Pub Date : 2016-08-01DOI: 10.1109/WHISPERS.2016.8071665
Ø. Rudjord, Ø. Trier
This paper presents a new method to discriminate between spruce, pine and birch, which are the dominating tree species in Norwegian forests. For this purpose, simultaneously acquired airborne laser scanning (ALS) and hyperspectral data are used. The laser scanning data was used to mask pixels with low or no vegetation in the hyperspectral data. From the species-specific spectra, three wavelengths were identified for species discrimination: 544 nm (green), 674 nm (red) and 710 nm (red edge). A decision tree-based pixel classification method obtained 83–86% correct classification. We plan a field revisit to include misclassified trees in an extended in situ data set, and then to re-calibrate and re-run the classifier. There is also potential for improvement by using individual tree crown delineation. Further, the vegetation height could potentially be used to improve classification.
{"title":"Tree species classification with hyperspectral imaging and lidar","authors":"Ø. Rudjord, Ø. Trier","doi":"10.1109/WHISPERS.2016.8071665","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071665","url":null,"abstract":"This paper presents a new method to discriminate between spruce, pine and birch, which are the dominating tree species in Norwegian forests. For this purpose, simultaneously acquired airborne laser scanning (ALS) and hyperspectral data are used. The laser scanning data was used to mask pixels with low or no vegetation in the hyperspectral data. From the species-specific spectra, three wavelengths were identified for species discrimination: 544 nm (green), 674 nm (red) and 710 nm (red edge). A decision tree-based pixel classification method obtained 83–86% correct classification. We plan a field revisit to include misclassified trees in an extended in situ data set, and then to re-calibrate and re-run the classifier. There is also potential for improvement by using individual tree crown delineation. Further, the vegetation height could potentially be used to improve classification.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"59 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":"129725541","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.2016.8071724
Zhou Zhang, M. Crawford
Utilization of both spectral and spatial features for hyperspectral image classification can often improve the classification accuracy. However, the high dimensionality of the input data and the limited number of labeled samples are two key challenges for supervised techniques. In this paper, a regularized multi-metric learning approach is proposed for feature extraction and combined with active learning (AL) to deal with these issues simultaneously. In particular, distinct metrics are assigned to different types of features and then learned jointly. Also, the proposed regularizer helps to avoid overfitting at early AL stages by taking advantage of the unlabeled data information. Finally, multiple feature are projected into a common feature space, in which a new batch-mode AL strategy combining uncertainty and diversity is performed in conjunction with k-nearest neighbor (ANN) classification to enrich the set of labeled samples. Experiments on a benchmark hyperspectral dataset illustrate the effectiveness of the proposed framework.
{"title":"A regularized multi-metric active learning framework for hyperspectral image classification","authors":"Zhou Zhang, M. Crawford","doi":"10.1109/WHISPERS.2016.8071724","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071724","url":null,"abstract":"Utilization of both spectral and spatial features for hyperspectral image classification can often improve the classification accuracy. However, the high dimensionality of the input data and the limited number of labeled samples are two key challenges for supervised techniques. In this paper, a regularized multi-metric learning approach is proposed for feature extraction and combined with active learning (AL) to deal with these issues simultaneously. In particular, distinct metrics are assigned to different types of features and then learned jointly. Also, the proposed regularizer helps to avoid overfitting at early AL stages by taking advantage of the unlabeled data information. Finally, multiple feature are projected into a common feature space, in which a new batch-mode AL strategy combining uncertainty and diversity is performed in conjunction with k-nearest neighbor (ANN) classification to enrich the set of labeled samples. Experiments on a benchmark hyperspectral dataset illustrate the effectiveness of the proposed framework.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"31 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":"129939541","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.2016.8071711
D. Tratt, K. Buckland, E. Keim, P. Johnson
The advantages of airborne hyperspectral longwave-infrared imaging for emissions monitoring are described in the context of urban-industrial environments. These benefits are illustrated by means of several case studies.
{"title":"Urban-industrial emissions monitoring with airborne longwave-infrared hyperspectral imaging","authors":"D. Tratt, K. Buckland, E. Keim, P. Johnson","doi":"10.1109/WHISPERS.2016.8071711","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071711","url":null,"abstract":"The advantages of airborne hyperspectral longwave-infrared imaging for emissions monitoring are described in the context of urban-industrial environments. These benefits are illustrated by means of several case studies.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"58 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":"132636745","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.2016.8071776
A. Marinoni, J. Plaza, A. Plaza, P. Gamba
In order to provide a careful description of the interactions among endmembers in hyperspectral images, a new method for adaptive design of mixture models for hyperspectral unmixing is introduced. Specifically, the proposed approach relies on exploiting geometrical features of hyperspectral signatures in terms of nonorthogonal projections onto the space induced by the endmembers' spectra. Then, an iterative process is deployed in order to understand the order of local nonlinearity that is displayed by each endmember over every pixel. Experimental results show that the proposed approach is actually able to retrieve thorough information on the nature of the nonlinear effects over the image while providing excellent performance in reconstructing the given dataset.
{"title":"An iterative enhancement of higher order nonlinear mixture model for accurate hyperspectral unmixing","authors":"A. Marinoni, J. Plaza, A. Plaza, P. Gamba","doi":"10.1109/WHISPERS.2016.8071776","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071776","url":null,"abstract":"In order to provide a careful description of the interactions among endmembers in hyperspectral images, a new method for adaptive design of mixture models for hyperspectral unmixing is introduced. Specifically, the proposed approach relies on exploiting geometrical features of hyperspectral signatures in terms of nonorthogonal projections onto the space induced by the endmembers' spectra. Then, an iterative process is deployed in order to understand the order of local nonlinearity that is displayed by each endmember over every pixel. Experimental results show that the proposed approach is actually able to retrieve thorough information on the nature of the nonlinear effects over the image while providing excellent performance in reconstructing the given dataset.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"8 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":"132111320","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.2016.8071705
S. Bauer, F. P. León
In the last couple of years, methods based on nonnegative matrix factorization (NMF) have been used for spectral unmixing of hyperspectral images. We propose a meta-method based on image pyramids for the acceleration of the unmixing calculation. Starting the factorization from a spatially coarse level, neighboring pixel spectra are averaged and considered as new pixel spectra. In the subsequent iterations, the resolution is increased step by step, which means that the previous lower resolution outcomes can be regarded as close-to-optimal starting points for the higher resolution iterations. By performing many steps on lower resolution levels, only few steps have to be calculated on the original size data. We will demonstrate the application of the new method, showing that for both spatial and spectral extensions of NMF, the proposed method in most cases leads to equal objective function values in less time. The unmixing calculation can be accelerated up to several times. Due to the fact that the objective functions of different NMF algorithms exhibit more or less local minima, not all NMF-based unmixing algorithms are equally well-suited for the application of the proposed method.
{"title":"Using image pyramids for the acceleration of spectral unmixing based on nonnegative matrix factorization","authors":"S. Bauer, F. P. León","doi":"10.1109/WHISPERS.2016.8071705","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071705","url":null,"abstract":"In the last couple of years, methods based on nonnegative matrix factorization (NMF) have been used for spectral unmixing of hyperspectral images. We propose a meta-method based on image pyramids for the acceleration of the unmixing calculation. Starting the factorization from a spatially coarse level, neighboring pixel spectra are averaged and considered as new pixel spectra. In the subsequent iterations, the resolution is increased step by step, which means that the previous lower resolution outcomes can be regarded as close-to-optimal starting points for the higher resolution iterations. By performing many steps on lower resolution levels, only few steps have to be calculated on the original size data. We will demonstrate the application of the new method, showing that for both spatial and spectral extensions of NMF, the proposed method in most cases leads to equal objective function values in less time. The unmixing calculation can be accelerated up to several times. Due to the fact that the objective functions of different NMF algorithms exhibit more or less local minima, not all NMF-based unmixing algorithms are equally well-suited for the application of the proposed method.","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":"130073505","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.2016.8071709
H. Soydan, A. Koz, H. S. Düzgün, Aydin Alatan
Depending on the ground sampling distance of a remote sensor, a pixel of a spectral data cube is represented as a combination of the reflected signals of the materials which constitutes the observed pixel. Hyperspectral unmixing algorithms model the pixel of a data cube to determine and extract the spectral signatures of its components, namely endmembers, with their corresponding abundance fractions. This study first reviews the interaction and mitigation mechanisms of heavy metals with carbon content in soil, specifically due to coal mining activities and thermal plants. Such mechanism is then investigated with hyperspectral unmixing techniques by producing total carbon maps for an abandoned coal mine site. The utilized data for the study area is obtained on August 2013 with multispectral Worldview-2 satellite sensor. The acquired image is orthorectified and atmospherically corrected for radiance to reflectance conversion prior to the analysis. The soil samples are mainly collected from the problematic regions in terms of soil pollution. The samples are analyzed with LECO TrueSpec CHN_S device to measure total carbon levels, which are employed as ground truth to assess the performance of unmixing algorithms. The resulting abundance maps for carbon content are found to have a high compatibility with each other and the ground truth data, which effectively point out the regions of high carbon content.
{"title":"Total carbon mapping with hyperspectral unmixing techniques","authors":"H. Soydan, A. Koz, H. S. Düzgün, Aydin Alatan","doi":"10.1109/WHISPERS.2016.8071709","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071709","url":null,"abstract":"Depending on the ground sampling distance of a remote sensor, a pixel of a spectral data cube is represented as a combination of the reflected signals of the materials which constitutes the observed pixel. Hyperspectral unmixing algorithms model the pixel of a data cube to determine and extract the spectral signatures of its components, namely endmembers, with their corresponding abundance fractions. This study first reviews the interaction and mitigation mechanisms of heavy metals with carbon content in soil, specifically due to coal mining activities and thermal plants. Such mechanism is then investigated with hyperspectral unmixing techniques by producing total carbon maps for an abandoned coal mine site. The utilized data for the study area is obtained on August 2013 with multispectral Worldview-2 satellite sensor. The acquired image is orthorectified and atmospherically corrected for radiance to reflectance conversion prior to the analysis. The soil samples are mainly collected from the problematic regions in terms of soil pollution. The samples are analyzed with LECO TrueSpec CHN_S device to measure total carbon levels, which are employed as ground truth to assess the performance of unmixing algorithms. The resulting abundance maps for carbon content are found to have a high compatibility with each other and the ground truth data, which effectively point out the regions of high carbon content.","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":"131330895","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.2016.8071792
I. Colkesen, T. Kavzoglu
Hyperspectral images provide important information for addressing complex classification problems required for a detailed characterization of spectral behavior of the target objects. Classification of such datasets into meaningful land use and land cover classes (LULC) has been the most concentrated topic in remote sensing arena. Rotation forest (RotFor), a new ensemble learning method, has been recently proposed as an alternative to conventional classifiers in the context of multispectral and hyperspectral image classification. In this study, the use of RotFor was investigated for the classification of hyperspectral imagery, specifically an AVIRIS image data. Support vector machine (SVM) was also used as a benchmark classifier. In order to select the best contributing bands of AVIRIS, support vector machine-recursive feature elimination (SVM-RFE) approach was applied. Results of this study showed that RotFor algorithm provided more accurate classification results than the SVM classifier with the use of smaller size data sets selected by SVM-RFE. Based on the Wilcoxon's signed-rank test, the performance difference between RotFor and SVM was found to be statistically significant.
{"title":"Performance evaluation of rotation forest for svm-based recursive feature elimination using hyperspectral imagery","authors":"I. Colkesen, T. Kavzoglu","doi":"10.1109/WHISPERS.2016.8071792","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071792","url":null,"abstract":"Hyperspectral images provide important information for addressing complex classification problems required for a detailed characterization of spectral behavior of the target objects. Classification of such datasets into meaningful land use and land cover classes (LULC) has been the most concentrated topic in remote sensing arena. Rotation forest (RotFor), a new ensemble learning method, has been recently proposed as an alternative to conventional classifiers in the context of multispectral and hyperspectral image classification. In this study, the use of RotFor was investigated for the classification of hyperspectral imagery, specifically an AVIRIS image data. Support vector machine (SVM) was also used as a benchmark classifier. In order to select the best contributing bands of AVIRIS, support vector machine-recursive feature elimination (SVM-RFE) approach was applied. Results of this study showed that RotFor algorithm provided more accurate classification results than the SVM classifier with the use of smaller size data sets selected by SVM-RFE. Based on the Wilcoxon's signed-rank test, the performance difference between RotFor and SVM was found to be statistically significant.","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":"115047641","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.2016.8071796
V. Menon, Q. Du, J. Fowler
Nonnegative least squares, a state-of-the-art approach to endmember abundance estimation in the hyperspectral-unmixing problem, is coupled with random projection employed for dimensionality reduction. Both Hadamard- and Gaussian-based projections are considered. Experimental results reveal that random projections can significantly reduce data volume without detrimentally affecting the accuracy of the abundance estimation, with the Hadamard-based approach slightly outperforming its Gaussian counterpart.
{"title":"Random-projection-based nonnegative least squares for hyperspectral image unmixing","authors":"V. Menon, Q. Du, J. Fowler","doi":"10.1109/WHISPERS.2016.8071796","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071796","url":null,"abstract":"Nonnegative least squares, a state-of-the-art approach to endmember abundance estimation in the hyperspectral-unmixing problem, is coupled with random projection employed for dimensionality reduction. Both Hadamard- and Gaussian-based projections are considered. Experimental results reveal that random projections can significantly reduce data volume without detrimentally affecting the accuracy of the abundance estimation, with the Hadamard-based approach slightly outperforming its Gaussian counterpart.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"156 10 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":"128732985","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.2016.8071765
H. Aggarwal, A. Majumdar
This work proposes a hyperspectral unmixing technique based on sparse filtering approach. The proposed method exploits the sparsity of feature distribution rather than modeling the data distribution. The proposed sparse filtering based unmixing procedure is essentially parameter-free, and the only parameter is to find the number of endmembers to be extracted. This approach is a blind unmixing approach because it does not require prior knowledge of endmember matrix. Experimental results on two real hyperspectral datasets demonstrate that the proposed sparse filtering procedure provide better abundance maps compared to nonnegative matrix factorization based approach.
{"title":"Sparse filtering based hyperspectral unmixing","authors":"H. Aggarwal, A. Majumdar","doi":"10.1109/WHISPERS.2016.8071765","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071765","url":null,"abstract":"This work proposes a hyperspectral unmixing technique based on sparse filtering approach. The proposed method exploits the sparsity of feature distribution rather than modeling the data distribution. The proposed sparse filtering based unmixing procedure is essentially parameter-free, and the only parameter is to find the number of endmembers to be extracted. This approach is a blind unmixing approach because it does not require prior knowledge of endmember matrix. Experimental results on two real hyperspectral datasets demonstrate that the proposed sparse filtering procedure provide better abundance maps compared to nonnegative matrix factorization based approach.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"49 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":"134646661","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.2016.8071748
Mengmeng Zhang, Wei Li, Q. Du
Representation-based classification has gained great interest recently. In this paper, we present a novel joint low rank and sparse representation-based classification (JLRSRC) method for hyperspectral imagery. For a testing set, JLRSRC seeks weight coefficients to represent a testing pixel as linear combination of atoms in an over-complete dictionary. Since the low rank model is capable of preserving global data structures of data while sparsity can select the discriminative neighbors in the feature space, the resulting representation is both representative and discriminative. Experimental results demonstrate the effectiveness of the proposed JLRSRC when compared with the traditional counterparts.
{"title":"Joint low rank and sparse representation-based hyperspectral image classification","authors":"Mengmeng Zhang, Wei Li, Q. Du","doi":"10.1109/WHISPERS.2016.8071748","DOIUrl":"https://doi.org/10.1109/WHISPERS.2016.8071748","url":null,"abstract":"Representation-based classification has gained great interest recently. In this paper, we present a novel joint low rank and sparse representation-based classification (JLRSRC) method for hyperspectral imagery. For a testing set, JLRSRC seeks weight coefficients to represent a testing pixel as linear combination of atoms in an over-complete dictionary. Since the low rank model is capable of preserving global data structures of data while sparsity can select the discriminative neighbors in the feature space, the resulting representation is both representative and discriminative. Experimental results demonstrate the effectiveness of the proposed JLRSRC when compared with the traditional counterparts.","PeriodicalId":369281,"journal":{"name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","volume":"19 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":"125445503","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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