Abstract
Classification of hyperspectral images is paramount to an increasing number of user applications. With the advent of more powerful technology, sensed images demand for larger requirements in computational and memory capabilities, which has led to devise compression techniques to alleviate the transmission and storage necessities.
Classification of compressed images is addressed in this paper. Compression takes into account the spectral correlation of hyperspectral images together with more simple approaches. Experiments have been performed on a large hyperspectral CASI image with 72 bands. Both coding and classification results indicate that the performance of 3d-DWT is superior to the other two lossy coding approaches, providing consistent improvements of more than 10 dB for the coding process, and maintaining both the global accuracy and the percentage of classified area for the classification process.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Jensen, J.: Introductory Digital Image Processing. A Remote Sensing Perspective. Pearson Prentice Hall, London (2005)
Taubman, D.S., Marcellin, M.W.: JPEG 2000: Image Compression Fundamentals, Standards, and Practice. Kluwer Academic Publishers, Dordrecht (2002)
Li, Z., Yuan, X., Lam, K.W.: Effects of JPEG compression on the accuracy of photogrammetric point determination. Photogrammetric Engineering and Remote Sensing 68(8), 847–853 (2002)
Shih, T.Y., Liu, J.K.: Effects of JPEG 2000 compression on automated dsm extraction: evidence from aerial photographs. The Photogrammetric Record 20, 351–365 (2005)
Zabala, A., Pons, X., Diaz-Delgado, R., Garcia, F., Auli-Llinas, F., Serra-Sagrista, J.: Effects of JPEG and JPEG2000 lossy compression on remote sensing image classification for mapping crops and forest areas. In: IGARSS 2006, pp. 790–793. IEEE, Los Alamitos (2006)
Tintrup, F., De Natale, F., Giusto, D.: Automatic land classification vs. data compression: a comparative evaluation. In: Proceedings of IGARSS 1998, vol. 4, pp. 1751–1753. IEEE, Los Alamitos (1998)
Penna, B., Tillo, T., Magli, E., Olmo, G.: Transform coding techniques for lossy hyperspectral data compression. IEEE Trans. Geoscience Remote Sensing 45(5), 1408–1421 (2007)
Palà, V., Alamús, R., Pérez, F., Arbiol, R., Talaya, J.: El sistema CASI-ICC: un sensor multiespectral aerotransportado con capacidades cartográficas. In: Revista de Teledetección, Asociación Española de Teledetección, vol. 12, pp. 89–92 (1999)
Tang, X., Pearlman, W.A.: Three-Dimensional Wavelet-Based Compression of hyperspectral Images. In: Hyperspectral Data Compression, pp. 273–308. Springer, USA (2006)
Yeh, P.S., Armbruster, P., Kiely, A., Masschelein, B., Moury, G., Schaefer, C., Thiebaut, C.: The New CCSDS Image Compression Recommendation. In: Aerospace Conference, vol. 5-12, pp. 4138–4145. IEEE, Los Alamitos (2005)
Ramakrishna, B., Plaza, A., Chang, C.I., Ren, H., Du, Q., Chang, C.C.: Spectral/Spatial Hyperspectral Image Compression. In: Hyperspectral Data Compression, pp. 309–346. Springer, Heidelberg (2006)
Serra, P., Pons, X., Saurí, D.: Post-classification change detection with data from different sensors. Some accuracy considerations. International Journal of Remote Sensing 24(16), 3311–3340 (2003)
Pons, X., Moré, G., Serra, P.: Improvements on Classification by Tolerating NoData Values. Application to a Hybrid Classifier to Discriminate Mediterranean Vegetation with a Detailed Legend Using Multitemporal Series of Images. In: IEEE IGARSS and 27th CSRS, Denver, pp. 192–195 (2006)
Duda, R.D., Hart, P.E.: Pattern Classification and Scene Analysis. John Wiley & Sons, New York (1973)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Blanes, I., Zabala, A., Moré, G., Pons, X., Serra-Sagristà, J. (2008). Classification of Hyperspectral Images Compressed through 3D-JPEG2000. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2008. Lecture Notes in Computer Science(), vol 5179. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85567-5_52
Download citation
DOI: https://doi.org/10.1007/978-3-540-85567-5_52
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85566-8
Online ISBN: 978-3-540-85567-5
eBook Packages: Computer ScienceComputer Science (R0)