Aller au contenu principal

page search

Bibliothèque Enhancing post-classification change detection through morphological post-processing – a sensitivity analysis

Enhancing post-classification change detection through morphological post-processing – a sensitivity analysis

Enhancing post-classification change detection through morphological post-processing – a sensitivity analysis

Resource information

Date of publication
Décembre 2013
Resource Language
ISBN / Resource ID
AGRIS:US201400169070
Pages
7145-7162

Monitoring land-cover change is often done by simple overlay of two classified maps from different dates. However, such analysis tends to overestimate the rate of change. Main error sources are the mis-registration between classified maps and their thematic accuracies. This study proposes a change detection method with morphological post-processing to improve change detection accuracy in comparison with traditional post-classification by taking into account these error sources. The method is developed for binary maps and is based on standard morphological procedures that are generally integrated in common spatial processing or free software. A detailed sensitivity analysis of this method based on simulated data sets of different landscape characteristics and error levels demonstrated the potential improvement. The degree of improvement in change detection accuracy mainly depended on the error type and level and the fragmentation of the landscape. In particular, location error effects on change detection were strongly reduced independent of class proportion. Up to 60% improvement in user's accuracy of change could be achieved for maps with location error and characterized by fragmented landscapes. Coping with classification errors was shown to be more challenging. A user-friendly reference table summarizes the potential improvement through the proposed methods for various landscape characteristics and error sources.

Share on RLBI navigator
NO

Authors and Publishers

Author(s), editor(s), contributor(s)

Seebach, Lucia
Strobl, Peter
Vogt, Peter
Mehl, Wolfgang
San-Miguel-Ayanz, Jesús

Publisher(s)
Data Provider