The Lossless Compression Method for Hyperspectral Images Based on Optimized Prediction Tree
Xia Hao Zhang Rong (Information Processing Center, University of Science & Technology of China, Hefei 230027, China)
Prediction tree is a traditional and efficient method for lossless compression of hyperspectral image. In this paper an optimized method based on prediction tree is presented. To express the variation of local context of two neighboring bands, a partial extending factor is introduced to compensate the predicted value of current pixel so as to reduce the prediction error. Furthermore, a synthetical prediction based lossless compression scheme for AVIRIS hyperspectral images is proposed. Experimental results demonstrate that the proposed method works efficiently on AVIRIS images with low complexity and limited memory.