An improved classified DCT-based compression algorithm for hyperspectral image is proposed. As variation of pixel
values in one band of the hyperspectral image is large, the traditional DCT is not very efficient for spectral decorrelation
(compared with the optimal KLT). The proposed algorithm is designed to deal with this problem. Our algorithm begins
with a 2D wavelet transform in spatial domain. After that, the obtained spectral vectors are clustered into different subsets
based on their statistics characteristics, and a 1D-DCT is performed on every subset. The classified algorithm consists of
three steps to make the statistics features fully used. In step1, a mean based clustering is performed to obtain basic subsets.
Step2 refines clustering by the range of spectral vector curve. Spectral vector curves, whose maximum and minimum
values are located in different intervals, are separated in step3. Since vectors in one subset are close to each other both in
values and statistic characteristics, which means a high relationship within one subset, the performance of DCT can be
very close to KLT, but the computation complexity is much lower. After the DWT and DCT in spatial and spectral domain,
an appropriate 3D-SPIHT image coding scheme is applied to the transformed coefficients to obtain a bit-stream with
scalable property. Results show that the proposed algorithm retains all the desirable features of compared state-of-art
algorithms despite its high efficiency, and can also have high performance over the non-classified ones at the same bitrates.