Compression of hyperspectral images using superpixel based and error-corrected sparse representation Hiperspektral görüntülerin süperpiksel temelli ve hata düzeltmeli seyrek gösterimle sikiştirilmasi

Ertem A., KARACA A. C., Gullu M. K.

27th Signal Processing and Communications Applications Conference, SIU 2019, Sivas, Turkey, 24 - 26 April 2019 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1109/siu.2019.8806302
  • City: Sivas
  • Country: Turkey
  • Keywords: Dictionary learning, Hyperspectral imaging, Image compression, Sparse coefficient, Superpixel
  • Yıldız Technical University Affiliated: No


© 2019 IEEE.Performance of the superpixel based SSASR method used in hyperspectral image compression is noticeably higher than the methods in the literature. However, the SSASR method gives low signal-to-noise ratio values if there are few pixels that differ spectrally in the image. It is aimed to overcome this problem with the proposed method. In the proposed method, first of all, the pixels that are significantly distorted in the reconstructed data are determined. After that, a new dictionary and sparse coefficients are defined for these pixels. In the experiments, the proposed method is compared with SSASR and the other state-of-the-art methods in terms of different quality metrics. It is represented that the the proposed M-SSASR method provides superior performance compared to the other methods for all these metrics.