Spectral-spatial classification based on integrated segmentation

Pedram Ghamisi, Micael S. Couceiro, Mathieu Fauvel, Jon Atli Benediktsson

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

A new spectral-spatial method for the classification of hyperspectral images is introduced. The proposed approach is based on two segmentation methods, Fractional-Order Darwinian Particle Swarm Optimization and Mean Shift Segmentation and one clustering method, K-means. In parallel, the input data set is classified by Support Vector Machines (SVM). Furthermore, the result of the segmentation and clustering steps are combined with the result of SVM through majority voting within each object. The final classification map is made by using majority voting between three produced classification maps. Experimental results indicate that the proposed method can significantly improve SVM and other studied methods in terms of accuracies.

Original languageEnglish
Title of host publication2013 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013 - Proceedings
Pages1458-1461
Number of pages4
DOIs
Publication statusPublished - 2013
Event2013 33rd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013 - Melbourne, VIC, Australia
Duration: 21 Jul 201326 Jul 2013

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference2013 33rd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013
Country/TerritoryAustralia
CityMelbourne, VIC
Period21/07/1326/07/13

Other keywords

  • Hyperspectral Image Analysis
  • Mean Shift Segmentation
  • Multilevel Segmentation
  • Remote Sensing
  • Support Vector Machine classifier
  • Swarm Optimization

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