Abstract
Principal component analysis (PCA) is the most efficient spectral decorrelation approach for hyperspectral image compression. In conjunction with JPEG2000-based spatial coding, the resulting PCA+JPEG2000 can yield superior rate-distortion performance. However, the involved overhead bits consumed by the large operation matrix for principal component transform may affect compression performance at low bitrates, particularly when the spatial size of an image patch to be compressed is relatively small compared to the spectral dimension. In our previous research, we proposed to apply the segmented principal component analysis (SPCA) to mitigate this effect, and the resulting compression algorithm, denoted as SPCA+JPEG2000, can improve the rate-distortion performance even when PCA+JPEG2000 is applicable. In this chapter, we investigate the quality of reconstructed data after SPCA+JPEG2000 compression based on the performance in spectral fidelity, classification, linear unmixing, and anomaly detection. The experimental results show that SPCA+JPEG2000 can outperform in terms of preserving more useful data information, in addition to offer excellent rate-distortion performance. Since the spectral partition in SPCA relies on the calculation of a data-dependent spectral correlation coefficient matrix, we investigate a sensor-dependent suboptimal partition approach, which can accelerate the compression process with no much distortion.
Keywords
- Principal Component Analysis
- Discrete Wavelet Transform
- Anomaly Detection
- Hyperspectral Image
- Compression Performance
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Information Technology—JPEG 2000 Image Coding System—Part 1: Core Coding System, ISO/IEC 15444–1, 2000.
Information Technology—JPEG 2000 Image Coding System—Part 2: Extensions, ISO/IEC 15444–2, 2004.
J. E. Fowler and J. T. Rucker, “3D wavelet-based compression of hyperspectral imagery,” in Hyperspectral Data Exploitation: Theory and Applications, C.-I. Chang, Ed., John Wiley & Sons, Inc., Hoboken, NJ, 2007.
B. Penna, T. Tillo, E. Magli, and G. Olmo, “Progressive 3-D coding of hyperspectral images based on JPEG 2000,” IEEE Geosciences and Remote Sensing Letters, vol. 3, no. 1, pp. 125–129, 2006.
B. Penna, T. Tillo, E. Magli, and G. Olmo, “Transform coding techniques for lossy hyperspectral data compression,” IEEE Transactions on Geosciences and Remote Sensing, vol. 45, no. 5, pp. 1408–1421, 2007.
Q. Du and J. E. Fowler, “Hyperspectral image compression using JPEG2000 and principal components analysis,” IEEE Geoscience and Remote Sensing Letters, vol. 4, no. 2, pp. 201–205, 2007.
W. Zhu, On the Performance of JPEG2000 and Principal Component Analysis in Hyperspectral Image Compression, Master’s Thesis, Mississippi State University, 2007.
B.-J. Kim, Z. Xiong, and W. A. Pearlman, “Low bit-rate scalable video coding with 3D set partitioning in hierarchical trees (3D SPIHT),” IEEE Transactions on Circuits and Systems for Video Technology, vol. 10, pp. 1374–1387, 2000.
X. Tang and W. A. Pearlman, “Scalable hyperspectral image coding,” Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 2, pp. 401–404, 2005.
B. Penna, T. Tillo, E. Magli, and G. Olmo, “Hyperspectral image compression employing a model of anomalous pixels,” IEEE Geoscience and Remote Sensing Letters, vol. 4, no. 4, pp. 664–668, 2007.
Q. Du, W. Zhu, and J. E. Fowler, “Anomaly-based JPEG2000 compression of hyperspectal imagery,” IEEE Geoscience and Remote Sensing Letters, vol. 5, no.4, pp. 696–700, 2008.
W. Zhu, Q. Du, and J. E. Fowler, “Multi-temporal hyperspectral image compression,” IEEE Geoscience and Remote Sensing Letters, vol. 8, no. 3, pp. 416–420, 2011.
F. Garcia-Vilchez and J. Serra-Sagrista, “Extending the CCSDS recommendation for image data compression for remote sensing scenarios,” IEEE Transactions on Geoscience and Remote Sensing, vol. 47, no. 10, pp. 3431–3445, 2009.
H. Yang, Q. Du, W. Zhu, J. E. Fowler, and I. Banicescu, “Parallel data compression for hyperspectral imagery,” Proceedings of IEEE International Geoscience and Remote Sensing Symposium, vol. 2, pp. 986–989, 2008.
Q. Du, W. Zhu, H. Yang, and J. E. Fowler, “Segmented principal component analysis for parallel compression of hyperspectral imagery,” IEEE Geoscience and Remote Sensing Letters, vol. 6, no. 4, pp. 713–717, 2009.
X. Jia and J. A. Richards, “Segmented principal components transformation for efficient hyperspectral remote-sensing image display and classification,” IEEE Transactions on Geosciences and Remote Sensing, vol. 37, no. 1, pp. 538–542, 1999.
V. Tsagaris, V. Anastassopoulos, and G. A. Lampropoulos, “Fusion of hyperspectral data using segmented PCT for color representation and classification,” IEEE Transactions on Geoscience and Remote Sensing vol. 43, no. 10, pp. 2365–2375, 2005.
Q. Du and J. E. Fowler, “Low-complexity principal component analysis for hyperspectral image compression,” International Journal of High Performance Computing Applications, vol. 22, no. 4, pp. 438–448, 2008.
Q. Du, N. H. Younan, R. L. King, and V. P. Shah, “On the performance evaluation of pan-sharpening techniques,” IEEE Geoscience and Remote Sensing Letters, vol. 4, no. 4, pp. 518–522, 2007.
G. Martin, V. Gonzalez-Ruiz, A. Plaza, J. P. Ortiz, and I. Garcia, “Impact of JPEG2000 compression on endmember extraction and unmixing of remotely sensed hyperspectral data,” Journal of Applied Remote Sensing, vol. 4, Article ID 041796, 2010.
F. Garcia-Vilchez, J. Munoz-Mari, M. Zortea, I. Blanes, V. Gonzalez-Ruiz, G. Camps-Valls, A. Plaza, and J. Serra-Sagrista, “On the impact of lossy compression on hyperspectral image classification and unmixing,” IEEE Geoscience and Remote Sensing Letters, vol. 8, no. 2, pp. 253–257, 2011.
F. A. Kruse, A. B. Lefkoff, J. W. Boardman, K. B. Heidebrecht, A. T. Shapiro, J. P. Barloon, and A. F. H. Goetz, “The spectral image processing system (SIPS) – Interactive visualization and analysis of imaging spectrometer data,” Remote Sensing of Environment, vol. 44, no. 2–3, pp. 145–163, 1993.
A. Hyvärinen, “Fast and robust fixed-point algorithms for independent component analysis,” IEEE Transactions on Neural Network, vol. 10, no. 3, pp. 626–634, 1999.
Q. Du, N. Raksuntorn, S. Cai, and R. J. Moorhead, “Color display for hyperspectral imagery,” IEEE Transactions on Geoscience and Remote Sensing, vol. 46, no. 6, pp. 1858–1866, Jun. 2008.
I. S. Reed and X. Yu, “Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution,” IEEE Transactions on Acoustic, Speech and Signal Processing, vol. 38, no. 10, pp. 1760–1770, 1990.
Q. Du, “Optimal linear unmixing for hyperspectral image analysis,” Proceedings of IEEE International Geoscience and Remote Sensing Symposium, vol. 5, pp. 3219–3221, Anchorage, AK, Sep. 2004.
D. Heinz and C.-I Chang, “Fully constrained least squares linear mixture analysis for material quantification in hyperspectral imagery,” IEEE Transactions on Geoscience Remote Sensing, vol. 39, no. 3, pp. 529–545, 2001.
Q. Du and C.-I Chang, “Linear mixture analysis-based compression for hyperspectal image analysis,” IEEE Transactions on Geoscience and Remote Sensing, vol. 42, no. 4, pp. 875–891, 2004.
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Zhu, W., Du, Q., Fowler, J.E. (2012). Hyperspectral Image Compression Using Segmented Principal Component Analysis. In: Huang, B. (eds) Satellite Data Compression. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-1183-3_11
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DOI: https://doi.org/10.1007/978-1-4614-1183-3_11
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