RT Journal Article
A1 Jinwei Song
AD Centre for Space Science and Applied Research, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
A1 Zhongwei Zhang
AD Centre for Space Science and Applied Research, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
A1 Xiaomin Chen
AD Centre for Space Science and Applied Research, Chinese Academy of Sciences, Beijing 100190, People's Republic of China

PB iet
T1 Lossless compression of hyperspectral imagery via RLS filter
JN Electronics Letters
VO 49
IS 16
SP 992
OP 994
AB 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.
K1 RLS filter
K1 local difference
K1 input vector
K1 adaptive arithmetic encoder
K1 recursive least square filter
K1 neighbour pixels
K1 correlation elimination
K1 hyperspectral imagery lossless compression
DO https://doi.org/10.1049/el.2013.1315
UL https://digital-library.theiet.org/;jsessionid=1mtt0r7cgw1b.x-iet-live-01content/journals/10.1049/el.2013.1315
LA English
SN 0013-5194
YR 2013
OL EN