Optimal Band Configuration for the Roof Surface Characterization Using Hyperspectral and LiDAR Imaging
Imaging spectroscopy in the remote sensing is an ever emerging platform that has offered the hyperspectral imaging (HSI) which delivers the Earth’s object information in hundreds of bands. HSI integrates conventional imaging with spectroscopy to get rich spectral and spatial features of the object....
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Journal of Spectroscopy |
| Online Access: | http://dx.doi.org/10.1155/2018/6460518 |
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| author | Prakash Nimbalkar Anna Jarocinska Bogdan Zagajewski |
| author_facet | Prakash Nimbalkar Anna Jarocinska Bogdan Zagajewski |
| author_sort | Prakash Nimbalkar |
| collection | DOAJ |
| description | Imaging spectroscopy in the remote sensing is an ever emerging platform that has offered the hyperspectral imaging (HSI) which delivers the Earth’s object information in hundreds of bands. HSI integrates conventional imaging with spectroscopy to get rich spectral and spatial features of the object. However, the challenges associated with HSI are its huge dimensionality and data redundancy that requests huge space, complex computations, and lengthier processing time. Therefore, this study aims to find the optimal bands to characterize the roof surfaces using supervised classifiers. To deal with high dimensionality of hyperspectral data, this study assesses the band selection method over data transformation methods. This study provides the comparison between data reduction methods and used classifiers. The height information from LiDAR was used to characterize urban roofs above the height of 2.5 meters. The optimal bands were investigated using supervised classifiers such as artificial neural network (ANN), support vector machine (SVM), and spectral angle mapper (SAM) by comparing accuracies. The classification result shows that ANN and SVM classifiers outperform whereas SAM performed poorly in roof characterization. The band selection method worked efficiently than the transformation methods. The classification algorithm successfully identifies the optimum bands with significant accuracy. |
| format | Article |
| id | doaj-art-6fb61498ac104ef8b390a6460dbbd4df |
| institution | OA Journals |
| issn | 2314-4920 2314-4939 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Spectroscopy |
| spelling | doaj-art-6fb61498ac104ef8b390a6460dbbd4df2025-08-20T02:06:13ZengWileyJournal of Spectroscopy2314-49202314-49392018-01-01201810.1155/2018/64605186460518Optimal Band Configuration for the Roof Surface Characterization Using Hyperspectral and LiDAR ImagingPrakash Nimbalkar0Anna Jarocinska1Bogdan Zagajewski2Department of Geoinformatics, Cartography and Remote Sensing, Faculty of Geography and Regional Studies, University of Warsaw, Krakowskie Przedmiescie 30, 00-927 Warsaw, PolandDepartment of Geoinformatics, Cartography and Remote Sensing, Faculty of Geography and Regional Studies, University of Warsaw, Krakowskie Przedmiescie 30, 00-927 Warsaw, PolandDepartment of Geoinformatics, Cartography and Remote Sensing, Faculty of Geography and Regional Studies, University of Warsaw, Krakowskie Przedmiescie 30, 00-927 Warsaw, PolandImaging spectroscopy in the remote sensing is an ever emerging platform that has offered the hyperspectral imaging (HSI) which delivers the Earth’s object information in hundreds of bands. HSI integrates conventional imaging with spectroscopy to get rich spectral and spatial features of the object. However, the challenges associated with HSI are its huge dimensionality and data redundancy that requests huge space, complex computations, and lengthier processing time. Therefore, this study aims to find the optimal bands to characterize the roof surfaces using supervised classifiers. To deal with high dimensionality of hyperspectral data, this study assesses the band selection method over data transformation methods. This study provides the comparison between data reduction methods and used classifiers. The height information from LiDAR was used to characterize urban roofs above the height of 2.5 meters. The optimal bands were investigated using supervised classifiers such as artificial neural network (ANN), support vector machine (SVM), and spectral angle mapper (SAM) by comparing accuracies. The classification result shows that ANN and SVM classifiers outperform whereas SAM performed poorly in roof characterization. The band selection method worked efficiently than the transformation methods. The classification algorithm successfully identifies the optimum bands with significant accuracy.http://dx.doi.org/10.1155/2018/6460518 |
| spellingShingle | Prakash Nimbalkar Anna Jarocinska Bogdan Zagajewski Optimal Band Configuration for the Roof Surface Characterization Using Hyperspectral and LiDAR Imaging Journal of Spectroscopy |
| title | Optimal Band Configuration for the Roof Surface Characterization Using Hyperspectral and LiDAR Imaging |
| title_full | Optimal Band Configuration for the Roof Surface Characterization Using Hyperspectral and LiDAR Imaging |
| title_fullStr | Optimal Band Configuration for the Roof Surface Characterization Using Hyperspectral and LiDAR Imaging |
| title_full_unstemmed | Optimal Band Configuration for the Roof Surface Characterization Using Hyperspectral and LiDAR Imaging |
| title_short | Optimal Band Configuration for the Roof Surface Characterization Using Hyperspectral and LiDAR Imaging |
| title_sort | optimal band configuration for the roof surface characterization using hyperspectral and lidar imaging |
| url | http://dx.doi.org/10.1155/2018/6460518 |
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