%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2022/09.10.19.32
%2 sid.inpe.br/sibgrapi/2022/09.10.19.32.31
%@doi 10.1109/SIBGRAPI55357.2022.9991781
%T Non-Linear co-registration in UAVs' images using deep learning
%D 2022
%A Silva, Leandro Henrique Furtado Pinto,
%A Júnior, Jocival Dantas Dias,
%A Mari, João Fernando,
%A Escarpinati, Mauricio Cunha,
%A Backes, André Ricardo,
%@affiliation School of Computer Science, Federal University of Uberlândia
%@affiliation School of Computer Science, Federal University of Uberlândia
%@affiliation Federal University of Viçosa, Campus Rio Paranaíba
%@affiliation School of Computer Science, Federal University of Uberlândia
%@affiliation School of Computer Science, Federal University of Uberlândia
%B Conference on Graphics, Patterns and Images, 35 (SIBGRAPI)
%C Natal, RN
%8 24-27 Oct. 2022
%S Proceedings
%K image registration, multispectral image, deep learning, precision agriculture, UAV.
%X Unmanned Aerial Vehicles (UAVs) has stood out for assisting, enhancing, and optimizing agricultural production. Images captured by UAVs allow a detailed view of the analyzed region since the flight occurs at low and medium altitudes (50m to 400m). In addition, there is a wide variety of sensors (RGB cameras, heat capture sensors, multi and hyperspectral cameras, among others), each with its own characteristics and capable of producing different information. In multi-spectral images acquisition, we use a distinct sensor to capture each image band and at different time, leading to misalignments. To tackle this problem we propose to train a deep neural network to predict the vector deformation fields to perform the registration between bands of a multi-spectral image. The proposed approach has an accuracy ranging from 89.90% to 93.79% in the task of estimating the displacement field between bands. With this field estimated by the network, it is possible to register between the bands without the need for manual marking of points.
%@language en
%3 backes_9.pdf