Image analysis based on probabilistic models

verfasst von

Christian Heipke, Franz Rottensteiner

Abstract

This paper discusses random field based image classification methods, and in particular conditional random fields (CRF), for topographic mapping. A short review of the CRF principles reveals their main advantages, namely the possibility to incorporate local context into the classification to quantify the quality of the results in terms of probabilities. Three examples, the classification of point cloud data, multi-temporal and multi-scale classification of satellite images of different epochs and geometric resolution as well as the verification of existing land use data demonstrate the power and flexibility of CRF, but also its limitation in terms of capturing long range context. The paper closes with a short discussion on how to overcome this deficiency in the future.

Organisationseinheit(en)

Institut für Photogrammetrie und Geoinformation

Typ

Paper

Publikationsdatum

2015

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Computernetzwerke und -kommunikation

Ziele für nachhaltige Entwicklung

SDG 15 – Lebensraum Land

Elektronische Version(en)

https://www.ipi.uni-hannover.de/fileadmin/ipi/publications/ACRS2015_Paper-ID_140.pdf (Zugang: Unbekannt)