Distributed lossless compression of hyperspectral images based on multi-band prediction
粘永健 Nian Yongjian, 辛勤 Xin Qin, 汤毅 Tang Yi, 万建伟 Wan Jianwei · Optics and Precision Engineering · 2012
A lossless compression algorithm based on distributed source coding was proposed to compress the airborne hyperspectral data effectively.In order to make full use of the spectral correlation of hyperspectral images,a multi-band prediction scheme was introduced to acquire the prediction values of the current block and to reduce the maximal absolute value of prediction error effectively.Then,by using the maximal absolute value to determine the coset index of pixels belonging to the current block,the lossless compression of hyperspectral images was realized by transmitting the coset index of the current block instead of its prediction error.Experimental results on hyperspectral images acquired by Airborne Visible Infrared Imaging Spectrometer(AVIRIS) show that the proposed algorithm can offer both high compression performance and low encoder complexity compared with those existing classical algorithms,which is available for on-board compression of hyperspectral images.