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Aerial Semantic Mapping for Precision Agriculture using Multispectral Imagery
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Nowadays constant technological evolution cover several necessities and daily tasks in our
society. In particular, drones usage, given its wide vision to capture the terrain surface
images, allows to collect large amounts of information with high efficiency, performance
and accuracy.
This master dissertation’s main purpose is the analysis, classification and respective
mapping of different terrain types and characteristics, using multispectral imagery.
Solar radiation flow reflected on the surface is captured by the used multispectral
camera’s different lenses (RedEdge-M, created by Micasense). Each one of these five
lenses is able to capture different colour spectrums (i.e. Blue, Green, Red, Near-Infrared
and RedEdge). It is possible to analyse the various spectrum indices from the collected
imagery, according to the fusion of different combinations between coloured bands (e.g.
NDVI, ENDVI, RDVI. . . ).
This project engages a ROS (Robot Operating System) framework development, capable
of correcting different captured imagery and, hence, calculating the implemented
spectral indices. Several parametrizations of terrain analysis were carried throughout the
project, and this information was represented in semantic maps by layers (e.g. vegetation,
water, soil, rocks).
The obtained experimental results were validated in the scope of several projects
incorporated in PDR2020, with success rates between 70% and 90%.
This framework can have multiple technical applications, not only in Precision Agriculture,
but also in vehicles autonomous navigation and multi-robot cooperation.
Descrição
Palavras-chave
Precision Agriculture Layered Map Semantic Map Imagery Stitching Unmanned Aerial Vehicle (UAV) Multispectral Imagery