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

authored by

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.

Organisation(s)

Institute of Photogrammetry and GeoInformation (IPI)

Type

Conference article

Journal

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

Volume

40

Pages

17-24

No. of pages

8

ISSN

1682-1750

Publication date

11.08.2014

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Information Systems, Geography, Planning and Development

Sustainable Development Goals

SDG 15 - Life on Land

Electronic version(s)

https://doi.org/10.5194/isprsarchives-XL-3-17-2014 (Access: Open)