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Journal of Spectroscopy
Volume 2018, Article ID 6460518, 15 pages
Research Article

Optimal Band Configuration for the Roof Surface Characterization Using Hyperspectral and LiDAR Imaging

Department of Geoinformatics, Cartography and Remote Sensing, Faculty of Geography and Regional Studies, University of Warsaw, Krakowskie Przedmiescie 30, 00-927 Warsaw, Poland

Correspondence should be addressed to Prakash Nimbalkar; moc.liamg@21panmhsakarp

Received 9 October 2017; Accepted 14 February 2018; Published 18 April 2018

Academic Editor: Jianxi Zhu

Copyright © 2018 Prakash Nimbalkar et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.