Classification of Targets in SAR Images Using SVM and k-NN Techniques


Demirhan M. E. , SALOR DURNA Ö.

24th Signal Processing and Communication Application Conference (SIU), Zonguldak, Türkiye, 16 - 19 Mayıs 2016, ss.1581-1584 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası:
  • Doi Numarası: 10.1109/siu.2016.7496056
  • Basıldığı Şehir: Zonguldak
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.1581-1584

Özet

In this paper, a method developed for classification of various military target types acquired from Synthetic Aperture Radar (SAR) images is described. For classification, first images are enhanced and segmentation is performed. Then, in the feature extraction step, the use of Modified Radial Function - (MRF) based features is proposed, which had not been used in previously for SAR-based classification studies in the literature. In addition to MRF, the mean of the segmented image and ellipse axis rate are used as features to increase the classification accuracy. A classification accuracy of 93.34% has been achieved by using Support Vector Machines (SVM) and k-Nearest Neighbors (k-NN) classifiers.