Optimization of Hyperspectral Images and Performance Evaluation Using Effective Loss Algorithm
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Publisher
Springer Singapore
Link
http://link.springer.com/content/pdf/10.1007/978-981-10-0448-3_77
Reference18 articles.
1. Cheng, K., Dill, J.: Hyper spectral images lossless compression using the 3D binary EZW algorithm. In: Proceedings of the SPIE 8655, Image Processing: Algorithms and Systems XI, 865515, Feb 19, 2013. doi: 10.1117/12.2002820
2. Alissou, S.A., Zhang, Y.: Hyper spectral data compression using lasso algorithm for spectral decorrelation. In: Proceedings of the SPIE 9124, Satellite Data Compression, Communications, and Processing X, 91240A, May 22, 2014. doi: 10.1117/12.2053265
3. Cheng, K., Dill, J.: An improved EZW Hyper spectral Image compression. J. Comput. Commun. 2, 31–36. doi: 10.4236/jcc.2014.22006
4. Nian, Y., He, M., Wan, J.: Low-Complexity compression algorithm for hyper spectral images based on distributed source coding. Math. Prob. Eng. 2013, Article ID 825673, 7 pp. (2013)
5. Anbarjafari, G. et al.: Lossy image compression using singular value decomposition and wavelet difference reduction. Digital Signal Processing (Impact Factor: 1.92). Sep 2013. doi: 10.1016/j.dsp.2013.09.008
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