Abstract
This paper proposes a novel method of lossy hyperspectral image compression using online learning dictionary. Spectral dictionary that learned in sparse coding mode could be used to represent the corresponding material. From the perspective of sparse coding, learning a sparse dictionary could achieve a better result of data decorrelation. In order to compress the hyperspectral data, an online learning sparse coding dictionary which could describe the characteristics of spectral curve was created to represent and reconstruct hyperspectral data. In the online learning phase, effective clustering algorithm is applied to generate and update the dictionary more properly. Results indicate that dictionary achieved by our method could improve the compression quality of hyperspectral image observably.
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Acknowledgments
This work is partially funded by the MOE–Microsoft Key Laboratory of Natural Language Processing and Speech, Harbin Institute of Technology, the Major State Basic Research Development Program of China (973 Program 2015CB351804) and the National Natural Science Foundation of China under Grant No. 61572155, 61672188 and 61272386. We would also like to acknowledge NVIDIA Corporation who kindly provided two sets of GPU.
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Jifara, W., Jiang, F., Zhang, B. et al. Hyperspectral image compression based on online learning spectral features dictionary. Multimed Tools Appl 76, 25003–25014 (2017). https://doi.org/10.1007/s11042-017-4724-8
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DOI: https://doi.org/10.1007/s11042-017-4724-8