Contextual land use classification

How detailed can the class structure be?

verfasst von

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.

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

41

Seiten

11-18

Anzahl der Seiten

8

ISSN

1682-1750

Publikationsdatum

2016

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-XLI-B4-11-2016 (Zugang: Offen)