%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2021/09.06.13.34
%2 sid.inpe.br/sibgrapi/2021/09.06.13.34.59
%T Scene classification using a combination of aerial and ground images
%D 2021
%A Machado, Gabriel Lucas Silva,
%A Nogueira, Keiller,
%A dos Santos, Jefersson Alex,
%@affiliation Universidade Federal de Minas Gerais
%@affiliation University of Stirling
%@affiliation Universidade Federal de Minas Gerais
%E Paiva, Afonso,
%E Menotti, David,
%E Baranoski, Gladimir V. G.,
%E Proença, Hugo Pedro,
%E Junior, Antonio Lopes Apolinario,
%E Papa, João Paulo,
%E Pagliosa, Paulo,
%E dos Santos, Thiago Oliveira,
%E e Sá, Asla Medeiros,
%E da Silveira, Thiago Lopes Trugillo,
%E Brazil, Emilio Vital,
%E Ponti, Moacir A.,
%E Fernandes, Leandro A. F.,
%E Avila, Sandra,
%B Conference on Graphics, Patterns and Images, 34 (SIBGRAPI)
%C Gramado, RS, Brazil (virtual)
%8 18-22 Oct. 2021
%I Sociedade Brasileira de Computação
%J Porto Alegre
%S Proceedings
%K deep learning, machine learning, remote sensing, image classification, multi-modal machine learning, metric learning, cross-view matching.
%X lt is undeniable that aerial images can provide useful information for a large variety of tasks, such as disaster relief, and urban planning. But, since these images only see the Earth from one point of view, some applications may benefit from complementary information provided by other perspective views of the scene, such as ground-level images. Despite a large number of public image repositories for both georeferenced photos and aerial images (such as Google Maps, and Street View), there is a lack of public datasets that allow studies that exploit the complementarity of aerial+ground imagery. Given this, we present two new publicly available datasets named AiRound and CV-BrCT. Using both, we tackled the scene classification task in 2 different scenarios. The first one has a fully-paired image set, while the second has missing samples. In both situations, we used deep learning and feature fusion algorithms. To handle missing samples, we proposed a content-based image retrieval framework.
%@language en
%3 WTD_Gabriel.pdf