Unsupervised classification of hyperspectral images using an Adaptive Vector Tunnel classifier

DEMIRCI S., Erer I.

Conference on Image and Signal Processing for Remote Sensing XVIII, Edinburgh, Saint Helena, 24 - 26 September 2012, vol.8537 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 8537
  • Doi Number: 10.1117/12.974716
  • City: Edinburgh
  • Country: Saint Helena
  • Istanbul Technical University Affiliated: Yes

Abstract

Hyperspectral image classification is one of the most popular information extraction methods in remote sensing applications. This method consists of variety of algorithms involving supervised, unsupervised or fuzzy classification, etc. In supervised classification, reference data which is known as a priori class information is used. On the other hand, computer based clustering algorithms are employed to group pixels which have similar spectral characteristics according to some statistical criteria in unsupervised classification.