Two-step Multi-spectral Registration Via Key-point Detector and Gradient Similarity: Application to Agronomic Scenes for Proxy-sensing

Jehan-Antoine Vayssade, Gawain Jones, Jean-Noel Paoli, Christelle Gee

2020

Abstract

The potential of multi-spectral images is growing rapidly in precision agriculture, and is currently based on the use of multi-sensor cameras. However, their development usually concerns aerial applications and their parameters are optimized for high altitudes acquisition by drone (UAV ≈ 50 meters) to ensure surface coverage and reduce technical problems. With the recent emergence of terrestrial robots (UGV), their use is diverted for nearby agronomic applications. Making it possible to explore new agronomic applications, maximizing specific traits extraction (spectral index, shape, texture . . . ) which requires high spatial resolution. The problem with these cameras is that all sensors are not aligned and the manufacturers’ methods are not suitable for close-field acquisition, resulting in offsets between spectral images and degrading the quality of extractable informations. We therefore need a solution to accurately align images in such condition. In this study we propose a two-steps method applied to the six-bands Airphen multi-sensor camera with (i) affine correction using pre-calibrated matrix at different heights, the closest transformation can be selected via internal GPS and (ii) perspective correction to refine the previous one, using key-points matching between enhanced gradients of each spectral bands. Nine types of key-point detection algorithms (ORB, GFTT, AGAST, FAST, AKAZE, KAZE, BRISK, SURF, MSER) with three different modalities of parameters were evaluated on their speed and performances, we also defined the best reference spectra on each of them. The results show that GFTT is the most suitable methods for key-point extraction using our enhanced gradients, and the best spectral reference was identified to be the band centered on 570 nm for this one. Without any treatment the initial error is about 62 px, with our method, the remaining residual error is less than 1 px, where the manufacturer’s involves distortions and loss of information with an estimated residual error of approximately 12 px.

Paper Citation

in Harvard Style

Vayssade J., Jones G., Paoli J. and Gee C. (2020). Two-step Multi-spectral Registration Via Key-point Detector and Gradient Similarity: Application to Agronomic Scenes for Proxy-sensing. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP; ISBN 978-989-758-402-2, SciTePress, pages 103-110. DOI: 10.5220/0009169301030110

in Bibtex Style

@conference{visapp20,
author={Jehan-Antoine Vayssade and Gawain Jones and Jean-Noel Paoli and Christelle Gee},
title={Two-step Multi-spectral Registration Via Key-point Detector and Gradient Similarity: Application to Agronomic Scenes for Proxy-sensing},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP},
year={2020},
pages={103-110},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009169301030110},
isbn={978-989-758-402-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP
TI - Two-step Multi-spectral Registration Via Key-point Detector and Gradient Similarity: Application to Agronomic Scenes for Proxy-sensing
SN - 978-989-758-402-2
AU - Vayssade J.
AU - Jones G.
AU - Paoli J.
AU - Gee C.
PY - 2020
SP - 103
EP - 110
DO - 10.5220/0009169301030110
PB - SciTePress