Abstract
Abstract. Precise point cloud classification can enhance lidar performance in various applications, such as land cover mapping, forestry management and autonomous driving. The development of multispectral lidar improves classification performance with rich spectral information. However, the employment of spectral information for classification is still underdeveloped. Therefore, we proposed a spectrally improved classification method for multispectral LiDAR. We conducted spectral improvement in two aspects: (1) we improved the eigenentropy-based neighbourhood selection by spectral angle match (SAM) to reform the more reliable neighbour; (2) we utilized both geometric and spectral features and compare the contributions of these features. A three-wavelength multispectral lidar and a complex indoor experimental scene were used for demonstration. The results indicate the effectiveness of our proposed spectrally improved method and the promising potential of spectral information on lidar classification.
Highlights
Since the invention of lidar, lidar point cloud classification has attracted considerable attention in the field of remote sensing (Vosselman and Maas 2010)
The experimental targets include a piece of black paper, a Sansevieria trifasciata plant, two ceramic flowerpots, two
This paper studied a spectrally improved classification method for multispectral lidar
Summary
Since the invention of lidar, lidar point cloud classification has attracted considerable attention in the field of remote sensing (Vosselman and Maas 2010). Precise point cloud classification can enhance lidar performance in various applications, such as land cover mapping, forestry management and autonomous driving. There are many traditional single-wavelength LiDAR data classification studies, which are limited by the lack of spectral information. The spectral information from passive technologies could remedy this limitation, but the data fusion need to deal with the problem of the varying illumination conditions (Malik et al 2007) and the registration problem (Zhang et al 2015). The development of multispectral lidar successfully obtains spectral and spatial information simultaneously (Hakala et al 2012; Wei et al 2012; Woodhouse et al 2011). The multispectral lidar has been gradually effective and practical (Chen et al 2019c; Ren et al 2018)
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