Skip to main content

The potential of kernel classification techniques for land use mapping in urban areas using 5m-spatial resolution IRS-1C imagery

Buy Article:

$55.00 plus tax (Refund Policy)

Two techniques, integrating texture and spatial context properties for the classification of fine spatial resolution imagery from the city of Athens (Hellas) have been tested in terms of accuracy and class specificity. Both techniques were kernel based, using an artificial neural network and the kernel reclassification algorithm. The study demonstrated the high potential of the kernel classifiers to discriminate residential categories on 5 m-spatial resolution imagery. The overall accuracy percentages achieved were 73.44% and 74.22% respectively, considering a seven-class classification scheme. The adopted scheme was subset of the nomenclature referred to as 'Classification for Land Use Statistics Eurostat's Remote Sensing programme' (CLUSTERS) used by the Statistical Office of the European Communities (EUROSTAT) to map urban and rural environment.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics

Document Type: Research Article

Affiliations: 1: Institute for Space Applications and Remote Sensing, National Observatory of Athens, Metaxa and Vas.Pavlou Str, Palaia Penteli, GR 15236, Athens, Greece 2: GAFmbH, Company for Applied Remote Sensing, Arnulfstr. 197, 80634, Munich, Germany

Publication date: 2000-11-10

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