Research On Hyperspectralimage Compression Algorithm Based On Wavelet Transform

Hyperspectral image, an image sequence generated by spectrometer in manynarrow ranges of wavelength, possesses additional spectrum informationcorresponding to traditional two dimensional remote sensing images. The character ofthe high spatial resolution and high spectral resolution makes the hyperspectral imageoccupy huge set of data, which results in heavy burden for data transmission andstorage. Therefore, some application-specific data compression techniques should beapplied, including lossy, near-lossless and lossless compression. Hyperspectral imaging is a promising technique and mainly used for surfacedetection and identification, target classification and status monitoring. Thecompression scheme should not lose useful information in original data. In this paper,we study related lossless and near-lossless compression for hyperspectral image.Moreover, the compression scheme provides embedded bitstream from lossy tolossless, which facilitates the image retrieving and browsing. The proposed algorithms here are based on the wavelet transform. For losslesscompression, the lifting scheme constructs the reversible integer wavelet transform.The choice of the wavelet type based on the analysis of the character of hyperspectralimage, then, SPIHT or SPECK algorithm will be applied for bitplane coding that canrealize the lossy to lossless progressive transmission. We also exploit the correlationbetween adjacent bands, the result implies that group of bands can form rectangularprism, thus the three dimensional transform can act on it, causing decreasedcorrelation both in spatial dimension and spectral dimension, finally 3-D SPIHTalgorithm sorts the wavelet coefficient along the path of 3-D trees. The result oflossless algorithm has slight improvement than that of 2-D algorithm. The mostsurprising way is not the 3-D algorithm, but the 2-D algorithm used for decorrelatedgroups of bands, i.e. applying wavelet transform first on spectral direction, then 2-Dalgorithm is used to explore the coefficients' relation. The experiment gives thestirring result for lossless compression. Video coding methods based on wavelet transform are introduced in this paper forhyperspectral image compressing, the result is acceptable for near losslesscompression but still have some other disadvantages such as highly computingcomplexity. The study here is elementary and still has a long way to go.