Information Technology Reference
In-Depth Information
structure but reduced for the images Jasper Ridge and Moffett Field. Thus, our
recommendation is to use 16 sub-bands in the group.
4 Conclusions
The modification of the DCT-based method for the lossy compression of the
hyperspectral AVIRIS data is proposed. Due to the proposed blind estimation
of the additive noise standard deviation and the image normalization with sub-
band grouping, the compression ratio (CR) has increased by 6 . . . 50% with
respect to the method designed earlier. The grouping of the bands remains image-
dependent: images with simple structures allow a higher number of bands in a
group. If there are more complex structures then the compression ratio may
reduce. Thus, the recommendation is to use a quasi-optimal number, namely 16,
of the bands in a group to remain in the safe side with respect to the compression.
The filtering effect is observed for sub-bands with low SNR whilst no visual
distortions take place for sub-bands with large SNR.
References
1. Christophe, E., Leger, D., Mailhes, C.: Quality criteria benchmark for hyperspectral
imagery. IEEE Transactions on Geoscience and Remote Sensing 43(9), 2103-2114
(2005)
2. Chang, C.-I. (ed.): Hyperspectral Data Exploitation: Theory and Applications.
Wiley-Interscience, Hoboken (2007)
3. Kaarna, A.: Compression of Spectral Images. In: Obinata, G., Dutta, A. (eds.)
Vision Systems: Segmentation and Pattern Recognition, I-Tech, Austria, ch. 14,
pp. 269-298 (2007)
4. Mielikainen, J.: Lossless compression of hyperspectral images using lookup tables.
IEEE Signal Processing Letters 13(3), 157-160 (2006)
5. Penna, B., Tillo, T., Magli, E., Olmo, G.: Transform coding techniques for lossy
hyperspectral data compression. IEEE Transactions on Geoscience and Remote
Sensing 45(5), 1408-1421 (2007)
6. Aiazzi, B., Baronti, S., Lastri, C., Santurri, L., Alparone, L.: Low complexity
lossless/near-lossless compression of hyperspectral imagery through classified linear
spectral prediction. In: Proceedings of IEEE International Geoscience and Remote
Sensing Symposium, p. 4 (2005)
7. Tang, X., Pearlman, W.A.: Three-dimensional wavelet-based compression of hyper-
spectral images. In: Motta, G., Rizzo, F., Storer, J.A. (eds.) Hyperspectral Data
Compression, pp. 273-308. Springer US, Heidelberg (2006)
8. Miguel, A.C., Askew, A.R., Chang, A., Hauck, S., Ladner, R.E., Riskin, E.A.:
Reduced complexity wavelet-based predictive coding of hyperspectral images for
FPGA implementation. In: Proceedings of Data Compression Conference, pp. 1-10
(2004)
9. Ryan, M.J., Pickering, M.R.: An improved M-NVQ algorithm for the compres-
sion of hyperspectral data. In: Proceedings of IEEE International Geoscience and
Remote Sensing Symposium, vol. 2, pp. 600-602 (2000)
Search WWH ::




Custom Search