Haar Dalgacik Donusumu ile Izgel llintisizlestirilmis Hiperspektral Verilerin Seyrek Kodlamaya Dayali Sikistirilmasi

Sparse coding based compression of hyperspectral imagery yields better rate-distortion performance especially for low bit-rates when compared with other state-of-the-art methods in the literature. In this paper, an on-line dictionary learning based lossy compression method is proposed yielding even a better rate-distortion performance, thanks to the spectral decorrelation achieved by the Haar wavelet transform. The hyperspectral data is decorrelated in the spectral dimension using a single-level Haar transform which is followed by a dictionary learning step over the low-subband data. The higher subband is further compressed in a lossless manner using JPEG2000. Rate-distortion results are obtaind for AVIRIS hyperspectral data. Results indicate that the spectral decorrelation coupled with sparse dictionary learning of low-subband images yield superior performance over existing hyperspectral data compression schemes.