Hyperspectral Image Compression Using Three-Dimensional Wavelet Coding

Hyperspectral image is a sequence of images generated by hundreds of detectors. Each detector is sensitive only to a narrow range of wavelengths. One can view such an image sequence as a three-dimensional array of intensity values (pixels) within a rectangular prism. This image prism reveals contiguous spectrum information about the composition of the area being viewed by the instrument. The price paid for these high resolution, contiguous spectrum images is an extremely large set of data. These data cause processing, storage and transmission problems. Therefore, some application-specific data compression techniques should be applied before we process, store or transmit hyperspectral images. To solve this problem, 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 inter-band correlations) and there is no motion between bands. Therefore, three-dimensional discrete wavelet transform can fully exploit the inter-band correlations. A modified SPECK [5] partitioning algorithm is used to sort important information (significant pixels). Rate distortion (Peak Signal-to-Noise Ratio 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 savings at least a factor of 2 over 2D coding of separate spectral bands without axial transformation. Index Terms Three Dimensional Image Compression, Discrete Wavelet Transform (DWT), Hyperspectral Imaging, AVIRIS Imaging, SPIHT