Class3Dp: A supervised classifier of vegetation species from point clouds

Abstract

Recognizing the species composition of an ecosystem is essential for conservation and land management. This study presents the software Class3Dp, a supervised classifier of vegetation species for coloured point clouds. Class3Dp is run through a graphical user interface (GUI) that allows for the selection of training samples from RGB or MS (multispectral) clouds and their classification based on geometric, spectral and neighbourhood features, along with different machine learning methods, obtaining the point cloud classified according to the classes (species) introduced. A case study is shown where a classification of ground and vegetation is carried out, obtaining an overall accuracy (OA) of 0.94 in the RGB classification and 0.95 in the MS. Points classified as vegetation were re-classified in the species Anthyllis cytisoides L., Chamaerops humilis L., Cistus monspeliensis L., Pistacia lentiscus L. and Quercus coccifera L., obtaining an OA of 0.86 in the RGB classification and 0.87 in the MS.