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首页> 外文期刊>Journal of spectroscopy >Optimal Band Configuration for the Roof Surface Characterization Using Hyperspectral and LiDAR Imaging
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Optimal Band Configuration for the Roof Surface Characterization Using Hyperspectral and LiDAR Imaging

机译：使用高光谱和LiDAR成像进行屋顶表面表征的最佳波段配置

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摘要

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.
机译：遥感影像光谱学是一个新兴的平台，它提供了高光谱成像（HSI），可在数百个波段中传递地球的物体信息。 HSI将常规成像与光谱学结合在一起，以获得物体的丰富光谱和空间特征。但是，与HSI相关的挑战是其巨大的尺寸和数据冗余，这需要巨大的空间，复杂的计算和更长的处理时间。因此，本研究旨在使用监督分类器找到表征屋顶表面的最佳波段。为了处理高光谱数据的高维性，本研究评估了波段选择方法而不是数据转换方法。这项研究提供了数据约简方法和使用的分类器之间的比较。 LiDAR的高度信息用于表征2.5米以上的城市屋顶。通过比较精确度，使用监督分类器（例如人工神经网络（ANN），支持向量机（SVM）和光谱角映射器（SAM））研究了最佳频段。分类结果表明，ANN和SVM分类器的性能优于SAM，但在屋顶特征描述方面表现不佳。频带选择方法比变换方法有效地工作。该分类算法以很高的准确度成功地确定了最佳频段。

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