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Fusion of Hyperspectral and LiDAR Data Using Morphological Attribute Profiles

机译:使用形态学属性谱融合高光谱和LiDAR数据

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In this paper we investigate the application of Morphological Attribute Profiles to both hyperspectral and LiDAR data to fuse spectral, spatial and elevation data for classification purposes. While hyperspectral data provides a wealth of spectral information, multi-return LiDAR data provides geometrical information on the elevation and the structure of the objects on the ground as well as a measure of their laser cross section. Therefore, hyperspectral and LiDAR data are complementary information sources and potentially their joint analysis can improve classification accuracies. Morphological Profiles (MPs) and Morphological Attribute Profiles (MAPs) have been successfully used as tools to combine spectral and spatial information for classification of remote sensing data. MPs and MAPs can also be used with the LiDAR data to reduce the irregularities in the LiDAR measurements which are inherent with the sampling strategy used in the acquisition process.. Experiments carried out on hyperspectral and LiDAR data acquired on a urban area of the city of Trento (Italy) point out the effectiveness of MAPs for the classification process.
机译:在本文中,我们研究了形态属性谱在高光谱和LiDAR数据中的应用,以融合光谱,空间和海拔数据进行分类。高光谱数据可提供大量光谱信息,而多返回LiDAR数据可提供有关地面上物体的高程和结构以及其激光横截面尺寸的几何信息。因此,高光谱和LiDAR数据是互补的信息源,它们的联合分析可能会提高分类的准确性。形态特征(MPs)和形态属性特征(MAPs)已成功用作组合光谱和空间信息以进行遥感数据分类的工具。 MP和MAP也可以与LiDAR数据一起使用,以减少LiDAR测量中的不规则性,这是采集过程中使用的采样策略所固有的。对在城市市区采集的高光谱和LiDAR数据进行的实验特伦托(意大利)指出了MAP在分类过程中的有效性。

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