Compression of multispectral images using spectral correlation and SPIHT algorithm

摘要

Many methods for lossy and lossless compression of multispectral imaging data has been developed. 3-dimensional compression of multispectral images has been studied by many researchers. Although, the 3-D compression method provides relatively good performances, a major problem is that the method requires a large amount of memory and processing time. A salient property of hyperspectral images is that strong spectral correlation exists throughout almost all bands. This could be because, in these bands, the signal associated with these frequencies is greatly attenuated by the atmosphere or the materials being imaged. In this paper, we take into account these property of multispectal data and propose a new compression algorithm based on a 2-dimensional wavelet transform. In the proposed method, we divide the spectral bands of multispectral images into a number of groups in which each group contains two adjacent bands. The first band of each group is SPIHT coded. Its decoded version is subtracted from the second band, and then SPIHT is applied to the residual image. The data used in this paper was acquired by AVIRIS. There were 224 contiguous spectral bands using wavelengths between 400 and 2500nm. The data set contains 512 scan lines with 614 pixels in each scanline. We selected a sub-region with the size of 512×512 pixels. As can be seen in the results, the proposed algorithm provides better performance than the SPIHT algorithm.