Pub Date : 2016-08-01DOI: 10.1109/WHISPERS.2016.8071706
Charoula Andreou, Franziska Halbritter, Derek M. Rogge, R. Müller
Materials of interest comprised in a hyperspectral image often present intra-class spectral variability inherent to their natural compositional make-up. Obtaining the best spectral representations of such materials with respect to a given application is critical for both identification and spatial mapping. Recently, a multiscaled-band partitioning (MSBP) approach has been developed for detecting and clustering spectrally similar but physically distinct materials. In this work, it is examined 1) whether the endmember clusters of the multiscaled-band partitioning contribute to an improved abundance estimation compared to other endmember extraction methods and, 2) to what extent different unmixing strategies can retain the spectral variability of the extracted endmember clusters in the resulted abundance maps. Experiments were conducted using an airborne hyperspectral dataset highlighting the potential of MSBP for the unmixing process in case of materials with intra-class variability.
{"title":"Effects of the multiscaled-band partitioning on the abundance estimation","authors":"Charoula Andreou, Franziska Halbritter, Derek M. Rogge, R. Müller","doi":"10.1109/WHISPERS.2016.8071706","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071706","url":null,"abstract":"Materials of interest comprised in a hyperspectral image often present intra-class spectral variability inherent to their natural compositional make-up. Obtaining the best spectral representations of such materials with respect to a given application is critical for both identification and spatial mapping. Recently, a multiscaled-band partitioning (MSBP) approach has been developed for detecting and clustering spectrally similar but physically distinct materials. In this work, it is examined 1) whether the endmember clusters of the multiscaled-band partitioning contribute to an improved abundance estimation compared to other endmember extraction methods and, 2) to what extent different unmixing strategies can retain the spectral variability of the extracted endmember clusters in the resulted abundance maps. Experiments were conducted using an airborne hyperspectral dataset highlighting the potential of MSBP for the unmixing process in case of materials with intra-class variability.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131271116","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 : 2016-08-01DOI: 10.1109/WHISPERS.2016.8071703
Karmon Vongsy, M. Mendenhall
Change detection (CD) is an important topic in the remote sensing community. Although many CD works exist using spatial information or spectral information only, few works have incorporated both in the CD process. We propose a fused spatial-spectral feature vector for use in a maximum likelihood correlation coefficient (MLCC)-based change detector where the resulting test statistic provides the ability to label changes as departures or arrivals relative to the reference image. Results show that incorporating both spatial and spectral information has an advantage over either one independently. Additionally, incorporating spatial and spectral information in the CD process adds some robustness in the presence of misregistration errors.
{"title":"Integrating spatial & spectral information for change detection in hyperspectral imagery","authors":"Karmon Vongsy, M. Mendenhall","doi":"10.1109/WHISPERS.2016.8071703","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071703","url":null,"abstract":"Change detection (CD) is an important topic in the remote sensing community. Although many CD works exist using spatial information or spectral information only, few works have incorporated both in the CD process. We propose a fused spatial-spectral feature vector for use in a maximum likelihood correlation coefficient (MLCC)-based change detector where the resulting test statistic provides the ability to label changes as departures or arrivals relative to the reference image. Results show that incorporating both spatial and spectral information has an advantage over either one independently. Additionally, incorporating spatial and spectral information in the CD process adds some robustness in the presence of misregistration errors.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115555081","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 : 2016-08-01DOI: 10.1109/WHISPERS.2016.8071661
H. Su, Bo Zhao
Conformal geometric algebra (CGA) has several advantages such as consistent geometric representation, compact algebra formulae, efficient geometric computing, coordinate free, and dimensionality independent etc., it can provides a new mathematical tool for hyperspectral dimensionality reduction. In this paper, an efficient band selection and classification approach for hyperspectral imagery based on CGA is proposed. In order to achieve more concise, fast, robust hyperspectral dimensionality reduction, the CGA-supported band selection method in conformal space is designed. The experiment results show that the CGA-based band selection algorithm outperforms the popular sequential forward selection (SFS) and particle swarm optimization (PSO) with lower cost for hyperspectral band selection.
{"title":"Conformal geometric algebra based band selection and classification for hyperspectral imagery","authors":"H. Su, Bo Zhao","doi":"10.1109/WHISPERS.2016.8071661","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071661","url":null,"abstract":"Conformal geometric algebra (CGA) has several advantages such as consistent geometric representation, compact algebra formulae, efficient geometric computing, coordinate free, and dimensionality independent etc., it can provides a new mathematical tool for hyperspectral dimensionality reduction. In this paper, an efficient band selection and classification approach for hyperspectral imagery based on CGA is proposed. In order to achieve more concise, fast, robust hyperspectral dimensionality reduction, the CGA-supported band selection method in conformal space is designed. The experiment results show that the CGA-based band selection algorithm outperforms the popular sequential forward selection (SFS) and particle swarm optimization (PSO) with lower cost for hyperspectral band selection.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125704317","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 : 2016-08-01DOI: 10.1109/WHISPERS.2016.8071727
Maider Marin-McGee, M. Velez-Reyes
A spectrally weighted structure tensor (SWST) is applied to tensor nonlinear anisotropic diffusion (TAND) for Coherence Enhancing Diffusion (CED). Experiments on spatial enhancement of hyperspectral imagery from thyroid tissue are shown. TAND-CED with a diffusion tensor derived from the SWST is compared with the one using the diffusion tensor derived from the classical (uniformly weighted) structure tensor (CST). Comparisons between methods show that the SWST produces more complete edges with CED.
{"title":"Coherence enhancement diffusion for hyperspectral imagery using a spectrally weighted structure tensor","authors":"Maider Marin-McGee, M. Velez-Reyes","doi":"10.1109/WHISPERS.2016.8071727","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071727","url":null,"abstract":"A spectrally weighted structure tensor (SWST) is applied to tensor nonlinear anisotropic diffusion (TAND) for Coherence Enhancing Diffusion (CED). Experiments on spatial enhancement of hyperspectral imagery from thyroid tissue are shown. TAND-CED with a diffusion tensor derived from the SWST is compared with the one using the diffusion tensor derived from the classical (uniformly weighted) structure tensor (CST). Comparisons between methods show that the SWST produces more complete edges with CED.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128280071","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 : 2016-08-01DOI: 10.1109/WHISPERS.2016.8071746
Yair Elhadad, S. Rotman, D. Blumberg
In this paper, we test anomaly change detection algorithms in hyperspectral images. Focusing on difference-based algorithms, our goal is to optimize performance using new methods that utilize the spatial and statistical characteristics of the images. These methods increase the probability of detection while minimizing false alarms. The algorithms are tested on the hyperspectral images of the Rochester Institute of Technology (RIT).
{"title":"Analysis of hyperspectral anomaly change detection algorithms","authors":"Yair Elhadad, S. Rotman, D. Blumberg","doi":"10.1109/WHISPERS.2016.8071746","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071746","url":null,"abstract":"In this paper, we test anomaly change detection algorithms in hyperspectral images. Focusing on difference-based algorithms, our goal is to optimize performance using new methods that utilize the spatial and statistical characteristics of the images. These methods increase the probability of detection while minimizing false alarms. The algorithms are tested on the hyperspectral images of the Rochester Institute of Technology (RIT).","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"22 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129142787","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 : 2016-08-01DOI: 10.1109/WHISPERS.2016.8071743
J. Cierniewski, J. Ceglarek, A. Karnieli, Sławomir Królewicz, Cezary Kaźmierowski, Bogdan Zagajewski
The objective of this study was to assess the relationship between the hyperspectral reflectance of soils and its albedo, measured under various roughness conditions. 108 soil surfaces measurements were conducted in Poland and Israel. Each surface was characterized by its diurnal albedo variation in the field as well as its reflectance spectra that was obtained in the laboratory. The best fit to the model was achieved by postprocessing manipulation of the spectra, namely second derivate transformation. Using stepwise elimination process, four spectral wavelengths, as well as roughness index, were selected for modeling. The resulted models allow predicting the albedo of a soil at specific roughness for any solar zenithal angle, provided that hyperspectral reflectance data is available.
{"title":"Use of laboratory hyperspectral reflectance data of soils for predicting their diurnal albedo dynamics accomodating their roughness","authors":"J. Cierniewski, J. Ceglarek, A. Karnieli, Sławomir Królewicz, Cezary Kaźmierowski, Bogdan Zagajewski","doi":"10.1109/WHISPERS.2016.8071743","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071743","url":null,"abstract":"The objective of this study was to assess the relationship between the hyperspectral reflectance of soils and its albedo, measured under various roughness conditions. 108 soil surfaces measurements were conducted in Poland and Israel. Each surface was characterized by its diurnal albedo variation in the field as well as its reflectance spectra that was obtained in the laboratory. The best fit to the model was achieved by postprocessing manipulation of the spectra, namely second derivate transformation. Using stepwise elimination process, four spectral wavelengths, as well as roughness index, were selected for modeling. The resulted models allow predicting the albedo of a soil at specific roughness for any solar zenithal angle, provided that hyperspectral reflectance data is available.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132165735","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 : 2016-08-01DOI: 10.1109/WHISPERS.2016.8071798
Nasehe Jamshidpour, Saeid Homayouni, A. Safari
Hyperspectral images classification has been one of the most popular research areas in remote sensing community in the past decades. However, there are still some difficulties that need specific attentions, such as the lack of enough labeled samples for training the classifier and the high dimensionality problem, which degrade the supervised classification performance dramatically. The main idea of semisupervised learning is to overcome the contribution of unlabeled samples, which are available in an enormous amount. In this paper, we propose a graph-based semisupervised classification method, using both spectral and spatial information. More specifically, two graphs are constructed and each one exploits the relationship among pixels in spectral and spatial spaces respectively. Then, the Laplacians of both constructed graphs are merged in order to form a weighted joint graph. The experimental results are carried out on Indian Pine AVIRIS image data. The efficiency and the excellent performance of the proposed method is clearly observed in comparison with well-known supervised classification methods, such as SVM, in both terms of accuracy and homogeneity of the produced classified maps.
{"title":"Graph-based semi-supervised hyperspectral image classification using spatial information","authors":"Nasehe Jamshidpour, Saeid Homayouni, A. Safari","doi":"10.1109/WHISPERS.2016.8071798","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071798","url":null,"abstract":"Hyperspectral images classification has been one of the most popular research areas in remote sensing community in the past decades. However, there are still some difficulties that need specific attentions, such as the lack of enough labeled samples for training the classifier and the high dimensionality problem, which degrade the supervised classification performance dramatically. The main idea of semisupervised learning is to overcome the contribution of unlabeled samples, which are available in an enormous amount. In this paper, we propose a graph-based semisupervised classification method, using both spectral and spatial information. More specifically, two graphs are constructed and each one exploits the relationship among pixels in spectral and spatial spaces respectively. Then, the Laplacians of both constructed graphs are merged in order to form a weighted joint graph. The experimental results are carried out on Indian Pine AVIRIS image data. The efficiency and the excellent performance of the proposed method is clearly observed in comparison with well-known supervised classification methods, such as SVM, in both terms of accuracy and homogeneity of the produced classified maps.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114081431","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 : 2016-08-01DOI: 10.1109/WHISPERS.2016.8071715
Saurabh Morchhale, V. P. Pauca, R. Plemmons, T. Torgersen
We investigate classification from pixel-level fusion of Hyperspectral (HSI) and Light Detection and Ranging (LiDAR) data using convolutional neural networks (CNN). HSI and LiDAR imaging are complementary modalities increasingly used together for geospatial data collection in remote sensing. HSI data is used to glean information about material composition and LiDAR data provides information about the geometry of objects in the scene. Two key questions relative to classification performance are addressed: the effect of merging multi-modal data and the effect of uncertainty in the CNN training data. Two recent co-registered HSI and LiDAR datasets are used here to characterize performance. One was collected, over Houston TX, by the University of Houston National Center for Airborne Laser Mapping with NSF sponsorship, and the other was collected, over Gulfport MS, by Universities of Florida and Missouri with NGA sponsorship.
{"title":"Classification of pixel-level fused hyperspectral and lidar data using deep convolutional neural networks","authors":"Saurabh Morchhale, V. P. Pauca, R. Plemmons, T. Torgersen","doi":"10.1109/WHISPERS.2016.8071715","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071715","url":null,"abstract":"We investigate classification from pixel-level fusion of Hyperspectral (HSI) and Light Detection and Ranging (LiDAR) data using convolutional neural networks (CNN). HSI and LiDAR imaging are complementary modalities increasingly used together for geospatial data collection in remote sensing. HSI data is used to glean information about material composition and LiDAR data provides information about the geometry of objects in the scene. Two key questions relative to classification performance are addressed: the effect of merging multi-modal data and the effect of uncertainty in the CNN training data. Two recent co-registered HSI and LiDAR datasets are used here to characterize performance. One was collected, over Houston TX, by the University of Houston National Center for Airborne Laser Mapping with NSF sponsorship, and the other was collected, over Gulfport MS, by Universities of Florida and Missouri with NGA sponsorship.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121726403","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 : 2016-08-01DOI: 10.1109/WHISPERS.2016.8071807
R. Greenberger, B. Ehlmann, P. Jewell, L. Birgenheier, R. Green
Oil shales contain abundant immature organic matter and are a potential unconventional petroleum resource. Prior studies have used visible/shortwave infrared imaging spectroscopy to map surface exposures of deposits from satellite and airborne platforms and image cores in the laboratory. Here, we work at an intermediate, outcrop-scale, testing the ability of field-based imaging spectroscopy to identify oil shale strata and characterize the depositional environments that led to enrichment of organic matter in sedimentary rocks within the Green River Formation, Utah, USA. The oil shale layers as well as carbonates, phyllosilicates, gypsum, hydrated silica, and ferric oxides are identified in discrete lithologic units and successfully mapped in the images, showing a transition from siliciclastic to carbonate- and organic-rich rocks consistent with previous stratigraphic studies conducted with geological fieldwork.
{"title":"Detection of organic-rich oil shales of the green river formation, Utah, with ground-based imaging spectroscopy","authors":"R. Greenberger, B. Ehlmann, P. Jewell, L. Birgenheier, R. Green","doi":"10.1109/WHISPERS.2016.8071807","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071807","url":null,"abstract":"Oil shales contain abundant immature organic matter and are a potential unconventional petroleum resource. Prior studies have used visible/shortwave infrared imaging spectroscopy to map surface exposures of deposits from satellite and airborne platforms and image cores in the laboratory. Here, we work at an intermediate, outcrop-scale, testing the ability of field-based imaging spectroscopy to identify oil shale strata and characterize the depositional environments that led to enrichment of organic matter in sedimentary rocks within the Green River Formation, Utah, USA. The oil shale layers as well as carbonates, phyllosilicates, gypsum, hydrated silica, and ferric oxides are identified in discrete lithologic units and successfully mapped in the images, showing a transition from siliciclastic to carbonate- and organic-rich rocks consistent with previous stratigraphic studies conducted with geological fieldwork.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130191885","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 : 2016-08-01DOI: 10.1109/WHISPERS.2016.8071734
Théo Masson, M. Mura, M. Dumont, P. Sirguey, M. Veganzones, J. Chanussot, J. Dedieu
Spectral Unmixing is the most recent method used to recover the Snow Cover Fraction of an area, but it depends particularly on the relevance of the set of endmembers. This communication investigates different strategies for defining set of endmembers for retrieving snow cover fraction with spectral unmixing. Endmembers can be estimated from on site measurements or estimated directly on the image. In this work we propose a set of endmembers associating semantics of field data for snow endmembers with the extraction of a set in a date without snow for other materials. A heterogeneous area in the Alps was considered in the experiment. Considering reference maps of snow available for several dates, Precision and Mean Absolute Error were computed for evaluating the estimated Snow Cover Fractions. Results obtained confirm the soundness of the proposed approach for low snow fraction.
{"title":"Snow cover estimation based on spectral unmixing","authors":"Théo Masson, M. Mura, M. Dumont, P. Sirguey, M. Veganzones, J. Chanussot, J. Dedieu","doi":"10.1109/WHISPERS.2016.8071734","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071734","url":null,"abstract":"Spectral Unmixing is the most recent method used to recover the Snow Cover Fraction of an area, but it depends particularly on the relevance of the set of endmembers. This communication investigates different strategies for defining set of endmembers for retrieving snow cover fraction with spectral unmixing. Endmembers can be estimated from on site measurements or estimated directly on the image. In this work we propose a set of endmembers associating semantics of field data for snow endmembers with the extraction of a set in a date without snow for other materials. A heterogeneous area in the Alps was considered in the experiment. Considering reference maps of snow available for several dates, Precision and Mean Absolute Error were computed for evaluating the estimated Snow Cover Fractions. Results obtained confirm the soundness of the proposed approach for low snow fraction.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133573420","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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