Hyperspectral image compression using three-dimensional wavelet coding

A Hyperspectral image is a sequence of images generated by collecting contiguously spaced spectral bands of data. One can view such an image sequence as a three-dimensional array of intensity values (pixels) within a rectangular prism. We present a Three-Dimensional Set Partitioned Embedded bloCK (3DSPECK) algorithm based on the observation that hyperspectral images are contiguous in the spectrum axis (this implies large interband correlations) and there is no motion between bands. Therefore, the three-dimensional discrete wavelet transform can fully exploit the inter-band correlations. A SPECK partitioning algorithm extended to three-dimensions is used to sort significant pixels. Rate distortion (Peak Signal-to-Noise Ratio (PSNR) vs. bit rate) performances were plotted by comparing 3DSPECK against 3DSPIHT on several sets of hyperspectral images. Results show that 3DSPECK is comparable to 3DSPIHT in hyperspectral image compression. 3DSPECK can achieve compression ratios in the approximate range of 16 to 27 while providing very high quality reconstructed images. It guarantees over 3 dB PSNR improvement at all rates or rate saving at least a factor of 2 over 2D coding of separate spectral bands without axial transformation.