Pub Date : 2019-07-01DOI: 10.1109/IGARSS.2019.8897970
M. Koch, A. Gaber, N. Darwish, Juliette Bateman, S. Gopal, M. Helmi
Land subsidence and flooding events in coastal Semarang City, Central Java, has had severe impacts on the region’s population and economy. This work presents a methodology based on a combination of InSAR subsidence mapping and optical classification and change detection techniques to estimate the spatial distribution of subsidence rate and assess its impact on urbanization growth, land conversion and coastal flooding. Significant spatial relationships were found between urban zones (building density), flood extent (shoreline retreat) and subsidence rates. The overexploitation of aquifers and city zoning development contribute to accelerate subsidence rates.
{"title":"Estimating Land Subsidence in Relation to Urban Expansion in Semarang City, Indonesia, Using InSAR and Optical Change Detection Methods","authors":"M. Koch, A. Gaber, N. Darwish, Juliette Bateman, S. Gopal, M. Helmi","doi":"10.1109/IGARSS.2019.8897970","DOIUrl":"https://doi.org/10.1109/IGARSS.2019.8897970","url":null,"abstract":"Land subsidence and flooding events in coastal Semarang City, Central Java, has had severe impacts on the region’s population and economy. This work presents a methodology based on a combination of InSAR subsidence mapping and optical classification and change detection techniques to estimate the spatial distribution of subsidence rate and assess its impact on urbanization growth, land conversion and coastal flooding. Significant spatial relationships were found between urban zones (building density), flood extent (shoreline retreat) and subsidence rates. The overexploitation of aquifers and city zoning development contribute to accelerate subsidence rates.","PeriodicalId":13262,"journal":{"name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","volume":"18 1","pages":"9686-9689"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73430845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-01DOI: 10.1109/IGARSS.2019.8900031
U. Khati, Gulab Singh, S. Tebaldini
Stock volume is an important forest inventory parameter. In case of agro-forests and plantation forests, stock volume estimates are important as they provide reliable indicator of the productivity of these species. In this study stock volume loss due to harvest of polar plantations between 2017 and 2018 are estimated using ALOS-2/PALSAR-2 backscatter data. Using simple linear regression models the AGB of the plantations before and after harvest are estimated. These are converted to stock volume loss per hectare. From field inventory, the actual stock volume during harvest are measured. These are validated against the estimations using two models – M1 and M2. Model M1, utilizes only HV-pol backscatter data and provides a lower accuracy with r2 = 0.46. Model M2 utilizes HH- and HV-pol backscatter and provides stock volume loss estimation with r2 = 0.51.
{"title":"Stock Volume Loss Estimation in Poplars using Regression Models and ALOS-2/PALSAR-2 backscatter","authors":"U. Khati, Gulab Singh, S. Tebaldini","doi":"10.1109/IGARSS.2019.8900031","DOIUrl":"https://doi.org/10.1109/IGARSS.2019.8900031","url":null,"abstract":"Stock volume is an important forest inventory parameter. In case of agro-forests and plantation forests, stock volume estimates are important as they provide reliable indicator of the productivity of these species. In this study stock volume loss due to harvest of polar plantations between 2017 and 2018 are estimated using ALOS-2/PALSAR-2 backscatter data. Using simple linear regression models the AGB of the plantations before and after harvest are estimated. These are converted to stock volume loss per hectare. From field inventory, the actual stock volume during harvest are measured. These are validated against the estimations using two models – M1 and M2. Model M1, utilizes only HV-pol backscatter data and provides a lower accuracy with r2 = 0.46. Model M2 utilizes HH- and HV-pol backscatter and provides stock volume loss estimation with r2 = 0.51.","PeriodicalId":13262,"journal":{"name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","volume":"23 1","pages":"5933-5935"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75321888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-01DOI: 10.1109/IGARSS.2019.8898539
Xiuxiu Hu, Yan Shi, Wei Li, R. Tao
The fusion of Panchromatic (PAN) and Hyperspectral image (HSI) aims at improving resolution in spatial and spectral domain simultaneously. Multiresolution analysis is a widely used method for Mutispectral or HSI pansharpening. However, only detail information from PAN is considered while ignoring the detail information from HSI. In this paper, an improved approach based on multiresolution analysis is proposed, which extracts detail information from both PAN and HSI by choosing optimal multiresolution layers. Another contribution is that we discuss the weight when fusing the detail information. The experimental results demonstrate that the proposed method can provide better quality metrics and visual effects when compared with some existing methods.
{"title":"Improved Multiresolution Analysis Method for Hyperspectral Pansharpening","authors":"Xiuxiu Hu, Yan Shi, Wei Li, R. Tao","doi":"10.1109/IGARSS.2019.8898539","DOIUrl":"https://doi.org/10.1109/IGARSS.2019.8898539","url":null,"abstract":"The fusion of Panchromatic (PAN) and Hyperspectral image (HSI) aims at improving resolution in spatial and spectral domain simultaneously. Multiresolution analysis is a widely used method for Mutispectral or HSI pansharpening. However, only detail information from PAN is considered while ignoring the detail information from HSI. In this paper, an improved approach based on multiresolution analysis is proposed, which extracts detail information from both PAN and HSI by choosing optimal multiresolution layers. Another contribution is that we discuss the weight when fusing the detail information. The experimental results demonstrate that the proposed method can provide better quality metrics and visual effects when compared with some existing methods.","PeriodicalId":13262,"journal":{"name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","volume":"17 1","pages":"2778-2781"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75343471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-01DOI: 10.1109/IGARSS.2019.8900473
Mengfei Yu, Fei Li, Y. Deng, Heng Zhang, Weidong Yu, Robert Wang
The GaoFen-3(GF-3) satellite is the first full-polarized synthetic aperture radar (SAR) imaging satellite of china, which was launched in August 2016. This paper obtained the geographic information based on the actual data from GF-3 satellite. Range-Doppler Model is used to process GF-3 data to verify the different between the position accuracy after correction and the practical positioning accuracy. The difference provides a certain reference for domestic satellites to promote actual positioning accuracy.
{"title":"Preliminary Analgsis of Geometric Positioning Accuracy Based on Gaofen-3 Data","authors":"Mengfei Yu, Fei Li, Y. Deng, Heng Zhang, Weidong Yu, Robert Wang","doi":"10.1109/IGARSS.2019.8900473","DOIUrl":"https://doi.org/10.1109/IGARSS.2019.8900473","url":null,"abstract":"The GaoFen-3(GF-3) satellite is the first full-polarized synthetic aperture radar (SAR) imaging satellite of china, which was launched in August 2016. This paper obtained the geographic information based on the actual data from GF-3 satellite. Range-Doppler Model is used to process GF-3 data to verify the different between the position accuracy after correction and the practical positioning accuracy. The difference provides a certain reference for domestic satellites to promote actual positioning accuracy.","PeriodicalId":13262,"journal":{"name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","volume":"6 1","pages":"2937-2940"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75380326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-01DOI: 10.1109/IGARSS.2019.8897878
Geng-Ming Jiang, Wen-Xia Li, Guicai Li, Chuan Li
This work addresses the development of Diurnal Temperature Cycle (DTC) model and its application of temporal normalization of Land Surface Temperature (LST) derived from the measurements acquired by the Advanced Himawari Imager on Himawari-8. The results show that the DTC model can describe the LST diurnal variation with root-mean-square error (RMSE) less than 0.45 K at the four selected typical locations, and with about 90% RMSEs less than 1.0 K over the whole study area. Finally, temporally normalized LST is produced using the DTC model.
{"title":"Temporal Normalization of Land Surface Temperature Derived from Ahi-8 Measurements using a Diurnal Temperature Cycle Model","authors":"Geng-Ming Jiang, Wen-Xia Li, Guicai Li, Chuan Li","doi":"10.1109/IGARSS.2019.8897878","DOIUrl":"https://doi.org/10.1109/IGARSS.2019.8897878","url":null,"abstract":"This work addresses the development of Diurnal Temperature Cycle (DTC) model and its application of temporal normalization of Land Surface Temperature (LST) derived from the measurements acquired by the Advanced Himawari Imager on Himawari-8. The results show that the DTC model can describe the LST diurnal variation with root-mean-square error (RMSE) less than 0.45 K at the four selected typical locations, and with about 90% RMSEs less than 1.0 K over the whole study area. Finally, temporally normalized LST is produced using the DTC model.","PeriodicalId":13262,"journal":{"name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","volume":"41 1","pages":"1821-1824"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75396975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-01DOI: 10.1109/IGARSS.2019.8900596
F. Tupin, L. Denis, C. Deledalle, G. Ferraioli
Speckle reduction is a major issue for many SAR imaging applications using amplitude, interferometric, polarimetric or tomographic data. This subject has been widely investigated using various approaches. Since a decade, breakthrough methods based on patches have brought unprecedented results to improve the estimation of radar properties. In this paper, we give a review of the different adaptations which have been proposed in the past years for different SAR modalities (mono-channel data like intensity images, multi-channel data like interferometric, tomographic or polarimetric data, or multimodalities combining optic and SAR images), and discuss the new trends on this subject.
{"title":"Ten Years of Patch-Based Approaches for Sar Imaging: A Review","authors":"F. Tupin, L. Denis, C. Deledalle, G. Ferraioli","doi":"10.1109/IGARSS.2019.8900596","DOIUrl":"https://doi.org/10.1109/IGARSS.2019.8900596","url":null,"abstract":"Speckle reduction is a major issue for many SAR imaging applications using amplitude, interferometric, polarimetric or tomographic data. This subject has been widely investigated using various approaches. Since a decade, breakthrough methods based on patches have brought unprecedented results to improve the estimation of radar properties. In this paper, we give a review of the different adaptations which have been proposed in the past years for different SAR modalities (mono-channel data like intensity images, multi-channel data like interferometric, tomographic or polarimetric data, or multimodalities combining optic and SAR images), and discuss the new trends on this subject.","PeriodicalId":13262,"journal":{"name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","volume":"3 1","pages":"5105-5108"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75459971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Based on the different micro-Doppler modulation of three motion states of rotor target, i.e., hovering, rising and falling, a convolutional neural network (CNN) is utilized for motion states classification of rotor target in this paper. Firstly, to obtain the time-frequency spectrograms of target, the short-time Fourier transform (STFT) is applied to target echo signal after pulse compression, theoretical analysis and simulation experiments show that the maximum value of micro-Doppler frequency varies with different motion states. Secondly, we partition the echo data under three different radios of training data, i.e., 20%, 33% and 50%. Finally, the spectrogram data are fed into the proposed CNN architecture, and the cross validation is utilized to investigate the robustness of the proposed method. Experimental results show that with the continuous training iteration of the network, the data fitting ability and classification accuracy increases gradually and reaches 98.23% on average with the training data radio is 50%.
{"title":"Motion States Classification of Rotor Target Based On Micro-Doppler Features Using CNN","authors":"Wantian Wang, Zi-yue Tang, Xin Xiong, Yi-chang Chen, Yuanpeng Zhang, Yongjian Sun, Zhenbo Zhu, Chang Zhou","doi":"10.1109/IGARSS.2019.8900361","DOIUrl":"https://doi.org/10.1109/IGARSS.2019.8900361","url":null,"abstract":"Based on the different micro-Doppler modulation of three motion states of rotor target, i.e., hovering, rising and falling, a convolutional neural network (CNN) is utilized for motion states classification of rotor target in this paper. Firstly, to obtain the time-frequency spectrograms of target, the short-time Fourier transform (STFT) is applied to target echo signal after pulse compression, theoretical analysis and simulation experiments show that the maximum value of micro-Doppler frequency varies with different motion states. Secondly, we partition the echo data under three different radios of training data, i.e., 20%, 33% and 50%. Finally, the spectrogram data are fed into the proposed CNN architecture, and the cross validation is utilized to investigate the robustness of the proposed method. Experimental results show that with the continuous training iteration of the network, the data fitting ability and classification accuracy increases gradually and reaches 98.23% on average with the training data radio is 50%.","PeriodicalId":13262,"journal":{"name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","volume":"18 1","pages":"1390-1393"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75471337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hyperspectral (HS) and multispectral (MS) image fusion has attracted great attention during the past decades. Numerous of fusion methods have been developed and shown their effectiveness particularly on simulated data. Nonetheless, for real remote sensing data, the different acquisition times or conditions result in a serious spectral distortion and severely affect the fusion quality. Yet very few works have considered this issue. In this paper, a spectral modulation (SM) method is proposed to better maintain the spectral information of the HS data when fusing with MS data. The goal is to generate an adjusted MS image that would have been observed under the same imaging conditions with the corresponding HS sensor. Experiments on two HS and MS data sets acquired by different platforms demonstrate that the proposed method is beneficial to the spectral fidelity and spatial enhancement of the fused image compared with some state-of-the-art fusion techniques.
{"title":"Spectral Modulation for Fusion of Hyperspectral and Multispectral Images","authors":"Xiaochen Lu, Xiangzhen Yu, Wenming Tang, Bingqi Zhu","doi":"10.1109/IGARSS.2019.8898754","DOIUrl":"https://doi.org/10.1109/IGARSS.2019.8898754","url":null,"abstract":"Hyperspectral (HS) and multispectral (MS) image fusion has attracted great attention during the past decades. Numerous of fusion methods have been developed and shown their effectiveness particularly on simulated data. Nonetheless, for real remote sensing data, the different acquisition times or conditions result in a serious spectral distortion and severely affect the fusion quality. Yet very few works have considered this issue. In this paper, a spectral modulation (SM) method is proposed to better maintain the spectral information of the HS data when fusing with MS data. The goal is to generate an adjusted MS image that would have been observed under the same imaging conditions with the corresponding HS sensor. Experiments on two HS and MS data sets acquired by different platforms demonstrate that the proposed method is beneficial to the spectral fidelity and spatial enhancement of the fused image compared with some state-of-the-art fusion techniques.","PeriodicalId":13262,"journal":{"name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","volume":"82 6 1","pages":"3149-3152"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75541087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-01DOI: 10.1109/IGARSS.2019.8897885
Yuexin Gao, Xinyu Zhang, M. Xing, Jixiang Fu, Zi-jing Zhang, Ying Wang
A suitable regularization parameter plays an important role in sparse ISAR imaging algorithms. With a proper regularization parameter, the quality of ISAR images improves. In this paper, the Homotopy re-weighted ℓ1-norm minimization is applied to ISAR imaging. This method is able to choose the accurate regularization parameter for each point in ISAR image with high efficiency. As a result, the imaging results processed by this method contain more details of the target and less artificial points. Both simulated and real data experiments validate the feasibility of the proposed method.
{"title":"ISAR Imaging Based on Homotopy Re-Weighted ℓ1-Norm Minimization","authors":"Yuexin Gao, Xinyu Zhang, M. Xing, Jixiang Fu, Zi-jing Zhang, Ying Wang","doi":"10.1109/IGARSS.2019.8897885","DOIUrl":"https://doi.org/10.1109/IGARSS.2019.8897885","url":null,"abstract":"A suitable regularization parameter plays an important role in sparse ISAR imaging algorithms. With a proper regularization parameter, the quality of ISAR images improves. In this paper, the Homotopy re-weighted ℓ1-norm minimization is applied to ISAR imaging. This method is able to choose the accurate regularization parameter for each point in ISAR image with high efficiency. As a result, the imaging results processed by this method contain more details of the target and less artificial points. Both simulated and real data experiments validate the feasibility of the proposed method.","PeriodicalId":13262,"journal":{"name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","volume":"68 1","pages":"1204-1207"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74184493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-01DOI: 10.1109/IGARSS.2019.8898302
Z. Jiao, Anxin Ding, A. Kokhanovsky, Yadong Dong
The linear kernel-driven RossThick-LiSparseReciprocal (RTLSR) bidirectional reflectance distribution function (BRDF) model was originally developed for modeling the simplified scenarios of the continuous and discreet vegetation canopies, and has been widely used to fit the multiangle observations for the vegetation-soil system of the land surface in many fields. However, there is a need to develop this model to characterize the light scattering properties of snow, which tends to exhibit strongly forward scattering behaviors. This study proposes a snow kernel to describe the reflectance anisotropy of snow, mainly based on the asymptotic radiative transfer theory (ART) for a semi-infinite weakly absorbing layer of snow, and then applies this kernel to the framework of kernel-driven BRDF model. This snow kernel adopts the analytic form of the ART model with an improved ability in forward scattering direction, particularly in a case of a large viewing zenith angle (> 60°) where the simulation accuracy of the ART model somewhat decreases in the principal plane (PP). Validation of this method was implemented using observed multiangle data. Pure snow targets were selected from the entire archive of the POLDER BRDF data. This validation demonstrates that this proposed snow kernel in the framework of the kernel-driven RTLSR model show potentials for many potential applications, particularly in the field of Earth’s water cycle and radiation budget where snow cover plays an important role.
{"title":"Modeling the Anisotropic Reflectance of Snow in a Kernel-Driven BRDF Model Framework Using a Snow Kernel","authors":"Z. Jiao, Anxin Ding, A. Kokhanovsky, Yadong Dong","doi":"10.1109/IGARSS.2019.8898302","DOIUrl":"https://doi.org/10.1109/IGARSS.2019.8898302","url":null,"abstract":"The linear kernel-driven RossThick-LiSparseReciprocal (RTLSR) bidirectional reflectance distribution function (BRDF) model was originally developed for modeling the simplified scenarios of the continuous and discreet vegetation canopies, and has been widely used to fit the multiangle observations for the vegetation-soil system of the land surface in many fields. However, there is a need to develop this model to characterize the light scattering properties of snow, which tends to exhibit strongly forward scattering behaviors. This study proposes a snow kernel to describe the reflectance anisotropy of snow, mainly based on the asymptotic radiative transfer theory (ART) for a semi-infinite weakly absorbing layer of snow, and then applies this kernel to the framework of kernel-driven BRDF model. This snow kernel adopts the analytic form of the ART model with an improved ability in forward scattering direction, particularly in a case of a large viewing zenith angle (> 60°) where the simulation accuracy of the ART model somewhat decreases in the principal plane (PP). Validation of this method was implemented using observed multiangle data. Pure snow targets were selected from the entire archive of the POLDER BRDF data. This validation demonstrates that this proposed snow kernel in the framework of the kernel-driven RTLSR model show potentials for many potential applications, particularly in the field of Earth’s water cycle and radiation budget where snow cover plays an important role.","PeriodicalId":13262,"journal":{"name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","volume":"57 1","pages":"740-743"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74244397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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