A fast approach for adaptive conventional recursive least-squares predictor in lossless compression of hyperspectral images Hiperspektral Görüntülerin Kayipsiz Sikiştirmasinda Adaptif Geleneksel Yinelemeli En Küçük Karesel Hata Kestiricisi için Hizli Bir Yaklaşim

KARACA A. C., Gullu M. K.

25th Signal Processing and Communications Applications Conference, SIU 2017, Antalya, Türkiye, 15 - 18 Mayıs 2017 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası:
  • Doi Numarası: 10.1109/siu.2017.7960453
  • Basıldığı Şehir: Antalya
  • Basıldığı Ülke: Türkiye
  • Anahtar Kelimeler: Hyperspectral image compression, lossless compression, recursive least squares algorithm
  • Yıldız Teknik Üniversitesi Adresli: Hayır

Özet

© 2017 IEEE.Recursive least-squares (RLS) based prediction methods are very popular in lossless compression of hyperspectral imaging. Adaptive selection of number of bands used in the prediction in RLS based methods increases compression performance significantly. However, this process brings additional computational load. In this work, a sample reduction based fast adaptive method to determine the number of bands required for prediction is proposed. Performance of the proposed method is compared to the-state-of-the-art methods in terms of bitrates and computation times and obtained results are discussed.