Contextual land use classification

How detailed can the class structure be?

authored by

L. Albert, F. Rottensteiner, C. Heipke

Abstract

The goal of this paper is to investigate the maximum level of semantic resolution that can be achieved in an automated land use change detection process based on mono-temporal, multi-spectral, high-resolution aerial image data. For this purpose, we perform a step-wise refinement of the land use classes that follows the hierarchical structure of most object catalogues for land use databases. The investigation is based on our previous work for the simultaneous contextual classification of aerial imagery to determine land cover and land use. Land cover is determined at the level of small image segments. Land use classification is applied to objects from the geospatial database. Experiments are carried out on two test areas with different characteristics and are intended to evaluate the step-wise refinement of the land use classes empirically. The experiments show that a semantic resolution of ten classes still delivers acceptable results, where the accuracy of the results depends on the characteristics of the test areas used. Furthermore, we confirm that the incorporation of contextual knowledge, especially in the form of contextual features, is beneficial for land use classification.

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

41

Pages

11-18

No. of pages

8

ISSN

1682-1750

Publication date

2016

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-XLI-B4-11-2016 (Access: Open)