A two-layer Conditional Random Field model for simultaneous classification of land cover and land use

verfasst von

L. Albert, F. Rottensteiner, C. Heipke

Abstract

This paper proposes a two-layer Conditional Random Field model for simultaneous classification of land cover and land use. Both classification tasks are integrated into a unified graphical model, which is reasonable due to the fact that land cover and land use exhibit strong contextual dependencies. In the CRF, we distinguish a land cover layer and a land use layer. Both layers differ with respect to the entities corresponding to the nodes and the classes to be distinguished. In the land cover layer, the nodes correspond to superpixels extracted from the image data, whereas in the land use layer the nodes correspond to objects of a geospatial land use database. Statistical dependencies between land cover and land use are explicitly modelled as pair-wise potentials. Thus, we obtain a consistent model, where the relations between land cover and land use are learned from representative training data. The approach is designed for input data based on aerial images. Experiments are performed on an urban test site. The experiments show the feasibility of the combination of both classification tasks into one overall approach and investigate the influence of the size of the superpixels on the classification result.

Organisationseinheit(en)

Institut für Photogrammetrie und Geoinformation

Typ

Konferenzaufsatz in Fachzeitschrift

Journal

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives

Band

40

Seiten

17-24

Anzahl der Seiten

8

ISSN

1682-1750

Publikationsdatum

11.08.2014

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Information systems, Geografie, Planung und Entwicklung

Ziele für nachhaltige Entwicklung

SDG 15 – Lebensraum Land

Elektronische Version(en)

https://doi.org/10.5194/isprsarchives-XL-3-17-2014 (Zugang: Offen)