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Photogrammetry is one of the widest techniques used to monitor terrain changes which occur due to natural process and geological natural risk zones. In order to carry out terrain monitoring, it is necessary to eliminate all the non-ground elements. One of the most variable elements in this monitoring is the presence of vegetation, which obscures the ground and can significantly mislead any multitemporal analysis to detect terrain changes. Therefore, the focus of this paper is about how best to filter the vegetation to attain an accurate reading of the terrain. There are several methods to filter it based on colourising an excessive greenness vegetation index or non-visible channels as the IR in the well-known index NVDI. However, achieving this kind of information is not always possible because its high cost. Instead this channel we can add new information using the HSV colour space obtained from the RGB information. In this paper, we propose a double possibility, on one hand work with RGB+HSV for a supervised segmentation on images. On the other, to use excessive greenness vegetation indices and RGB+HSV for the segmentation of point clouds. The results shown that the use of additional channels HSV can significantly improve the segmentation in both studies, and therefore render a much more accurate assessment of the underlying terrain.

Vegetation Filtering using Colour for Monitoring Applications from Photogrammetric Data