Associative semantic ranking of satellite images using PathFinder Network Scaling ensemble methods

Adrian S. Barb, Chi Ren Shyu

Research output: Contribution to conferencePaperpeer-review

4 Scopus citations

Abstract

This article proposes a methodology to reduce overfitting when ranking high-resolution satellite images by domain semantics. Our approach uses PathFinder Network Scaling ensemble methods. We generate cross-fold co-occurrence matrices for relevance of feature subspaces to each semantic. Each matrix is then reduced using the PathFinder network scaling algorithm. Irrelevant nodes are removed using node strength metrics resulting in an optimized model for ranking by semantic that generalizes better to new images. The experiments show that, when using this approach, the quality of ranking by semantic can be significantly improved. Results show that Mean Average Precision (MAP) of ranking over cross-fold experiments increased by a 13.2% while standard deviation of MAP was reduced by 16.8% relatively to experiments without PathFinder network scaling.

Original languageEnglish (US)
Pages5289-5292
Number of pages4
DOIs
StatePublished - Dec 1 2012
Event2012 32nd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2012 - Munich, Germany
Duration: Jul 22 2012Jul 27 2012

Other

Other2012 32nd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2012
CountryGermany
CityMunich
Period7/22/127/27/12

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Earth and Planetary Sciences(all)

Fingerprint Dive into the research topics of 'Associative semantic ranking of satellite images using PathFinder Network Scaling ensemble methods'. Together they form a unique fingerprint.

Cite this