Lossy hyperspectral image compression using improved classified DCT and 3DSPIHT

Keyan Wang; Zifan Hu; Ran Han; Jing Zhang; Yunsong Li

doi:10.1117/12.2053472

5 June 2014 Lossy hyperspectral image compression using improved classified DCT and 3DSPIHT

Keyan Wang, Zifan Hu, Ran Han, Jing Zhang, Yunsong Li

Proceedings Volume 9124, Satellite Data Compression, Communications, and Processing X; 912409 (2014) https://doi.org/10.1117/12.2053472
Event: SPIE Sensing Technology + Applications, 2014, Baltimore, MD, United States

Abstract

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.

Citation Download Citation

Keyan Wang, Zifan Hu, Ran Han, Jing Zhang, and Yunsong Li "Lossy hyperspectral image compression using improved classified DCT and 3DSPIHT", Proc. SPIE 9124, Satellite Data Compression, Communications, and Processing X, 912409 (5 June 2014); https://doi.org/10.1117/12.2053472

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KEYWORDS

Image compression

Hyperspectral imaging

Signal to noise ratio

Wavelet transforms

Discrete wavelet transforms

Thulium

3D image processing

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