Pub Date : 2016-08-21DOI: 10.1109/WHISPERS.2016.8071686
G. Tochon, Delphine Pauwels, M. Mura, J. Chanussot
It is now possible to collect hyperspectral video sequences (HVS) at a near real-time frame rate. The wealth of spectral, spatial and temporal information of those sequences is particularly appealing for chemical gas plume tracking. Existing state-of-the-art methods for such applications however produce only a binary information regarding the position and shape of the gas plume in the HVS. Here, we introduce a novel method relying on spectral unmixing considerations to perform chemical gas plume tracking, which provides information related to the gas plume concentration in addition to its spatial localization. The proposed approach is validated and compared with three state-of-the-art methods on a real HVS.
{"title":"Unmixing-based gas plume tracking in LWIR hyperspectral video sequences","authors":"G. Tochon, Delphine Pauwels, M. Mura, J. Chanussot","doi":"10.1109/WHISPERS.2016.8071686","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071686","url":null,"abstract":"It is now possible to collect hyperspectral video sequences (HVS) at a near real-time frame rate. The wealth of spectral, spatial and temporal information of those sequences is particularly appealing for chemical gas plume tracking. Existing state-of-the-art methods for such applications however produce only a binary information regarding the position and shape of the gas plume in the HVS. Here, we introduce a novel method relying on spectral unmixing considerations to perform chemical gas plume tracking, which provides information related to the gas plume concentration in addition to its spatial localization. The proposed approach is validated and compared with three state-of-the-art methods on a real HVS.","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":"2016-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128891619","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 : 2016-08-21DOI: 10.1109/WHISPERS.2016.8071762
S. Gadal, W. Ouerghemmi
Cities are characterized by a complex mosaic of objects, representing the urban structures, the history and the transformations. The characterization of urban objects requires powerful methods combined with high resolution imagery, in this study we present an object characterization method that takes into consideration the spatial and spectral characteristics of remote sensing imagery, using an airborne hyperspectral image. The method consists of two mains steps; 1) a spectral classification of the objects using an external spectral library combined with image collected spectra, 2) a morphological classification of the objects using their geometric attributes. The goal is to provide an efficient objects characterization method that takes advantage of both spatial and spectral dimensions of hyperspectral imagery, and to improve classification methods efficiency.
{"title":"Morpho-spectral objects classification by hyperspectral airborne imagery","authors":"S. Gadal, W. Ouerghemmi","doi":"10.1109/WHISPERS.2016.8071762","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071762","url":null,"abstract":"Cities are characterized by a complex mosaic of objects, representing the urban structures, the history and the transformations. The characterization of urban objects requires powerful methods combined with high resolution imagery, in this study we present an object characterization method that takes into consideration the spatial and spectral characteristics of remote sensing imagery, using an airborne hyperspectral image. The method consists of two mains steps; 1) a spectral classification of the objects using an external spectral library combined with image collected spectra, 2) a morphological classification of the objects using their geometric attributes. The goal is to provide an efficient objects characterization method that takes advantage of both spatial and spectral dimensions of hyperspectral imagery, and to improve classification methods efficiency.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121370125","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 : 2016-08-21DOI: 10.1109/WHISPERS.2016.8071678
J. Chan, N. Yokoya
Hyperspectral data provide indispensable timely information for environmental monitoring. It has become one of the most sought after data set for many specific applications. However, for large areal coverage, spaceborne hyperspectral data are currently acquired at low resolution. Due to the proven usefulness of hyperspectral data and its potential in newer applications, many researchers have investigated novel enhancement methods for Earth Observation hyperspectral images. We have examined four different enhancement methods using a classification scheme at medium level of difficulty. Two of the examined methods are pansharpening methods and the other two are sub-space methods. The results do not show improvements in classification using spatially enhanced images except for the class of Pine trees. However, using full groundtruth of road and buildings, it is clear that spatially enhanced hyperspectral images achieve substantial improvement in classifying small sized houses. Better characterization of road networks can be visualized and also higher accuracy is observed but to a lesser extent than buildings. Among the four methods, a pansharpening method performed best.
{"title":"Mapping land covers of brussels capital region using spatially enhanced hyperspectral images","authors":"J. Chan, N. Yokoya","doi":"10.1109/WHISPERS.2016.8071678","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071678","url":null,"abstract":"Hyperspectral data provide indispensable timely information for environmental monitoring. It has become one of the most sought after data set for many specific applications. However, for large areal coverage, spaceborne hyperspectral data are currently acquired at low resolution. Due to the proven usefulness of hyperspectral data and its potential in newer applications, many researchers have investigated novel enhancement methods for Earth Observation hyperspectral images. We have examined four different enhancement methods using a classification scheme at medium level of difficulty. Two of the examined methods are pansharpening methods and the other two are sub-space methods. The results do not show improvements in classification using spatially enhanced images except for the class of Pine trees. However, using full groundtruth of road and buildings, it is clear that spatially enhanced hyperspectral images achieve substantial improvement in classifying small sized houses. Better characterization of road networks can be visualized and also higher accuracy is observed but to a lesser extent than buildings. Among the four methods, a pansharpening method performed best.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114137517","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 : 2016-08-21DOI: 10.1109/WHISPERS.2016.8071675
Lucas Drumetz, J. Chanussot, C. Jutten
Endmember variability has been identified as one of the main limitations of the usual Linear Mixing Model, conventionally used to perform spectral unmixing of hyperspectral data. The topic is currently receiving a lot of attention from the community, and many new algorithms have recently been developed to model this variability and take it into account. In this paper, we review state of the art methods dealing with this problem and classify them into three categories: the algorithms based on endmember bundles, the ones based on computational models, and the ones based on parametric physics-based models. We discuss the advantages and drawbacks of each category of methods and list some open problems and current challenges.
{"title":"Variability of the endmembers in spectral unmixing: Recent advances","authors":"Lucas Drumetz, J. Chanussot, C. Jutten","doi":"10.1109/WHISPERS.2016.8071675","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071675","url":null,"abstract":"Endmember variability has been identified as one of the main limitations of the usual Linear Mixing Model, conventionally used to perform spectral unmixing of hyperspectral data. The topic is currently receiving a lot of attention from the community, and many new algorithms have recently been developed to model this variability and take it into account. In this paper, we review state of the art methods dealing with this problem and classify them into three categories: the algorithms based on endmember bundles, the ones based on computational models, and the ones based on parametric physics-based models. We discuss the advantages and drawbacks of each category of methods and list some open problems and current challenges.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"218 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129479008","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 : 2016-08-21DOI: 10.1109/WHISPERS.2016.8071761
N. Yokoya, Pedram Ghamisi
This paper presents a new method for unsupervised detection of multiple changes using time-serires hyperspectral data. The proposed method is based on fractional-order Darwinian particle swarm optimization (FODPSO) segmentation. The proposed method is applied to monitor land-cover changes following the Fukushima Daiichi nuclear disaster using multitemporal Hyperion images. Experimental results indicate that the integration of segmentation and a time-series of hyperspectral images has great potential for unsupervised detection of multiple changes.
{"title":"Land-cover monitoring using time-series hyperspectral data via fractional-order darwinian particle swarm optimization segmentation","authors":"N. Yokoya, Pedram Ghamisi","doi":"10.1109/WHISPERS.2016.8071761","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071761","url":null,"abstract":"This paper presents a new method for unsupervised detection of multiple changes using time-serires hyperspectral data. The proposed method is based on fractional-order Darwinian particle swarm optimization (FODPSO) segmentation. The proposed method is applied to monitor land-cover changes following the Fukushima Daiichi nuclear disaster using multitemporal Hyperion images. Experimental results indicate that the integration of segmentation and a time-series of hyperspectral images has great potential for unsupervised detection of multiple changes.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"240 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123097128","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 : 2016-08-21DOI: 10.1109/WHISPERS.2016.8071756
G. Mozgeris, S. Gadal, D. Jonikavicius, L. Straigytė, W. Ouerghemmi, Vytaute Juodkiene
Imaging system based on simultaneous use of Rikola hyperspectral and RGB/NIR cameras installed on a manned ultra-light aircraft is introduced in this study. Simultaneously acquired hyperspectral and color-infrared (CIR) images were tested for their potential to identify deciduous tree species and estimate tree health in Kaunas city, Lithuania. Six urban deciduous tree species were separated using tree crown level statistics, extracted from 16 visible-near infrared spectral band hyperspectral images, and discriminant analyses with an overall classification accuracy of 63.1 %. Classification accuracy increased by 3 percent when hyperspectral images were integrated with simultaneously acquired CIR images. The accuracy in identifying tree health using fused hyperspectral and CIR images, ranged from poor to moderate.
{"title":"Hyperspectral and color-infrared imaging from ultralight aircraft: Potential to recognize tree species in urban environments","authors":"G. Mozgeris, S. Gadal, D. Jonikavicius, L. Straigytė, W. Ouerghemmi, Vytaute Juodkiene","doi":"10.1109/WHISPERS.2016.8071756","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071756","url":null,"abstract":"Imaging system based on simultaneous use of Rikola hyperspectral and RGB/NIR cameras installed on a manned ultra-light aircraft is introduced in this study. Simultaneously acquired hyperspectral and color-infrared (CIR) images were tested for their potential to identify deciduous tree species and estimate tree health in Kaunas city, Lithuania. Six urban deciduous tree species were separated using tree crown level statistics, extracted from 16 visible-near infrared spectral band hyperspectral images, and discriminant analyses with an overall classification accuracy of 63.1 %. Classification accuracy increased by 3 percent when hyperspectral images were integrated with simultaneously acquired CIR images. The accuracy in identifying tree health using fused hyperspectral and CIR images, ranged from poor to moderate.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":" 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113946349","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 : 2016-08-21DOI: 10.1109/WHISPERS.2016.8071730
G. Tochon, Lucas Drumetz, M. Veganzones, M. Mura, J. Chanussot
The linear mixing model is widely assumed when unmixing hyperspectral images, but it cannot account for endmembers spectral variability. Thus, several workarounds have arisen in the hyperspectral unmixing literature, such as the extended linear mixing model (ELMM), which authorizes endmembers to vary pixelwise according to scaling factors, or local spectral unmixing (LSU) where the unmixing process is conducted locally within the image. In the latter case however, results are difficult to interpret at the whole image scale. In this work, we propose to analyze the local results of LSU within the ELMM framework, and show that it not only allows to reconstruct global endmembers and fractional abundances from the local ones, but it also gives access to the scaling factors advocated by the ELMM. Results obtained on a real hyperspectral image confirm the soundness of the proposed methodology.
{"title":"From local to global unmixing of hyperspectral images to reveal spectral variability","authors":"G. Tochon, Lucas Drumetz, M. Veganzones, M. Mura, J. Chanussot","doi":"10.1109/WHISPERS.2016.8071730","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071730","url":null,"abstract":"The linear mixing model is widely assumed when unmixing hyperspectral images, but it cannot account for endmembers spectral variability. Thus, several workarounds have arisen in the hyperspectral unmixing literature, such as the extended linear mixing model (ELMM), which authorizes endmembers to vary pixelwise according to scaling factors, or local spectral unmixing (LSU) where the unmixing process is conducted locally within the image. In the latter case however, results are difficult to interpret at the whole image scale. In this work, we propose to analyze the local results of LSU within the ELMM framework, and show that it not only allows to reconstruct global endmembers and fractional abundances from the local ones, but it also gives access to the scaling factors advocated by the ELMM. Results obtained on a real hyperspectral image confirm the soundness of the proposed methodology.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134440312","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 : 2016-08-21DOI: 10.1109/WHISPERS.2016.8071723
M. Veganzones, S. Douté, Jeremy E. Cohen, R. C. Farias, J. Chanussot, P. Comon
The Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) sensor aboard the Mars Reconnaissance Orbiter takes hyperspectral multiangle acquisitions of Martian surface from the top of the atmosphere (TOA) on visible and infrared wavelengths. The Multiangle Approach for Retrieval of Surface Reflectance from CRISM Observations (MARS-ReCO) defined an innovative TOA radiance model and inversion scheme aimed at correcting for aerosols effects taking advantage of the near-simultaneous multiangle CRISM observations. Here, we aim to provide validation evidence of MARS-ReCO by unmixing the estimated multiangle bidirectional reflectance (BRF) from highly reflective and anisotropic icy surfaces at high latitudes with grazing illumination, using a nonnegative CP decomposition. Obtained results are in accordance with other complementary studies, which compose a collaboration effort to validate MARS-ReCO through the cross-validation of different techniques in the absence of ground truth.
{"title":"Nonnegative CP decomposition of multiangle hyperspectral data: A case study on CRISM observations of Martian ICY surface","authors":"M. Veganzones, S. Douté, Jeremy E. Cohen, R. C. Farias, J. Chanussot, P. Comon","doi":"10.1109/WHISPERS.2016.8071723","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071723","url":null,"abstract":"The Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) sensor aboard the Mars Reconnaissance Orbiter takes hyperspectral multiangle acquisitions of Martian surface from the top of the atmosphere (TOA) on visible and infrared wavelengths. The Multiangle Approach for Retrieval of Surface Reflectance from CRISM Observations (MARS-ReCO) defined an innovative TOA radiance model and inversion scheme aimed at correcting for aerosols effects taking advantage of the near-simultaneous multiangle CRISM observations. Here, we aim to provide validation evidence of MARS-ReCO by unmixing the estimated multiangle bidirectional reflectance (BRF) from highly reflective and anisotropic icy surfaces at high latitudes with grazing illumination, using a nonnegative CP decomposition. Obtained results are in accordance with other complementary studies, which compose a collaboration effort to validate MARS-ReCO through the cross-validation of different techniques in the absence of ground truth.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127709393","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.2016.8071780
Nan Wang, Lifu Zhang, Y. Cen, Q. Tong
In this paper, the ground truth information is introduced to improve the accuracy of hyperspectral unmixing based on nonnegative matrix factorization. Specifically, the partial known endmembers which could be surveyed is introduced in NMF model. The relationship between the known and unknown endmembers are explored. The distance function is designed to describe the relationship and combined with NMF model. In this way, the new proposed NMF approach, called PENMF, could use the known endmembers to help estimating the unknown endmembers, so that accurate and robust results can be obtained. The proposed algorithm was compared with NMFupk, which also considered partial known endmembers, using extensive synthetic data and real hyperspectral data. The experiments show that the proposed algorithm can give a better performance.
{"title":"A non-negative matrix factorization approach for hyperspectral unmixing with partial known endmembers","authors":"Nan Wang, Lifu Zhang, Y. Cen, Q. Tong","doi":"10.1109/WHISPERS.2016.8071780","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071780","url":null,"abstract":"In this paper, the ground truth information is introduced to improve the accuracy of hyperspectral unmixing based on nonnegative matrix factorization. Specifically, the partial known endmembers which could be surveyed is introduced in NMF model. The relationship between the known and unknown endmembers are explored. The distance function is designed to describe the relationship and combined with NMF model. In this way, the new proposed NMF approach, called PENMF, could use the known endmembers to help estimating the unknown endmembers, so that accurate and robust results can be obtained. The proposed algorithm was compared with NMFupk, which also considered partial known endmembers, using extensive synthetic data and real hyperspectral data. The experiments show that the proposed algorithm can give a better performance.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"179 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":"116649414","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.2016.8071667
Jacob A. Martin, K. Gross
A method for retrieving index of refraction from polarimetric hyperspectral imagery (P-HSI) has been developed using a model to describe the spectral variation of the index. Index of refraction is modeled in one of two ways to reduce the number of parameters in the problem. Additionally, MODTRAN is used to model the atmosphere, further reducing the number of variables and enabling an overdetermined solution to be found. Results from simulated data of a SiC target at 25°C, with realistic noise levels, show index is retrieved to within 0.0116 for the real component and 0.034 for the imaginary component. This also shows that the atmospheric downwelling can be accurately retrieved even without a priori knowledge.
{"title":"Estimating index of refraction, surface temperature, and downwelling radiance using polarimetric-hyperspectral imagery (P-HSI)","authors":"Jacob A. Martin, K. Gross","doi":"10.1109/WHISPERS.2016.8071667","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071667","url":null,"abstract":"A method for retrieving index of refraction from polarimetric hyperspectral imagery (P-HSI) has been developed using a model to describe the spectral variation of the index. Index of refraction is modeled in one of two ways to reduce the number of parameters in the problem. Additionally, MODTRAN is used to model the atmosphere, further reducing the number of variables and enabling an overdetermined solution to be found. Results from simulated data of a SiC target at 25°C, with realistic noise levels, show index is retrieved to within 0.0116 for the real component and 0.034 for the imaginary component. This also shows that the atmospheric downwelling can be accurately retrieved even without a priori knowledge.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"29 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":"127129610","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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