Large-scale semantic classification: Outcome of the first year of inria aerial image labeling benchmark

Published

Conference Paper

© 2018 IEEE. Over the recent years, there has been an increasing interest in large-scale classification of remote sensing images. In this context, the Inria Aerial Image Labeling Benchmark has been released online in December 2016. In this paper, we discuss the outcomes of the first year of the benchmark contest, which consisted in dense labeling of aerial images into building/not building classes, covering areas of five cities not present in the training set. We present four methods with the highest numerical accuracies, all four being convolutional neural network approaches. It is remarkable that three of these methods use the U-net architecture, which has thus proven to become a new standard in image dense labeling.

Full Text

Duke Authors

Cited Authors

  • Huang, B; Lu, K; Audebert, N; Khalel, A; Tarabalka, Y; Malof, J; Boulch, A; Saux, BL; Collins, L; Bradbury, K; Lefèvre, S; El-Saban, M

Published Date

  • October 31, 2018

Published In

  • International Geoscience and Remote Sensing Symposium (Igarss)

Volume / Issue

  • 2018-July /

Start / End Page

  • 6947 - 6950

International Standard Book Number 13 (ISBN-13)

  • 9781538671504

Digital Object Identifier (DOI)

  • 10.1109/IGARSS.2018.8518525

Citation Source

  • Scopus