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Cannelle Bertrand

Professeur HES ordinaire, Responsable de la filière Génie territorial (GTE), Responsable de la filière Géomatique (GEC)

Main skills

Gestion de filière

BIM Building Information Modeling

Main contract

Professeur HES ordinaire, Responsable de la filière Génie territorial (GTE), Responsable de la filière Géomatique (GEC)

Phone: +41 24 557 75 65

Desktop: D53

Haute Ecole d'Ingénierie et de Gestion du Canton de Vaud
Route de Cheseaux 1, 1400 Yverdon-les-Bains, CH

Institute
insit - Institut d’ingénierie du territoire

BSc HES-SO en Géomatique - Haute Ecole d'Ingénierie et de Gestion du Canton de Vaud

BSc HES-SO en Génie territorial - Haute école d'Ingénierie et de Gestion du Canton de Vaud

MSc UNIGE/HES-SO en Développement territorial - HES-SO Master

2025

Lexique de la photogrammétrie

Book chapter ArODES

Emmanuel Cledat, Jean-François Hangouët, Arnaud Le Bris, Marc Poupée, Adrien Gressin, Bertrand Cannelle, Sylvie Daniel, Christian Larouche, Elisabeth Simonetto, Maxime Seguin

Lexique de l'Association francophone de topographie (50 p.). 2025, Saint-Mandé, France : Association francophone de topographie (AFP) Saint-Mandé, France : Société Française de Photogrammétrie et de Télédétection

Link to the publication

2020

Application of photogrammetry and image processing for the study of porous surface courses :

Conference ArODES

which accuracy for which application?

Bertrand Cannelle, Françoise Beltzung, Michela Thiémard-Spada

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; Proceedings of International Society for Photogrammetry and Remote Sensing (ISPRS), 31 August - 2 September 2020, Online

Link to the conference

Summary:

This article presents an application of photogrammetry and image processing for road sample analysis. The technique makes it possible to measure the void content of a sample and at the same time to analyse this physical characteristic as a function of the depth relative to the pavement surface. In a first part, the device dedicated and data acquired will be presented, then our algorithm (pore extraction and geometry processing) will be described and finally the results will be discussed, both on the quantitative aspect and on the transmission via a dedicated report.

2019

Application de la photogrammétrie et du traitement d'images pour l'étude de revêtements routier

Conference ArODES

Bertrand Cannelle, N. Chevallier, Michela Thiémard-Spada, Françoise Beltzung, Tobias Balmer

Actes de la Conférence Française de Photogrammétrie et de Télédétection (CFPT) 2018, 25-28 juin 2018, Marne-la-Vallée, France

Link to the conference

2016

Augmenting a convolutional neural network with local histograms :

Conference ArODES

a case study in crop classification from high-resolution UAV imagery

Julien Rebetez, Héctor F. Satizábal, Matteo Mota, Dorothea Noll, Lucie Büchi, Marina Wendling, Bertrand Cannelle, Andres Perez-Uribe, Stéphane Burgos

Proceedings of ESANN 2016, European Symposium on artifical neural networks, Computational Intelligence and Machine Learning, 27-29 April 2016, Bruges, Belgium

Link to the conference

Summary:

The advent of affordable drones capable of taking high resolution images of agricultural fields creates new challenges and opportunities in aerial scene understanding. This paper tackles the problem of recognizing crop types from aerial imagery and proposes a new hybrid neural network architecture which combines histograms and convolutional units. We evaluate the performance of the proposed model on a 23-class classification task and compare it to other models. The result is an improvement of the classification performance.

2015

Use of very high-resolution airborne images to analyse 3d canopy architecture of a vineyard

Conference ArODES

Stéphane Burgos, Matteo Mota, Dorothea Noll, Bertrand Cannelle

Proceedings of ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 28 September - 3 October 2015, La Grande Motte, France

Link to the conference

Summary:

Differencing between green cover and grape canopy is a challenge for vigour status evaluation in viticulture. This paper presents the acquisition methodology of very high-resolution images (4 cm), using a Sensefly Swinglet CAM unmanned aerial vehicle (UAV) and their processing to construct a 3D digital surface model (DSM) for the creation of precise digital terrain models (DTM). The DTM was obtained using python processing libraries. The DTM was then subtracted to the DSM in order to obtain a differential digital model (DDM) of a vineyard. In the DDM, the vine pixels were then obtained by selecting all pixels with an elevation higher than 50 [cm] above the ground level. The results show that it was possible to separate pixels from the green cover and the vine rows. The DDM showed values between −0.1 and + 1.5 [m]. A manually delineation of polygons based on the RGB image belonging to the green cover and to the vine rows gave a highly significant differences with an average value of 1.23 [m] and 0.08 [m] for the vine and the ground respectively. The vine rows elevation is in good accordance with the topping height of the vines 1.35 [m] measured on the field. This mask could be used to analyse images of the same plot taken at different times. The extraction of only vine pixels will facilitate subsequent analyses, for example, a supervised classification of these pixels.

Achievements

PEOPLE@HES-SO Directory and Skills inventory

Cannelle Bertrand

Professeur HES ordinaire, Responsable de la filière Génie territorial (GTE), Responsable de la filière Géomatique (GEC)

Main skills

Gestion de filière

BIM Building Information Modeling

Professeur HES ordinaire, Responsable de la filière Génie territorial (GTE), Responsable de la filière Géomatique (GEC)

PEOPLE@HES-SO
Directory and Skills inventory