Multiple Instance Bagging Approach for Ensemble Learning Methods on Hyperspectral Images


Ergul U. , BİLGİN G.

23nd Signal Processing and Communications Applications Conference (SIU), Malatya, Türkiye, 16 - 19 Mayıs 2015, ss.403-406

Atıf İçin Kopyala
  • Doi Numarası: 10.1109/siu.2015.7129844
  • Basıldığı Şehir: Malatya
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayısı: ss.403-406

Özet

In this work, a novel ensemble learning (EnLe) method is proposed for hyperspectral images by the motivation of bagging method in the multiple instance (MI) learning (MIL) algorithms. Ensemble based bagging is made by using training samples in the hyperspectral scene and multiple instance bags are created by defining local variable windows upon selected instances. A naive classification method used in the multi-instance learning areas is adopted and applied to ROSIS-03 Pavia University hyperspectral image. Obtained classification results are presented along with the results of single classifiers and the results of the state of the art EnLe methods comparatively.