Semantic labeling of aerial images by learning class-specific object proposals

Michele Volpi, Devis Tuia

Research output: Chapter in Book/Report/Conference proceedingConference paper

1 Citation (Scopus)

Abstract

Land-cover and land-use semantic labeling in centimeter resolution imagery (ultra-high resolution) is mostly performed by supervised classification of informative descriptors extracted from spatially coherent but small objects (e.g. superpixels or patches). In this paper, we propose an extension of this reasoning by proposing a class-specific, multi-scale and bottom-up object proposal strategy to perform semantic labeling. Specifically, we rely on a fully trainable boundary (edge) detector, allowing us to extract class-specific object-proposals. Such proposals enable training rich appearance and object models as well as enhanced spatial reasoning. We evaluate the proposed strategy on the Vaihingen dataset with promising results.

Original languageEnglish
Title of host publication2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1556-1559
Number of pages4
ISBN (Electronic)9781509033324
ISBN (Print)9781509033331
DOIs
Publication statusPublished - Nov 2016
Externally publishedYes
Event36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Beijing, China
Duration: 10 Jul 201615 Jul 2016

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume2016-November

Conference

Conference36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016
CountryChina
CityBeijing
Period10/07/1615/07/16

Keywords

  • Aerial imagery
  • Object proposals
  • Object-based classification
  • Random forests
  • Semantic labeling

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  • Cite this

    Volpi, M., & Tuia, D. (2016). Semantic labeling of aerial images by learning class-specific object proposals. In 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings (pp. 1556-1559). [7729397] (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2016-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS.2016.7729397