Skip to main content

Post-classification change detection with data from different sensors: some accuracy considerations

Buy Article:

$55.00 plus tax (Refund Policy)

Change detection from remote sensing data is often done by simple overlay of classified maps. However, such analyses can contain a significant proportion of boundary errors, especially when combining data from different sensors. This paper presents a protocol that allows reliable post-classification comparisons by taking into account classification accuracies, landscape fragmentation, planimetric accuracies, pixel sizes and grid origins. The proposed protocol has been applied, with little extra effort, in a fragmented agricultural Mediterranean zone using MSS (1970s) and TM (1990s) images. Applying the protocol, change detection had an accuracy of 85.1%, while for a direct overlay it was only 43.9% accurate. The drawback of this method is that it reduces the useful area of comparison. As the accuracy of individual classifications is critical, the paper also describes and tests a hybrid classifier that combines an unsupervised classification approach with training areas. This approach has proved more successful than maximum likelihood classifiers.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Data/Media
No Metrics

Document Type: Research Article

Affiliations: Departament de Geografia, Edifici B, Universitat Autònoma de Barcelona 08193 Bellaterra, Spain

Publication date: 2003-08-01

More about this publication?
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more