Ultraspectral sounder data compression review

Abstract

Ultraspectral sounders provide an enormous amount of measurements to advance our knowledge of weather and climate applications. The use of robust data compression techniques will be beneficial for ultraspectral data transfer and archiving. This paper reviews the progress in lossless compression of ultraspectral sounder data. Various transform-based, prediction-based, and clustering-based compression methods are covered. Also studied is a preprocessing scheme for data reordering to improve compression gains. All the coding experiments are performed on the ultraspectral compression benchmark dataset collected from the NASA Atmospheric Infrared Sounder (AIRS) observations.

References

1. Abousleman G P (1999). Adaptive coding of hyperspectral imagery. In: Proceedings of the 1999 IEEE International Conference on Acoustics,Speech, and Signal Processing (ICASSP), 4: 2243–2246. doi:10.1109/ICASSP.1999.758383
2. Abousleman G P, Marcellin M W, Hunt B R (1997). Hyperspectral image compression usingentropy-constrained predictive trellis coded quantization. IEEE Transactions on Image Processing, 6(4): 566–573. doi:10.1109/83.563321
3. Aumann H H, Strow L (2001). AIRS,the first hyper-spectral infrared sounder for operational weatherforecasting. In: Proceedings of IEEE AerospaceConference, 4: 1683–1692
4. Bilgin A, Zweig G, Marcellin M W (2000). Three-dimensional image compressionwith integer wavelet transforms. AppliedOptics, 39(11): 1799–1814. doi:10.1364/AO.39.001799
5. Bloom H J (2001). The Cross-track Infrared Sounder (CrIS): a sensor foroperational meteorological remote sensing. In: Proceedings of the 2001 International Geoscience and Remote SensingSymposium (IGARSS), 3: 1341–1343
6. Canta G R, Poggi G (1998). Kronecker-productgain-shape vector quantization for multispectral and hyperspectralimage coding. IEEE Transactions on ImageProcessing, 7(5): 668–678. doi:10.1109/83.668024
7. Chang C I, Du Q (1999). Interferenceand noise-adjusted principal components analysis. IEEE Transactions on Geoscience and Remote Sensing, 37(5): 2387–2396. doi:10.1109/36.789637
8. Cuperman V (1993). Joint bit allocation and dimensions optimization forvector transform quantization. IEEE Transactionson Information Theory, 39(1): 302–305. doi:10.1109/18.179379
9. Cuperman V, Gersho A (1982). Adaptivedifferential vector coding of speech. In:Proceedings of IEEE GLOBECOM, 10: 1092–1096
10. Daubechies I (1992). Ten Lectures on Wavelets. CBMS-NSF Regional Conference Series in Applied Mathematics, 61, SIAM,Philadelphia, PA
11. Daubechies I, Sweldens W (1998). Factoringwavelet and subband transforms into lifting steps. Journal of Fourier Analysis and Applications, 4(3): 247–269. doi:10.1007/BF02476026
12. Dragotti P L, Poggi G, Ragozini A R P (2000). Compression of multispectral imagesby three-dimensional SPIHT algorithm. IEEETransactions on Geoscience and Remote Sensing, 38(1): 416–428. doi:10.1109/36.823937
13. Eckstein B A, Peters R, Irvine J M, et al. (2000). Assessing the performanceeffects of data compression for SAR imagery. In: Proceedings of the 2000 IEEE Applied Imagery and Pattern RecognitionWorkshop, 102–108
14. Gelli G, Poggi G (1999). Compressionof multispectral images by spectral classification and transform coding. IEEE Transactions on Image Processing, 8(4): 476–489. doi:10.1109/83.753736
15. Gersho A, Gray R M (1992). VectorQuantization and Signal Compression. Norwell, Mass: Kluwer Academic
16. Golomb S W (1966). Run-length encoding. IEEETransactions on Information Theory, IT-12: 399–401. doi:10.1109/TIT.1966.1053907
17. Golub G H, Van Loan C F (1996). MatrixComputations. John Hopkins University Press
18. Gray R M (1984). Vector Quantization. In:IEEE Acoustics, Speech, and Signal Processing Magazine, 1: 4–29
19. Hoffman R N, Johnson D W (1994). Applicationof EOF's to multispectral imagery: data compression and noise detectionfor AVIRIS. IEEE Transactions on Geoscienceand Remote Sensing, 32(1): 25–34. doi:10.1109/36.285186
20. Huang B, Ahuja A, Huang H L, et al. (2004a). Predictive partitionedvector quantization for hyperspectral sounder data compression. In: SPIE Annual Meeting, Denver, Proceedings of SPIE, 5548: 70–77. doi:10.1117/12.560402
21. Huang B, Ahuja A, Huang H L, et al. (2004c). Lossless compression of3D hyperspectral sounding data using context-based adaptive losslessimage codec with Bias-Adjusted Reordering. Optical Engineering, 43(9): 2071–2079. doi:10.1117/1.1778732
22. Huang B, Ahuja A, Huang H L, et al. (2004d). Effects of the startingchannel for spectral reordering on the lossless compression of 3Dultraspectral sounder data. In: SPIE InternationalAsia-Pacific Symposium, 2003, Honolulu, Hawaii, Proceedingsof SPIE, 5655: 353–363. doi:10.1117/12.579062
23. Huang B, Ahuja A, Huang H L, et al. (2005). Fast precomputed VQ withoptimal bit allocation for lossless compression of ultraspectral sounderdata. To appear In: Proceedings of the2005 IEEE Data Compression Conference
24. Huang B, Huang H L, Ahuja A, et al. (2004b). Lossless data compressionfor infrared hyperspectral sounders – an update. In: SPIE Annual Meeting, Denver, Proceedings of SPIE, 5548: 109–119. doi:10.1117/12.560404
25. Huang B, Huang H L, Chen H, et al. (2003). Data compression studiesfor NOAA hyperspectral environmental suite using 3D integer wavelettransforms with 3D set partitioning in hierarchical trees. In: SPIEInternational Symposium on Remote Sensing Europe, Barcelona, Spain, Proceedingsof SPIE, 5238: 255–265. doi:10.1117/12.511437
26. Huang B, Smith W L, Huang H L, et al. (2002). Comparison of linear formsof the radiative transfer equation with analytic Jacobians, Applied Optics, 41(21): 4209–4219. doi:10.1364/AO.41.004209
27. ISO/IEC 14495-1 and ITU Recommendation T.87, 1999 . Information Technology –lossless and near-lossless compression of continuous-tone still images
28. ISO/IEC 15444-1, 2000 . Information technology - JPEG2000 image coding system-part 1: Core coding system
29. Kovačević J, Sweldens W (2000). Waveletfamilies of increasing order in arbitrary dimensions. IEEE Transactions on Image Processing, 9(3): 480–496. doi:10.1109/83.826784
30. Langdon G G (1984). An introduction to arithmetic coding. IBM Journal of Research and Development, 28: 135–139
31. Lee H S, Younan N H, King R L (2000). Hyperspectral image cube compressioncombining JPEG 2000 and spectral decorrelation. In: Proceedings of the IEEE International Geoscience and Remote SensingSymposium (IGARSS), 6: 3317–3319. doi:10.1109/IGARSS.2002.1027168
32. Li X, Orchard M (2001). Edge-directedprediction for lossless compression of natural images. IEEE Transactions on Image Processing, 10(6): 813–817. doi:10.1109/83.923277
33. Lian C J, Chen K F, Chen H H, et al. (2003). Analysis and architecturedesign of block-coding engine for EBCOT in JPEG 2000. IEEE Transactions on Circuits and Systems for Video Technology, 13(3): 219–230. doi:10.1109/TCSVT.2003.809833
34. Linde Y, Buzo A, Gray R M (1980). An Algorithm for Vector QuantizerDesign. IEEE Transactions on Communications, COM-28: 84–95. doi:10.1109/TCOM.1980.1094577
35. Mielikainen J, Toivanen P, Kaarna A (2003). Linear prediction in lossless compressionof hyperspectral images. Optical Engineering, 42(4): 1013–1017. doi:10.1117/1.1557174
36. Motta G, Rizzo F, Storer J A (2003). Compression of hyperspectral imagery. In: Proceedings of the 2003 IEEE Data CompressionConference, 333–342
37. Motta G, Storer J A, Carpentieri B (1999). Adaptive linear prediction losslessimage coding. In: Proceedings of the 1999IEEE Data Compression Conference, 491–500
38. Phulpin T, Cayla F, Chalon G, et al. (2002). IASI onboard Metop: Projectstatus and scientific preparation. In:12^th International TOVS Study Conference, Lorne, Victoria, Australia, 234–243
39. Qian S E, Hollinger A B, Dutkiewicz M, et al. (2001). Effect of lossy vector quantizationhyperspectral data compression on retrieval of red-edge indices. IEEE Transactions on Geoscience and Remote Sensing, 39(7): 1459–1470. doi:10.1109/36.934077
40. Qian S E, Hollinger A B, Williams D, et al. (1996). Fast 3-D data compressionof hyperspectral imagery using vector quantization with spectral-feature-basedbinary coding. Optical Engineering, 35(11): 3242–3249. doi:10.1117/1.601062
41. Qian S E, Hu B, Bergeron M, et al. (2002). Quantitative evaluationof hyperspectral data compressed by near lossless onboard compressiontechniques. In: Proceedings of the 2002International Geoscience and Remote Sensing Symposium (IGARSS), 1425–1427
42. Riskin E A (1991). Optimal bit allocation via the generalized BFOS algorithm. IEEE Transactions on Information Theory, 37(2): 400–402. doi:10.1109/18.75264
43. Ryan M J, Arnold J F (1998). A suitabledistortion measure for the lossy compression of hyperspectral data. In: Proceedings of the IEEE International Geoscienceand Remote Sensing Symposium (IGARSS), 4: 2056–2058
44. Saghri J A, Tescher A G, Reagan J T (1995). Practical Transform Coding of MultispectralImagery. In: IEEE Signal Processing Magazine, 12(1): 32–43. doi:10.1109/79.363506
45. Said A, Pearlman W A (1996). A new,fast, and efficient image codec based on set partitioning in hierarchicaltrees. IEEE Transactions on Circuits andSystems for Video Technology, 6(3): 243–250. doi:10.1109/76.499834
46. Shapiro J M (1993). Embedded image coding using zerotrees of wavelet coefficients. IEEE Transactions on Signal Processing, 41(12): 3445–3462. doi:10.1109/78.258085
47. Shapiro J M (1995). Apparatus and method for compressing information. United States Patent Number 5412741, Issued May2, 1995
48. Shaw G A, Burke H-h K (2003). SpectralImaging for Remote Sensing. Lincoln LaboratoryJournal, 14(1): 3–28
49. Shen S S, Lindgren J E, Payton P M (1993). Effects of multispectral compressionon machine exploitation. Twenty-SeventhAsilomar Conference on Signals, Systems, and Computers, 2: 1352–1356
50. Smith W L, Harrison F W, Hinton D E, et al. (2002). GIFTS - the precursor geostationarysatellite component of the future Earth Observing System. In: Proceedings of the 2002 International Geoscienceand Remote Sensing Symposium (IGARSS), 1: 357–361
51. Sweldens W (1996). The lifting scheme: A custom-design construction ofbiorthogonal wavelets. Journal of Appliedand Computational Harmonic Analysis, 3(2): 186–200. doi:10.1006/acha.1996.0015
52. Tang X, Cho S, Pearlman W A (2003). Comparison of 3D set partitioningmethods in hyperspectral image compression featuring an improved 3D-SPIHT. In: Proceedings of the 2003 Data Compression Conference, 449
53. Taubman D (2000). High performance scalable image compression with EBCOT. IEEE Transactions on Image Processing, 9(7): 1158–1170. doi:10.1109/83.847830
54. Taubman D, Marcellin M (2002). JPEG2000:Image Compression Fundamentals, Standards and Practice. Kluwer Academic, Norwell
55. Weinberger M J, Seroussi G, Sapiro G (2000). The LOCO-I lossless image compressionalgorithm: principles and standardization into JPEG-LS. IEEE Transactions on Image Processing, 9(8): 1309–1324. doi:10.1109/83.855427
56. Witten I H, Neal R M, Cleary J (1987). Arithmetic coding for data compression. Communications of the ACM, 30(6): 520–540. doi:10.1145/214762.214771
57. Wu X (1997). Context-based, adaptive, lossless image coding. IEEE Transactions on Communications, 45(4): 437–444. doi:10.1109/26.585919
58. Wu X, Barthel K (1998). Piecewise2D autoregression for predictive image coding. In: Proceedings of the International Conference on Image Processing(ICIP), 3: 901–904
59. Yang K M, Wu L, Mills M (1988). Fractal based image coding schemeusing peano scan. In: Proceedings of the1988 International Symposium on Circuits and Systems, 3: 2301–2304. doi:10.1109/ISCAS.1988.15404