Getting pixels and regions to agree with conditional random fields

Devis Tuia, Michele Volpi, Gabriele Moser

Research output: Chapter in Book/Report/Conference proceedingConference paperAcademicpeer-review

6 Citations (Scopus)

Abstract

Land cover / land use classification of remotely sensed images is inherently geographical. The use of spatial information, accounting for neighborhood relationship and spatial smoothness of geographical objects, made its proofs in countless occasions and, especially when considering very high resolution images, methods ignoring spatial context do not perform well. In this paper, we propose a hybrid dual-layer conditional random field model that enforces spatial smoothness and consistency between the pixel and region-based maps. We formulate these intuitions as a standard energy minimization problem, and we show that finding a joint solution over both output spaces leads to strong improvements in the numerical and visual senses.

Original languageEnglish
Title of host publication2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings
Place of PublicationBeijing
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3290-3293
Number of pages4
ISBN (Electronic)9781509033324
ISBN (Print)9781509033317
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

  • Conditional random fields
  • Markov random fields
  • random forests
  • structured prediction
  • urban remote sensing
  • very high resolution

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