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access icon free Lossless compression of hyperspectral imagery via RLS filter

© The Institution of Engineering and Technology
Received 17/04/2013, Published

A new algorithm for lossless compression of hyperspectral imagery is proposed. First, the average value of four neighbour pixels of the current pixel is calculated as local mean, which is subtracted by the current pixel to eliminate correlation in the current band image. The residual produced by this step is called local difference. The local differences of the pixels which co-locate with the current pixel in previous bands form the input vector of the recursive least square (RLS) filter, by which the prediction value of the current local difference is produced. Then, the prediction residual is sent to the adaptive arithmetic encoder. Experiment results show that the proposed algorithm produces state-of-the-art performance with relatively low complexity, and it is suitable for real-time compression on satellites.

Inspec keywords: data compression; vectors; adaptive codes; arithmetic codes; image resolution; remote sensing; adaptive filters; geophysical image processing; image coding; correlation methods

Other keywords: hyperspectral imagery lossless compression; recursive least square filter; input vector; local difference; RLS filter; neighbour pixels; adaptive arithmetic encoder; correlation elimination

Subjects: Geophysical techniques and equipment; Image and video coding; Geophysics computing; Computer vision and image processing techniques; Algebra; Geography and cartography computing; Filtering methods in signal processing; Algebra

References

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http://iet.metastore.ingenta.com/content/journals/10.1049/el.2013.1315
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