The Combined Use of UAV-Based RGB and DEM Images for the Detection and Delineation of Orange Tree Crowns with Mask R-CNN: An Approach of Labeling and Unified Framework

Felipe Lucena (), Fabio Marcelo Breunig and Hermann Kux
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Felipe Lucena: Divisão de Sensoriamento Remoto, Instituto Nacional de Pesquisas Espaciais (INPE), Av. dos Astronautas, 1758—Jardim da Granja, São José dos Campos CEP 12227-010, SP, Brazil
Fabio Marcelo Breunig: Departamento de Engenharia Florestal, Universidade Federal de Santa Maria (UFSM)—Campus Frederico Westphalen, linha Sete de Setembro s/n, UFSM, Frederico Westphalen CEP 98400-000, RS, Brazil
Hermann Kux: Divisão de Sensoriamento Remoto, Instituto Nacional de Pesquisas Espaciais (INPE), Av. dos Astronautas, 1758—Jardim da Granja, São José dos Campos CEP 12227-010, SP, Brazil

Future Internet, 2022, vol. 14, issue 10, 1-20

Abstract: In this study, we used images obtained by Unmanned Aerial Vehicles (UAV) and an instance segmentation model based on deep learning (Mask R-CNN) to evaluate the ability to detect and delineate canopies in high density orange plantations. The main objective of the work was to evaluate the improvement acquired by the segmentation model when integrating the Canopy Height Model (CHM) as a fourth band to the images. Two models were evaluated, one with RGB images and the other with RGB + CHM images, and the results indicated that the model with combined images presents better results (overall accuracy from 90.42% to 97.01%). In addition to the comparison, this work suggests a more efficient ground truth mapping method and proposes a methodology for mosaicking the results by Mask R-CNN on remotely sensed images.

Keywords: precision agriculture; instance segmentation; tree detection; tree delineation; UAV-based images; Mask R-CNN (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2022
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