Article

AiRound and CV-BrCT: Novel Multiview Datasets for Scene Classification

Citation

Machado G, Ferreira E, Nogueira K, Oliveira H, Brito M, Gama PHT & Santos JAd (2021) AiRound and CV-BrCT: Novel Multiview Datasets for Scene Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, pp. 488-503. https://doi.org/10.1109/JSTARS.2020.3033424

Abstract
It is undeniable that aerial/satellite images can provide useful information for a large variety of tasks. But, since these images are always taken from above, some applications can benefit from complementary information provided by other perspective views of the scene, such as ground-level images. Despite a large number of public repositories for both georeferenced photographs and aerial images, there is a lack of benchmark datasets that allow the development of approaches that exploit the benefits and complementarity of aerial/ground imagery. In this article, we present two new publicly available datasets named AiRound and CV-BrCT. The first one contains triplets of images from the same geographic coordinate with different perspectives of view extracted from various places around the world. Each triplet is composed of an aerial RGB image, a ground-level perspective image, and a Sentinel-2 sample. The second dataset contains pairs of aerial and street-level images extracted from southeast Brazil. We design an extensive set of experiments concerning multiview scene classification, using early and late fusion. Such experiments were conducted to show that image classification can be enhanced using multiview data.

Keywords
Data fusion; dataset; deep learning; feature fusion; multimodal machine learning; remote sensing

Journal
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing: Volume 14

StatusPublished
FundersBrazilian National Research Council
Publication date31/12/2021
Publication date online23/10/2020
Date accepted by journal17/10/2020
URLhttp://hdl.handle.net/1893/32232
ISSN1939-1404
eISSN2151-1535