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Hyperspectral Image Compression Using Segmented Principal Component Analysis

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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.

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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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