Air-borne and space-borne acquired hyperspectral images are used to recognize objects and to classify materials on the surface of the earth. The state of the art compressor for lossless compression of hyperspectral images is the Spectral oriented Least SQuares (SLSQ) compressor (see [1–7]). In this paper we discuss hyperspectral image compression: we show how to visualize each band of a hyperspectral image and how this visualization suggests that an appropriate band ordering can lead to improvements in the compression process. In particular, we consider two important distance measures for band ordering: Pearson’s Correlation and Bhattacharyya distance, and report on experimental results achieved by a Java-based implementation of SLSQ.
References
[1]
Rizzo, F.; Carpentieri, B.; Motta, G.; Storer, J.A. Low-complexity lossless compression of hyperspectral imagery via linear prediction. IEEE Signal Process. Lett. 2005, 12, 138–141, doi:10.1109/LSP.2004.840907.
[2]
Rizzo, F.; Carpentieri, B.; Motta, G.; Storer, J.A. Compression of Hyperspectral Imagery via Linear Prediction. In e-Business and Telecommunication Networks; Springer: Dordrecht, The Netherlands, 2007; pp. 284–291.
[3]
Rizzo, F.; Carpentieri, B.; Motta, G.; Storer, J.A. High Performance Compression of Hyperspectral Imagery with Reduced Search Complexity in the Compressed Domain. In Proceedings of IEEE Data Compression Conference (DCC ’04), Snowbird, UT, USA, 23-24 March 2004; pp. 479–488.
[4]
Carpentieri, B.; Storer, J.A.; Motta, G.; Rizzo, F. Compression of Hyperspectral Imagery. In Proceedings of IEEE Data Compression Conference (DCC ’03), Snowbird, UT, USA, 25-27 March 2003; pp. 317–324.
[5]
Motta, G.; Rizzo, F.; Storer, J.A.; Carpentieri, B. Real-time software compression and classification of hyperspectral images. In Proceedings of the International Society for Optics and Photonics; 2004; 5573, pp. 182–192.
[6]
Rizzo, F.; Motta, G.; Carpentieri, B.; Storer, J.A. Lossless compression of hyperspectral imagery: A real-time approach. In Proceedings of the International Society for Optics and Photonics; 2004; 5573, pp. 262–272.
[7]
Pizzolante, R. Lossless Compression of Hyperspectral Imagery. In Proceedings of 2011 First International Conference on Data Compression, Communications and Processing (CCP), Palinuro, Italy, 21-24 June 2011; pp. 157–162.
[8]
National Aeronautics and Space Administration (NASA). AVIRIS Home Page, 2011. Available online: http://aviris.jpl.nasa.gov/ (accessed on November 2011).
[9]
Moffett Federal Airfield. Moffett Federal Airfield from Wikipedia. 2011. Available online: http://en.wikipedia.org/wiki/Moffett_Federal_Airfield (accessed on November 2011).
[10]
Jasper Ridge Biological Preserve. Jasper Ridge Biological Preserve from Wikipedia. 2011. Available online: http://en.wikipedia.org/wiki/Jasper_Ridge_Biological_Preserve (accessed on November 2011).
[11]
Yellowstone National Park. Yellowstone National Park from Wikipedia. 2011. Available online: http://en.wikipedia.org/wiki/Yellowstone_National_Park (accessed on November 2011).
[12]
Carpentieri, B. Hyperpectral Images: Compression,Visualization and Band Ordering. In Proceedings of IPCV; 2011; 2, pp. 1023–1029.
[13]
National Aeronautics and Space Administration (NASA). AVIRIS False-Color Previews. 2011. Available online: http://compression.jpl.nasa.gov/hyperspectral/falsecolor.html (accessed on November 2011).
[14]
Android. Android Web Page, 2011. Available online: http://www.android.com (accessed on November 2011).
[15]
Android Developers. Android Developers Web Page, 2011. Available online: http://developer.android.com/index.html (accessed on November 2011).
[16]
Android SDK. Android SDK Web Page, 2011. Available online: http://developer.android.com/sdk/index.html (accessed on November 2011).
[17]
Weinberg, M.J.; Seroussi, G.; Sapiro, G. The LOCO-I lossless image compression algorithm: Principles and standardization into JPEG-LS. IEEE Trans. Image Process. 2000, 9, 1309–1324, doi:10.1109/83.855427.
[18]
Sánchez, J.E.; Auge, E.; Santaló, J.; Blanes, I.; Serra-Sagristà, J.; Kiely, A.B. Review and Implementation of the Emerging CCSDS Recommended Standard for Multispectral and Hyperspectral Lossless Image Coding. In Proceedings of 2011 First International Conference on Data Compression, Communications and Processing (CCP), Palinuro, Italy, 21-24 June 2011; pp. 222–228.
[19]
Aulí Llinàs, F.; Bartrina-Rapesta, J.; Serra-Sagristà, J.; Marcellin, M.W. Low Complexity, High Efficiency Probability Model for Hyper-spectral Image Coding. In Proceedings of 2011 First International Conference on Data Compression, Communications and Processing (CCP), Palinuro, Italy, 21-24 June 2011; pp. 229–235.
[20]
Brunello, D.; Calvagno, G.; Mian, G.A.; Rinaldo, R. Lossless video coding using optimal 3D prediction. In Proceedings of International Conference on Image Proceeding; 2002; 1, pp. 88–92.
[21]
Motta, G.; Rizzo, F.; Storer, J.A. Hyperspectral Data Compression; Springer Science: Berlin, Germany, 2006.
[22]
Pearson, K. Mathematical contributions to the theory of evolution.-III. Regression, heredity and panmixia. Philos. Trans. R. Soc. Lond. 1896, 187, 253–318, doi:10.1098/rsta.1896.0007.
[23]
Bhattacharyya, A. On a measure of divergence between two statistical populations defined by their probability distributions. Bull.Calcutta Math. Soc. 1943, 35, 99–109.
