investigated at different frequencies and polarizations on two test sites in the percolation zone of Greenland using fully polarimetric X-, C-, L-, and P-band SAR data. The vertical backscattering distributions were reconstructed by means of SAR tomography and compared to different vertical structure models. The tomographic assessment indicated that the subsurface in the upper percolation zone is dominated by scattering layers at specific depths, whereas a more homogeneous scattering structure appears in the lower percolation zone. The performance of the evaluated structure models, namely an exponential function with a vertical shift, a Gaussian function, and a Weibull function, was evaluated. The proposed models improve the representation of the data compared with existing models while the complexity is still low to enable potential model inversion approaches. The tomographic analysis and the model assessment is therefore a step forward toward subsurface structure information and penetration bias estimation from SAR data.
      PubDate: Nov. 2019
      Issue No: Vol. 12, No. 11 (2019)
  • Polarimetric SAR Signatures for Characterizing Geological Units in the
           Canadian Arctic
    • Authors: Byung-Hun Choe;Gordon R. Osinski;Catherine D. Neish;Livio L. Tornabene;
      Pages: 4406 - 4414
      Abstract: This study investigates the polarimetric radar signatures of geological units in the Canadian Arctic to characterize their physical surface properties. It focuses on the Tunnunik and Haughton meteorite impact structures using RADARSAT-2 quad polarimetric synthetic aperture radar (SAR) data. The geological units show different three-dimensional (3-D) polarimetric SAR signature plots (i.e., radar backscattering responses according to the orientation and the ellipticity of a polarization state). We analyzed the quantitative relationship between the polarimetric SAR signatures of the geological units and their surface roughness. The pedestal height and the standard deviation of linear copolarization responses (SDLP) were calculated from the 3-D copolarization power signature plot, and then compared to in situ surface roughness measurements derived from LiDAR scanned high-resolution surface topography. The results show that the pedestal height has a positive correlation coefficient of ∼0.6 with surface roughness, while the SDLP has a negative correlation coefficient of ∼0.8 with surface roughness. The variation between the different polarization responses is highly dependent on the surface roughness of the geological units. The SDLP is thus suggested as a promising parameter to characterize surface roughness, in addition to the pedestal height that has been commonly used.
      PubDate: Nov. 2019
      Issue No: Vol. 12, No. 11 (2019)
  • Characterizing Tree Species of a Tropical Wetland in Southern China at the
           Individual Tree Level Based on Convolutional Neural Network
    • Authors: Ying Sun;Qinchuan Xin;Jianfeng Huang;Bo Huang;Hongsheng Zhang;
      Pages: 4415 - 4425
      Abstract: Classification of species at the individual tree level would be beneficial to many applications including forest landscape visualization, forest management, and biodiversity monitoring. This article develops a patch-based classification algorithm of individual tree species based on convolutional neural network. The individual trees are first detected using the local maximum method from the canopy height model, as derived from light detection and ranging (LiDAR) data. The detected individual trees are then cropped into patches for classification based on the tree apexes, and three spatial scale image patches are chosen for analysis and discussion. A modified ResNet50 deep network is further employed for the cropped individual tree patches classification. The patch-based method accounts for the contexture information of a tree and does not require the feature selection or the feature reduction processes. About 1388 training samples including Ficus microcarpa Linn. f., Delonix regia, Chorisia speciosa A.St.-Hil., Dimocarpus longan Lour., Musa nana Lour., Carica papaya, and Others (the other tree species except the above six) were collected from both field work and visual interpretation. Aerial images, LiDAR data, and Worldview images were used for the tree species classification. For 362 test tree samples, the results of patch size 64 achieve the best accuracies, and the proposed method outperforms the traditional machine learning method with the overall accuracy of 89.06% + 0.58% using aerial images only. Transferability Study to the Luhu Park also indicated the feasibility of our method. While challenges in individual tree detection and multisource data fusion remain, the solution shows the potential in characterizing tree species at the individual tree level using remote sensing data.
      PubDate: Nov. 2019
      Issue No: Vol. 12, No. 11 (2019)
  • Downscaling Gridded DEMs Using the Hopfield Neural Network
    • Authors: Quang Minh Nguyen;Thi Thu Huong Nguyen;Phu Hien La;Hugh G. Lewis;Peter M. Atkinson;
      Pages: 4426 - 4437
      Abstract: A new Hopfield neural network (HNN) model for downscaling a digital elevation model in grid form (gridded DEM) is proposed. The HNN downscaling model works by minimizing the local semivariance as a goal, and by matching the original coarse spatial resolution elevation value as a constraint. The HNN model is defined such that each pixel of the original coarse DEM is divided into f × f subpixels, represented as network neurons. The elevation of each subpixel is then derived iteratively (i.e., optimized) based on minimizing the local semivariance under the coarse elevation constraint. The proposed HNN model was tested against three commonly applied alternative benchmark methods (bilinear resampling, bicubic and Kriging resampling methods) via an experiment using both degraded and sampled datasets at 20-, 60-, and 90-m spatial resolutions. For this task, a simple linear activation function was used in the HNN model. Evaluation of the proposed model was accomplished comprehensively with visual and quantitative assessments against the benchmarks. Visual assessment was based on direct comparison of the same topographic features in different downscaled images, scatterplots, and DEM profiles. Quantitative assessment was based on commonly used parameters for DEM accuracy assessment such as the root mean square error, linear regression parameters m and b, and the correlation coefficient R. Both visual and quantitative assessments revealed the much greater accuracy of the HNN model for increasing the grid density of gridded DEMs.
      PubDate: Nov. 2019
      Issue No: Vol. 12, No. 11 (2019)
  • Multifractality in Humanitarian Applications: A Case Study of Internally
           Displaced Persons/Refugee Camps
    • Authors: Małgorzata Jenerowicz;Anna Wawrzaszek;Wojciech Drzewiecki;Michał Krupiński;Sebastian Aleksandrowicz;
      Pages: 4438 - 4445
      Abstract: The coordination of humanitarian relief is always difficult due to a lack of data required for management and planning. Remote sensing imagery can be an important source of information about the in-situ situation, notably, no-access areas. Scenarios include situation awareness after natural disasters or military conflicts, damage assessment, or monitoring camp structure, either as a one-off exercise or on an ongoing basis. In this article, we propose a multifractal approach to automating information extraction about internally displaced persons/refugee camps and discuss its potential and limitations. Our case study uses multifractal features to determine the extent of camps in Kenya and Sudan. The results show that the method can be usefully applied to camp growing analysis and help to make a rough, but rapid estimation of their extent. Our multifractal method appears to be a reasonable step forward on the road between manual mapping and (not yet developed) fully automated, highly accurate processing.
      PubDate: Nov. 2019
      Issue No: Vol. 12, No. 11 (2019)
  • Triplanar Imaging of 3-D GPR Data for Deep-Learning-Based Underground
           Object Detection
    • Authors: Namgyu Kim;Sehoon Kim;Yun-Kyu An;Jong-Jae Lee;
      Pages: 4446 - 4456
      Abstract: This article proposes a deep-learning-based underground object classification technique that uses triplanar ground-penetrating radar images consisting of B-, C-, and D-scan images. Although multichannel ground-penetrating radar (GPR) systems provide three-dimensional (3-D) information about underground objects, there is currently no suitable technique available for processing 3-D data as opposed to 2-D images. In this article, a triplanar deep convolutional neural network technique is proposed for use in processing 3-D GPR data for use in automatized underground object classification. The proposed method was validated experimentally using 3-D GPR road scanning data obtained from urban roads in Seoul, South Korea. In addition, the classification performance of the method was compared to that of a conventional method that uses only B-scan-images. The results of the validation and comparison tests reveal that the classification performance of the proposed technique is notably better than that of the conventional B-scan-image-based method and that its use results in decrease misclassification ratios.
      PubDate: Nov. 2019
      Issue No: Vol. 12, No. 11 (2019)
  • A Novel Stitching Method for Dust and Rock Analysis Based on Yutu Rover
           Panoramic Imagery
    • Authors: Ben Ye;Zhanchuan Cai;Ting Lan;Wei Cao;
      Pages: 4457 - 4466
      Abstract: The distributions of lunar dust and rock can reflect a large amount of physical and geological information, which has contributed to analyze the environment of the Moon. China's Yutu rover collected large numbers of high-definition images of the lunar surface. In order to obtain a wide field of view and study the lunar environment at close quarters, this article proposes a novel image stitching method named as-least-loss-as-possible wrap to provide a reasonable wider overview of the studying area based on the Yutu rover. In the proposed scheme, a double-feature matching approach is designed to solve the matching error which is generated by the specific environment of the lunar surface. From the stitched image, it can be found that the proposed method can achieve well stitched results for scientific studies, the distribution of the lunar dust on the rock's surface associates with the shadow (temperature), roughness, and height of the rock, and a special ribbon-type distribution of the lunar rocks is demonstrated clearly in a wide stitched image. This ribbon band is caused by the density inhomogeneity of impact target. And the distribution of lunar dust on the rock has the same pattern, even on the whole Moon.
      PubDate: Nov. 2019
      Issue No: Vol. 12, No. 11 (2019)
  • Polarimetric SAR Image Terrain Classification
    • Authors: R. Derek West;Thomas E. LaBruyere III;Jacek Skryzalin;Katherine M. Simonson;Ross L. Hansen;Mark H. Van Benthem;
      Pages: 4467 - 4485
      Abstract: In practical applications of automated terrain classification from high-resolution polarimetric synthetic aperture radar (PolSAR) imagery, different terrain types may inherently contain a high level of internal variability, as when a broadly defined class (e.g., “trees”) contains elements arising from multiple subclasses (pine, oak, and willow). In addition, real-world factors such as the time of year of a collection, the moisture content of the scene, the imaging geometry, and the radar system parameters can all increase the variability observed within each class. Such variability challenges the ability of classifiers to maintain a high level of sensitivity in recognizing diverse elements that are within-class, without sacrificing their selectivity in rejecting out-of-class elements. In an effort to gauge the degree to which classifiers respond robustly in the presence of intraclass variability and generalize to untrained scenes and conditions, we compare the performance of a suite of classifiers across six broad terrain categories from a large set of polarimetric synthetic aperture radar (PolSAR) image sets. The main contributions of this article are as follows: 1) an analysis of the robustness of a variety of current state-of-the art classification algorithms to intraclass variability found in PolSAR image sets, and 2) the associated PolSAR image and feature data that Sandia is releasing to the research community with this publication. The analysis of the classification algorithms we provide will serve as a benchmark of performance for the future PolSAR terrain classification algorithm research and development enabled by the image sets and data provided. By sharing our analysis and high-resolution fully polarimetric Sandia data with the research community, we enable others to develop and assess a new generation of robust terrain classification algorithms for PolSAR.
      PubDate: Nov. 2019
      Issue No: Vol. 12, No. 11 (2019)
  • Ground Maneuvering Targets Imaging for Synthetic Aperture Radar Based on
           Second-Order Keystone Transform and High-Order Motion Parameter Estimation
    • Authors: Cao Zeng;Dong Li;Xi Luo;Dawei Song;Hongqing Liu;Jia Su;
      Pages: 4486 - 4501
      Abstract: With the developments of the synthetic aperture radar (SAR) system, the long dwell time provides high resolution but at the same time it produces large range migration (RM) and Doppler frequency migration, which brings a new challenge for ground moving target imaging. To address those problems, a ground moving target refocusing method based on a novel parameter estimation algorithm, namely second-order Keystone transform (SOKT)-Radon-cubic phase function-Fourier transform (RCFT), is proposed in this work. First, the SOKT is applied to correct the range curvature. After that, the Hough transform is utilized to estimate the trajectory slope for the range walk correction and the across-track velocity acquirement. Finally, a novel coherently integrated parameter estimation method, termed as RCFT, is developed in this work to transform the azimuth quadratic frequency-modulated signal into a peak point in two-dimensional (2-D) time-frequency plane. In the process, the second- and third-order motion parameters are simultaneously obtained, which eliminates the error propagation effect. Due to 2-D coherent integration realization and its bilinear function feature, the proposed RCFT method is capable of obtaining accurate parameter estimates in low signal-to-noise ratio scenario with a relatively low computational burden. Compared with the conventional SAR imaging methods using the second-order phase model, the proposed method produces better imaging quality since the third-order Doppler frequency migration is effectively eliminated. The performance of the proposed method is demonstrated by both the simulated and real data.
      PubDate: Nov. 2019
      Issue No: Vol. 12, No. 11 (2019)
  • Roll-Invariant Target Parameter Extraction From POLSAR Data
    • Authors: Jiehong Chen;Hong Zhang;Chao Wang;Junsong Jia;
      Pages: 4502 - 4516
      Abstract: In this article, we develop two target parameters to model the interaction between a target and a polarization wave. The two parameters are the target ellipticity angle (TEA) and the target phase angle (TPA). The concept of reflection symmetry or rotation symmetry is used to explain the robustness of the estimation method and to show problems associated with other algorithms. C-band Radarsat-2 and L band ALOS PALSAR multi-look full polarimetric images are used to substantiate the parameters, and validate the effectiveness of them. In addition, the TEA and TPA are compared with $alpha - beta $ model and Touzi TVSM model for single-look polarimetric SAR data. Experimental results show that the two target parameters are beneficial supplements for interpreting the target scattering processes and separating buildings from other targets.
      PubDate: Nov. 2019
      Issue No: Vol. 12, No. 11 (2019)
  • Change Detection From Synthetic Aperture Radar Images Based on Channel
           Weighting-Based Deep Cascade Network
    • Authors: Yunhao Gao;Feng Gao;Junyu Dong;Shengke Wang;
      Pages: 4517 - 4529
      Abstract: Deep learning methods have recently demonstrated their significant capability for synthetic aperture radar (SAR) image change detection. However, with the increase of network depth, convolutional neural networks often encounter some negative effects, such as overfitting and exploding gradients. In addition, the existing deep networks employed in SAR change detection tend to produce a lot of redundant features that affect the performance of the network. To solve the aforementioned problems, this article proposed a deep cascade network (DCNet) for SAR image change detection. On the one hand, a very DCNet is established to exploit discriminative features, and residual learning is introduced to solve the exploding gradients problem. In addition, a fusion mechanism is employed to combine the outputs of different hierarchical layers to further alleviate the exploding gradient problem. Moreover, a simple yet effective channel weighting-based module is designed for SAR change detection. Average pooling and max pooling are used to aggregate channel-wise information. Meaningful channel-wise features are emphasized and unnecessary ones are suppressed. Therefore, the similarity in feature maps can be reduced, and then, the classification performance of the DCNet is improved. Experimental results on four real SAR datasets demonstrated that the proposed DCNet can obtain better change detection performance than several competitive methods. Our codes are available at https://github.com/summitgao/SAR_CD_DCNet.
      PubDate: Nov. 2019
      Issue No: Vol. 12, No. 11 (2019)
  • Moving-Target Detection in SAR Images Using Difference Between Two Looks
    • Authors: Junfeng Wang;
      Pages: 4530 - 4542
      Abstract: Moving targets can be detected in synthetic aperture radar (SAR) images using the difference between two looks because in the two looks, the images of a stationary target are similar, but the images of a moving target are different. In this article, an elaborate scheme is presented to bring this idea into effect. First, the complex image is compensated to eliminate the shifting and the blurring of the stationary ground due to the velocity error of the radar, and this will decrease the loss of the similarity between the two looks of the stationary ground. Then, the Doppler spectrum is halved, each half is used to generate a look, and the two looks are despeckled to reduce the loss of their similarity. Finally, the difference between the two looks is characterized, smoothed, and thresholded to indicate moving targets. This scheme is effective for targets moving in range, azimuth, or both. It is also easy to implement and efficient in computation.
      PubDate: Nov. 2019
      Issue No: Vol. 12, No. 11 (2019)
  • A Ground Surface Deformation Monitoring InSAR Method Using Improved
           Distributed Scatterers Phase Estimation
    • Authors: Changjun Zhao;Zhen Li;Bangsen Tian;Ping Zhang;Quan Chen;
      Pages: 4543 - 4553
      Abstract: Persistent scatterer interferometric synthetic aperture radar (PSInSAR) technology provides a powerful tool for detecting ground surface displacements. However, one of its major limitations is an insufficient number of coherence points due to decorrelation. In this article, we propose an effective ground surface displacement monitoring approach based on the Stanford method for persistent scatterer (StaMPS). This approach dramatically increases the density of coherence points by jointly exploiting persistent scatterer (PS) and distributed scatterer (DS) points. In particular, we develop a new DS phase estimation method based on nonlinear optimization estimation (NLE) and exploit it in our approach. The NLE can obtain better DS phase noise reduction, can detect more coherence points, and requires slightly less computational time than conventional maximum-likelihood estimation (MLE). To demonstrate the effectiveness of our new approach, we apply it over 24 Envisat SAR images acquired over Tucson, Arizona in the USA. The coherence pixel density is increased substantially (> 6 times) compared with the conventional StaMPS-based PSInSAR approach. As for the improvement of our NLE over MLE, it can detect 11.7% more coherence pixels, can achieve better phase noise reduction, and needs approximately one third less computational time. These results validate the effectiveness of our new approach and suggest its great potential for land displacement measurements.
      PubDate: Nov. 2019
      Issue No: Vol. 12, No. 11 (2019)
  • Surface Normal Vector Estimation From Passive Millimeter-Wave Polarimetric
    • Authors: Yan Hu;Fei Hu;Yayun Cheng;Yanyu Xu;Jinlong Su;
      Pages: 4554 - 4562
      Abstract: Surface normal vector is essential for object recognition and three-dimensional reconstruction. This article establishes a passive millimeter-wave polarization observation model of object thermal radiation. Then, a polarization-based method is proposed to acquire the surface normal vector from multipolarization measurements. The simulation results show that our method has high accuracy and robustness. The outdoor imaging experiment was conducted, and the polarization characteristics of several typical objects in the imaging were analyzed. Experimental results show that our method can successfully estimate the surface normal vector. The possible applications of our method include object recognition and pavement inspection.
      PubDate: Nov. 2019
      Issue No: Vol. 12, No. 11 (2019)
  • Instrument Design and Performance of the High-Frequency Airborne Microwave
           and Millimeter-Wave Radiometer
    • Authors: Xavier Bosch-Lluis;Steven C. Reising;Pekka Kangaslahti;Alan B. Tanner;Shannon T. Brown;Sharmila Padmanabhan;Chaitali Parashare;Oliver Montes;Behzad Razavi;Victoria D. Hadel;Thaddeus P. Johnson;Mehmet Ogut;James Ranson;
      Pages: 4563 - 4577
      Abstract: The high-frequency airborne microwave and millimeter-wave radiometer (HAMMR) is a cross-track scanning airborne radiometer instrument with 25 channels from 18.7 to 183.3 GHz. HAMMR includes: low-frequency microwave channels at 18.7, 23.8, and 34.0 GHz at two linear-orthogonal polarizations; high-frequency millimeter-wave channels at 90, 130 and 168 GHz; and millimeter-wave sounding channels consisting of eight channels near the 118.75 GHz oxygen absorption line for temperature profiling and eight additional channels near the 183.31 GHz water vapor absorption line for water vapor profiling. HAMMR was deployed on a twin otter aircraft for a west coast flight campaign (WCFC) from November 4–17, 2014. During the WCFC, HAMMR collected radiometric observations for more than 53.5 h under diverse atmospheric conditions, including clear sky, scattered and dense clouds, as well as over a variety of surface types, including coastal ocean areas, inland water and land. These measurements provide a comprehensive dataset to validate the instrument.
      PubDate: Nov. 2019
      Issue No: Vol. 12, No. 11 (2019)
  • Improving Brightness Temperature Measurements Near Coastal Areas for SMAP
    • Authors: Julian Chaubell;Simon H. Yueh;Jinzheng Peng;R. Scott Dunbar;Steven K. Chan;Fan Chen;Jeffrey R. Piepmeier;Rajat Bindlish;Dara Entekhabi;Peggy E. O'Neill;
      Pages: 4578 - 4588
      Abstract: The Soil Moisture Active Passive (SMAP) mission is designed to acquire L-band radiometer measurements for the estimation of soil moisture with 0.04 m3/m3 volumetric accuracy in the top 5 cm for vegetation with water content of less than 5 kg/m2. In regions near the coast or near inland bodies of water, the signal measured by the SMAP radiometer contains emissions from land and water, resulting in errors in the soil moisture estimation. In this article, the effort to extract the brightness temperature (TB) according to the land fraction or water fraction (depending on the center of the footprint location) from the affected SMAP measurements was addressed. A single pixel correction algorithm was applied and its performance was evaluated over simulated data. A data-driven approach for the estimation of land and water TB for data correction was developed. The correction algorithm was then applied to real data and its performance was assessed over the SMAP soil moisture retrievals. We showed that the single pixel algorithm is an effective and computationally efficient algorithm for removing land or water TB contamination from the SMAP data.
      PubDate: Nov. 2019
      Issue No: Vol. 12, No. 11 (2019)
  • Multicomponent Driven Consistency Priors for Simultaneous Decomposition
           and Pansharpening
    • Authors: Pengfei Liu;Liang Xiao;
      Pages: 4589 - 4605
      Abstract: Pansharpening is also known as the fusion of a low-resolution (LR) multispectral (MS) image and a high-resolution (HR) panchromatic (Pan) image of the same scene, which is an effective way to improve the spatial resolution of the LR MS image so as to obtain an HR MS image (i.e., pansharpened MS image). In this article, we propose a novel multicomponent consistency priors driven variational model for simultaneous decomposition and pansharpening (SDP) in a unified optimization framework. Specifically, the proposed SDP model particularly decomposes the Pan and MS images into cartoon, structure and texture components, and fully exploits the multicomponent consistency priors on these cartoon, structure and texture components of the Pan and MS images. Thus, the proposed SDP model can suitably characterize the different properties of these multicomponents so that these multicomponents can be well preserved. Moreover, the proposed SDP model is actually a band-coupled model, which can fully preserve the intrinsic structural correlation among the MS bands, because the MS image is actually a spatial-spectral strongly correlated cube. Then, an efficient iterative algorithm based on the forward-backward splitting technique is designed to solve the proposed SDP model. Finally, we compare the proposed SDP method with some state-of-the-art methods on various satellite datasets, and the experimental results demonstrate the effectiveness of the proposed method, which can perform higher spectral and spatial qualities than the other methods.
      PubDate: Nov. 2019
      Issue No: Vol. 12, No. 11 (2019)
  • Constrained Distance-Based Clustering for Satellite Image Time-Series
    • Authors: Thomas Lampert;Baptiste Lafabregue;Thi-Bich-Hanh Dao;Nicolas Serrette;Christel Vrain;Pierre Gançarski;
      Pages: 4606 - 4621
      Abstract: The advent of high-resolution instruments for time-series sampling poses added complexity for the formal definition of thematic classes in the remote sensing domain—required by supervised methods—while unsupervised methods ignore expert knowledge and intuition. Constrained clustering is becoming an increasingly popular approach in data mining because it offers a solution to these problems; however, its application in remote sensing is relatively unknown. This article addresses this divide by adapting publicly available constrained clustering implementations to use the dynamic time warping (DTW) dissimilarity measure, which is sometimes used for time-series analysis. A comparative study is presented, in which their performance is evaluated (using both DTW and Euclidean distances). It is found that adding constraints to the clustering problem results in an increase in accuracy when compared to unconstrained clustering. The output of such algorithms are homogeneous in spatially defined regions. Declarative approaches and $k$-means-based algorithms are simple to apply, requiring little or no choice of parameter values. Spectral methods, however, require careful tuning, which is unrealistic in a semisupervised setting, although they offer the highest accuracy. These conclusions were drawn from two applications: crop clustering using 11 multispectral Landsat images nonuniformly sampled over a period of eight months in 2007; and tree-cut detection using 10 NDVI Sentinel-2 images nonuniformly sampled between 2016 and 2018.
      PubDate: Nov. 2019
      Issue No: Vol. 12, No. 11 (2019)
  • On-Orbit Measurement of the Effective Focal Length and Band-to-Band
           Registration of Satellite-Borne Whiskbroom Imaging Sensors
    • Authors: James C. Tilton;Robert E. Wolfe;Guoqing Lin;John J. Dellomo;
      Pages: 4622 - 4633
      Abstract: We have developed an approach for the measurement of the effective focal length (EFL) and band-to-band registration (BBR) of selected spectral bands of satellite-borne whiskbroom imaging sensors from on-orbit data. Our approach is based on simulating the coarser spatial resolution whiskbroom sensor data with finer spatial resolution Landsat 7 Enhanced Thematic Mapper Plus or Landsat 8 Operational Land Imager data using the geolocation (Earth location) information from each sensor and computing the correlation between the simulated and original data. For each scan of a selected spectral band of the whiskbroom dataset, various subsets of the data are examined to find the subset with the highest spatial correlation between the original and simulated data using the nominal geolocation information. Then, for this best subset, the focal length value and the spatial shift are varied to find the values that produce the highest spatial correlation between the original and simulated data. This best focal length value is taken to be the measured instrument EFL, and the best spatial shift is taken to be the registration of the whiskbroom data relative to the Landsat data, from which the BBR is inferred. Best results are obtained with cloud-free subsets with contrasting land features. This measurement is repeated over other scans with cloud-free subsets. We demonstrate our approach with on-orbit data from the Aqua and Terra MODIS instruments and SNPP and J1 VIIRS instruments.
      PubDate: Nov. 2019
      Issue No: Vol. 12, No. 11 (2019)
  • Drone Image Stitching Based on Compactly Supported Radial Basis Function
    • Authors: Jun Chen;Qi Wan;Linbo Luo;Yong Wang;Dapeng Luo;
      Pages: 4634 - 4643
      Abstract: Image stitching stitches multiple overlapping images into a seamless image according to the corresponding geometric relationship between the reference and source images. In this article, a parallax-tolerant image stitching method based on robust elastic warping is integrated in the stitching of drone images, and locality preserving feature matching is used to effectively remove outliers from a set of putative feature correspondences. Our method can avoid the ghost issue in traditional methods when the scene suffers large view point changes. The method can be divided into three stages, namely, locality preserving feature matching, robust elastic warping, and global projectivity preservation. First, a set of high-precision matched feature points are provided for a pair of drone images, where the locality preserving matching is used. Second, the robust elastic warping function eliminates the parallax error, and the input image is distorted according to the calculated deformation on the nongrid plane. Finally, the global projectivity preserving method is applied to obtain high-precision resultant panoramas. Experiments on several sets of drone images demonstrate that our method can generate better panoramas over the state-of-the-art competitors.
      PubDate: Nov. 2019
      Issue No: Vol. 12, No. 11 (2019)
  • Jointly Learning of Visual and Auditory: A New Approach for RS Image and
           Audio Cross-Modal Retrieval
    • Authors: Mao Guo;Chenghu Zhou;Jiahang Liu;
      Pages: 4644 - 4654
      Abstract: Remote sensing (RS) images are widely used in civilian and military fields. With the highly increasing image data, it has become a challenging issue to achieve fast and efficient RS image retrieval. However, the existing image retrieval methods, text-based or content-based, are still limited in the applications; for example, text input is inefficient, and the sample image for query is often unavailable. It is known that speech is a natural and convenient way of communication. Therefore, a novel speech-image cross-modal retrieval approach, named deep visual-audio network (DVAN), is presented in this article, which can establish the direct relationship between image and speech from paired image-audio data. The model mainly has three parts: 1) Image feature extraction, which is used to extract effective features of RS images; 2) audio feature learning, which is used to recognizing key information from raw data, and AudioNet, as part of DVAN, is proposed to obtain more distinguishing features; 3) multimodal embedding, which is used to learn the direct correlations of two modalities. Experimental results on RS image audio dataset demonstrate that the proposed method is effective and speech-image retrieval is feasible, and it provides a new way for faster and more convenient RS image retrieval.
      PubDate: Nov. 2019
      Issue No: Vol. 12, No. 11 (2019)
  • Nonlinear Elastic Impedance Inversion in Laplace–Fourier Domain
    • Authors: Guangsen Cheng;Xingyao Yin;Zhaoyun Zong;Jie Liu;
      Pages: 4655 - 4663
      Abstract: The elastic impedance (EI) inversion plays an important part in prestack seismic inversion. Compared with the linear EI equation, a novel nonlinear EI equation is derived, and the equation is more accurate and suitable for high-contrast situations. The conventional EI inversion approach faces the enormous challenges of estimating elastic parameters correctly and effectively when there is no well-log data or prior geophysical information to establish the EI initial models. In view of this problem, considering the phenomenon of the low-frequency amplitude and the proportion of low-frequency energy increase with the damping factor in the Laplace–Fourier domain, the low-frequency components can be recovered by the proposed EI inversion approach with Bayesian inference in the Laplace–Fourier domain and as the initial models further used in the conventional time-domain EI inversion. P- and S-wave impedances are extracted by combining artificial neural network inversion tool and the novel EI equation. The sensitivity analysis shows that the novel nonlinear EI equation is more sensitive to impedances rather than density. The field data examples demonstrate that our approach is feasible.
      PubDate: Nov. 2019
      Issue No: Vol. 12, No. 11 (2019)
  • Microseismic Event Estimation Based on an Efficient Wavefield Inversion
    • Authors: Chao Song;Tariq Alkhalifah;
      Pages: 4664 - 4671
      Abstract: Full-waveform inversion (FWI) has shown its potentials in active seismic scenario in extracting a high-resolution velocity model of the subsurface. In the microseismic scenario, the source function is unknown, and FWI can be applied to optimize the source image and the velocity model, simultaneously. However, FWI is a highly nonlinear optimization problem, and it causes a bigger challenge when the source location and origin time are unknown. To mitigate these issues, we propose a two-stage scheme to invert for the source function and the velocity using an efficient wavefield inversion (EWI). We use outer-loop iterations to repeat the process until we achieve convergence. We specifically formulate an optimization problem to linearly reconstruct the wavefield that tries to fit both the data, as well as the wave equation corresponding to the background model. In the first stage, the reconstructed wavefield is used to calculate a source function using the background wave equation modeling operator without any inversion or update process. In the second stage, we use the computed source function to represent the true source to update the velocity model in the same way we use EWI in the active seismic case. Applications on data generated from a modified Marmousi model with a single microseismic event and multiple simultaneous events demonstrate the ability of the proposed method. An application to field dataset also demonstrates that the proposed method is effective in locating the microseismic event and inverting for a reasonably good velocity model, sequentially.
      PubDate: Nov. 2019
      Issue No: Vol. 12, No. 11 (2019)
  • Spectral Decomposition of Seismic Data With Variational Mode
           Decomposition-Based Wigner–Ville Distribution
    • Authors: Xing-Jian Wang;Ya-Juan Xue;Wen Zhou;Jun-Song Luo;
      Pages: 4672 - 4683
      Abstract: Wigner–Ville distributions (WVDs), which provide superior time-frequency resolution and energy distribution concentration relative to other traditional Fourier-based or wavelet-based seismic time-frequency methods, are an important tool for spectral decomposition and have the potential to yield better seismic interpretations for highlighting geophysical responses in particular frequency bands. However, the existence of cross-term interference in WVDs limits their application. To effectively suppress the cross-term interference in a WVD without reducing the time-frequency resolution and energy aggregation, we propose a variational mode decomposition (VMD) based WVD approach. VMD is first used to decompose the multicomponent seismic data into a series of narrow band limited intrinsic mode functions (IMFs). Next, we calculate the WVDs of these IMFs. Finally, the maximum amplitude volume above the average amplitude and the peak frequency volume are extracted from these WVDs for seismic interpretation. A synthetic data example demonstrates the effectiveness of the VMD-based WVD approach and its superiority comparison to the WVD, smoothed pseudoWVD and empirical mode decomposition based WVD approaches. Real seismic data applications show that spectral decomposition with VMD-based WVD has a strong ability to highlight hydrocarbon-related information.
      PubDate: Nov. 2019
      Issue No: Vol. 12, No. 11 (2019)
  • Share and Manage Your Research Data
    • Pages: 4684 - 4684
      Abstract: Advertisement, IEEE.
      PubDate: Nov. 2019
      Issue No: Vol. 12, No. 11 (2019)
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