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Article

How to Build a 2D and 3D Aerial Multispectral Map?—All Steps Deeply Explained

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NOVA School of Science and Technology, 2829-516 Caparica, Portugal
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Cognitive and People-Centric Computing Labs (COPELABS), Universidade Lusófona de Humanidades e Tecnologias, Campo Grande 376, 1749-024 Lisboa, Portugal
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UAV4GEO, St. Petersburg, FL 33713, USA
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Centre of Technology and Systems, UNINOVA, 2829-516 Caparica, Portugal
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PDMFC—Projecto Desenvolvimento Manutenção Formação e Consultadoria, 1300-609 Lisbon, Portugal
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BEV—Beyond Vision, 2610-161 Ílhavo, Portugal
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Electrical and Computing Engineering Department, FEUP, University of Porto, 4099-002 Porto, Portugal
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LASIGE, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal
*
Author to whom correspondence should be addressed.
Academic Editors: Guido D’Urso, Lorenzo Comba, Jordi Llorens and Alessandro Biglia
Remote Sens. 2021, 13(16), 3227; https://doi.org/10.3390/rs13163227
Received: 24 June 2021 / Revised: 5 August 2021 / Accepted: 10 August 2021 / Published: 13 August 2021
(This article belongs to the Special Issue 3D Modelling and Mapping for Precision Agriculture)
The increased development of camera resolution, processing power, and aerial platforms helped to create more cost-efficient approaches to capture and generate point clouds to assist in scientific fields. The continuous development of methods to produce three-dimensional models based on two-dimensional images such as Structure from Motion (SfM) and Multi-View Stereopsis (MVS) allowed to improve the resolution of the produced models by a significant amount. By taking inspiration from the free and accessible workflow made available by OpenDroneMap, a detailed analysis of the processes is displayed in this paper. As of the writing of this paper, no literature was found that described in detail the necessary steps and processes that would allow the creation of digital models in two or three dimensions based on aerial images. With this, and based on the workflow of OpenDroneMap, a detailed study was performed. The digital model reconstruction process takes the initial aerial images obtained from the field survey and passes them through a series of stages. From each stage, a product is acquired and used for the following stage, for example, at the end of the initial stage a sparse reconstruction is produced, obtained by extracting features of the images and matching them, which is used in the following step, to increase its resolution. Additionally, from the analysis of the workflow, adaptations were made to the standard workflow in order to increase the compatibility of the developed system to different types of image sets. Particularly, adaptations focused on thermal imagery were made. Due to the low presence of strong features and therefore difficulty to match features across thermal images, a modification was implemented, so thermal models could be produced alongside the already implemented processes for multispectral and RGB image sets. View Full-Text
Keywords: structure from motion; multi-view stereo; Poisson Reconstruction; multispectral; aerial mapping; stitching; thermal; 2D and 3D mapping structure from motion; multi-view stereo; Poisson Reconstruction; multispectral; aerial mapping; stitching; thermal; 2D and 3D mapping
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MDPI and ACS Style

Vong, A.; Matos-Carvalho, J.P.; Toffanin, P.; Pedro, D.; Azevedo, F.; Moutinho, F.; Garcia, N.C.; Mora, A. How to Build a 2D and 3D Aerial Multispectral Map?—All Steps Deeply Explained. Remote Sens. 2021, 13, 3227. https://doi.org/10.3390/rs13163227

AMA Style

Vong A, Matos-Carvalho JP, Toffanin P, Pedro D, Azevedo F, Moutinho F, Garcia NC, Mora A. How to Build a 2D and 3D Aerial Multispectral Map?—All Steps Deeply Explained. Remote Sensing. 2021; 13(16):3227. https://doi.org/10.3390/rs13163227

Chicago/Turabian Style

Vong, André, João P. Matos-Carvalho, Piero Toffanin, Dário Pedro, Fábio Azevedo, Filipe Moutinho, Nuno Cruz Garcia, and André Mora. 2021. "How to Build a 2D and 3D Aerial Multispectral Map?—All Steps Deeply Explained" Remote Sensing 13, no. 16: 3227. https://doi.org/10.3390/rs13163227

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