Hyper-spectral image compression scheme based on spectral character

Regarding the characteristics and the imaging mechanism of LASIS (Large aperture static imaging spectrometry), a new compression scheme of interferential hyper-spectral images based on spectral character is proposed. There are two key techniques in the scheme: the first is a new matching algorithm based on optical flow which is applied to implement high precision matching of interferential hype-spectral image; the second is a novel compression algorithm based on 1D-DCT which is proposed to compress 'POINT' interferential image directly. According to the features of original interferential image cube produced by LASIS, the image cube is regrouped as 'POINT' interferential image sequences firstly. Due to the relationship of Fourier transform between the spectral information and 'POINT' interferential image, one dimensional DCT is used to remove the redundancy for each row of 'POINT' interferential image. A high efficient entropy coding method combined Run-length with context-based arithmetic coder is realized to compress the quantized DCT coefficients. The experimental results show that the scheme proposed in this paper can compress hyper-spectral images efficiently and it is much better than the method which compresses interferential image direcdy at the aspect of preserving the spectral information.